陆地不同生态系统土壤呼吸及土壤碳循环研究
|
摘要
利用田间试验数据、地面调查数据，从干旱区典型陆地生态系统入手，研究了干旱区典型陆地生态系统CO_2源汇关系；研究了土壤呼吸及其影响因素，土壤CO_2固定与释放特征；利用遥感技术手段和GIS技术，对区域尺度NPP进行了估算，实现了区域NPP的遥感动态监测，对NPP的时空分布规律进行了详细的描述和客观地评价，为陆地不同生态系统土壤碳循环规律及对策研究提供了依据。主要结论如下：
绿洲农田生态系统：各种类型的绿洲农田生态系统对的CO2固定量有一定日变化差异，在夜间的11个小时内，各农田生态系统都是碳源，即净释放CO2而白天小麦生态系统和棉花生态系统都有1个小时为碳源。研究表明玉米农田生态系统对CO2的净固定能力最强，24小时固定CO238.47g／m2。其次是小麦生态系统和棉花生态系统。从年固碳量来看，绿洲玉米生态系统为最高，达到141.66t CO2／hm2.a；其次为小麦生态系统，为122.60t CO2／hm2.a；棉花生态系统最低，为50.39t CO2／hm2.a。
荒漠林地生态系统：在夜间的11个小时内，各林地生态系统都是碳源，即净释放CO2。而在白天，云杉林地生态系统有7个小时为碳源，研究表明：云杉林地生态系统对CO2的净固定能力最弱，24小时内净释放CO2 4.22g／m2。最强的是梭梭林地生态系统，24小时净固定CO218.34 g／m2。红柳林地生态系统对CO2的固定能力稍弱于梭梭林地生态系统。从各观测样地的年固碳能力来看，梭梭林地生态系统固定量最大达到了9.29t CO2／hm2.a，红柳林地生态系统次之，为2.68t CO2／hm2.a。云杉林地生态系统总体来看是一个弱的碳源，年释放量达到8.20t CO2／hm2.a，这与传统的观点相左，尚需要进一步研究。
高山草地生态系统：围栏封育条件下，草地生态系统日CO2净固定量达到了12.76gCO2／m2·d，每天除18时和21时是弱的碳源外，其余时间均是碳汇。其中16时以前是碳的强汇，对CO2的净固定量达到12.02gCO2／m2，占到日总CO2净固定量的94.20％；自然放牧条件下，草地生态系统日CO2净固定量达到了11.52gCO2／m2·d，除9时、13时、14时和21时是弱的碳源外，其余时间均是碳汇。其中15～19时是碳的强汇，对CO2的净固定量达到9.46gCO2／m2，占到日CO2净固定量的82.00％。13、14时出现弱源的主要原因是由于植物的光合速率在中午有所下降即“午休”现象导致的。每年的5～9月份是牧草的生长期，对巴音布鲁克亚高山草地生态系统CO2的年固定量的初步估算结果表明：其CO2固定量达到7.14t CO2／hm2·a。
三工河流域土壤碳估算：新疆三工河流域总碳储量约为11.18Pg，其中有机碳约为5.43Pg，占48.54％，无机碳约为5.75Pg，占51.46％。各土壤生态系统相比较，森林土壤、草甸土壤具有较大的有机碳通量和有机碳容量，但其无机碳通量和无机碳容量均明显低于其它土壤生态系统；荒漠土壤生态系统的有机碳通量、碳容量最低，但其具有较高的无机碳储量。
亚高山草地生态系统碳估算：巴音布鲁克亚高山草地生态系统地上植物体碳总量约为7.20万t。其中地上部分约为3.20万t，约占44.44％；地下部分根系约为4.0万t，约占到55.56％。对巴音布鲁克亚高山草原生态系统的土壤有机碳进行了估算，结果表明：亚高山草原生态系统土壤有机碳的平均碳通量为16.80Ckg／m2，土壤有机碳总贮藏量约为3019.22万t。
土壤条件对凋落物分解速率的影响：壤质土上的有机物料分解速率高于粘质土和砂质土；中等土壤湿度条件下有机物料的分解速率最高；深埋方式有机物料的分解速率高于浅埋方式；中等土壤盐分条件下，有机物料的分解速率最高；不同类型凋落物，在其它条件完全相同的条件下，分解速率也不完全相同，主要是由于其木质素含量有所差异所致。本研究是在固定了其它因子的条件下，仅对单因子逐项进行了研究，因子间的交互作用尚需要进一步研究。
区域NPP的遥感估算：在AVHRR NOAA光谱数据的基础上，运用NOAA AVHRR的可见光波段、近红外波段和热红外波段来提取和反演地面参数，在地理信息系统的支持下，综合地学、生态学信息，精确估算陆地NPP，最终达到区域尺度范围内NPP动态监测的目的。
The experiment was conducted in the typical terrestrial ecosystem in arid region Sangong River drainage area in Xinjiang (and the selected grassland ecosystem is at Bayinbuluke Grassland) with Fukang Desert. Ecology Experimental Station of Xinjiang Ecological Geography Academic Institution of Chinese Academy of Sciences as the main backing, typical terrestrial ecosystem in arid region as the subject investigated. CO_2 source/sink relation of typical terrestrial ecosystem in arid region is studied systematically based on field-study data in the field, and carbon concerning Sangong River drainage area and Bayinbuluke subalpine meadow ecosystem is estimated. The main conclusions are as follows:
Oasis field ecosystem: There are certain differences among the fixation quantity of CO_2 of different types of field ecosystems. All field ecosystems are carbon source, i. e. net discharge of CO_2 during the 11 hours at night. However, there is one hour acting as carbon source for wheat-soil ecosystem and cotton-soil ecosystem in the daytime. Study shows that maize-soil ecosystem has biggest capability of CO_2 net fixation with fixation quantity of 38.47g/m~2 per hour. And wheat-soil ecosystem and cotton-soil ecosystem stand second on the list. From the point of view of annual carbon fixation quantity oasis maize-soil ecosystem is highest up to 141.66t CO_2/hm~2. a; the following one is wheat-soil ecosystem with 122.60 t CO_2 /hm~2. a; and cotton-soil ecosystem is lowest with 50.39 t CO_2/hm~2. a.
Desert forestland ecosystem: All forestland ecosystems are carbon source, i.e. net discharge of CO_2 during the 11 hours at night. However, there is 7 hours acting as carbon source for Picea schrenkiana forestland ecosystem. Study shows that Picea schrenkiana forestland ecosystem has the weakest capability of CO_2 net fixation with net discharge of 4.22g/m~2 within 24 hours. And Haloxylon ammodendron forestland ecosystem is of the strongest capability with CO_2 net fixation of 18.34g/m~2 within 24 hours. The CO~2 fixation capability of Tamarix ramosissima forestland ecosystem is slightly weaker than that of Haloxylon ammodendron forestland ecosystem. From the point of view of annual carbon fixation quantity of each observed plot, Haloxylon ammodendron forestland ecosystem is highest up to 9.29t CO_2/hm~2. a; the following one is Tamarix ramosissima forestland ecosystem with 2.68 t CO_2/hm~2. a; And Picea schrenkiana forestland ecosystem is a weak carbon source as a whole with annual discharge quantity of 8.20 t CO_2/hm~2. a which is at variance with traditional opinion and further study is needed.
Subapline meadow ecosystem: Under the condition of animal raising shut with fencing, the daily net fixation of CO_2 of grassland ecosystem is 12.76gCO_2/m~2.d, it is a carbon sink during the day except at 18:00 and 21:00 during which it is a weak source and of the rest time of the day it is an obvious strong carbon sink before 16:00 with CO_2 net fixation of 12.02g CO_2/m~2 which occupies 94.20% of daily total CO_2 net fixation. Under the natural pasturing condition, the daily net fixation of CO_2 of grassland ecosystem is 11.52gCO_2/m~2.d, it is a carbon sink during the day except at 9:00, 13:00, 14:00 and 21:00 during which it is a weak source and from 15:00 to 19:00 it is an obvious strong carbon sink with·CO_2 net fixation of 9.46gCO_2/m~2 which occupies 82.00% of daily total CO_2 net fixation. The lnain reason why a weak source appears at 13:00 and 14:00 is that photosynthesis rate of vegetation declines a little at noon, i.e. the so-called noon break. The period from May to September every year is growing period of forage grass, for which the annual CO_2 fixation of Bayinbuluke Subapline meadow ecosystem is up to 7.14 t CO_2/hm~2.a according to the preliminary estimate.
Estimation of carbon in Sangong River drainage area: Total reserves of carbon in Sangong River drainage area, Xinjiang is estimated to be about 11.18Pg, of which organic carbon is about 5.43Pg which occupying 48.54%, and inorganic carbon is about 5.75Pg which occupying 51.46% by taking soil ecosystem as basic unit, making use of GIS software, MapInfo software for statistics of corresponding soil ecosystem area in the way of field investigation and sampling analysis.
Comparing soil ecosystems with each other and the result shows that forest soil and meadow soil have bigger organic carbon flux and organic carbon capacity, but their inorganic carbon flux and inorganic carbon capacity are lower than other soil ecosystems obviously. Desert soil ecosystem has lowest organic flux and capacity, but higher inorganic carbon reserves.
Because of influence from. human factor on agricultural soil ecosystem, its inorganic carbon flux is lower than that of desert soil ecosystem markedly. But its organic carbon reserve is higher than that of desert soil ecosystem obviously owing to organic fertilizer application and some agriculture measures artificially. Different agricultural method of land usage and different agronomic crop decide that corresponding agriculture measures adopted are also not same absolutely, thus result in differences concerning the distribution of carbon in soil.
Estimation of carbon in subalpine meadow ecosystem: Aboveground biolnass on-the-spot survey is obtained in the way of actual quadrat harvesting and moisture converting. And combining with researching data from predecessors, the total carbon of aboveground vegetation in Bayinbuluke subalpine meadow ecosystem is estimated and the result shows that it is about 71.98 thousand t. Of which the aboveground part is about 31.99 thousand t occupying around 44.44%, and underground root system is about 39.99 thousand t occupying around 55.56%.
On the basis of actual measured bulk density of soil, organic carbon content of soil and investigated soil depth etc., area of corresponding grassland type is obtained in the way of using GIS MapInfo software to interpret remote sensing images, the organic carbon of soil in in Bayinbuluke subalpine meadow ecosystem is estimated by using corresponding formula. The results show that average carbon flux of soil in Bayinbuluke subalpine meadow ecosystem is 16.801Ckg/m~2, and the total organic carbon reserves of soil is about 30192.24 thousand t.
Effect of soil condition on decomposition rate of litter fall: Decomposition rate of organic matter on loamy soil is higher than that of clayey soil and sandy soil; decomposition rate of organic matter gains the highest under condition of medium moisture of soil; it is higher with deep bury than with shallow bury; it gains the highest under condition of medium saline concetnration. Different types of little falls also have different decomposition rates under same conditions because of different content of lignin mainly. Only one factor is studied in this paper under the condition that other factors are fixed. The interaction among the factors should be studies further Total NPP of terrestrial vegetation: The remote sensing model used in this paper is a parameter-based model. The parameters of the model can be inverted from the remote sensing data. Comparing with those models that compute the NPP by the correlation between NDVI and NPP, the remote sensing model based on the light energy utilization principle has three obvious advantages. On the basis of RS and GIS, the net primary production of terrestrial vegetation of China in every of ten days using the NOAA AVHRR data with five channels and 8km x 8km resolution is calculated
绿洲农田生态系统：各种类型的绿洲农田生态系统对的CO2固定量有一定日变化差异，在夜间的11个小时内，各农田生态系统都是碳源，即净释放CO2而白天小麦生态系统和棉花生态系统都有1个小时为碳源。研究表明玉米农田生态系统对CO2的净固定能力最强，24小时固定CO238.47g／m2。其次是小麦生态系统和棉花生态系统。从年固碳量来看，绿洲玉米生态系统为最高，达到141.66t CO2／hm2.a；其次为小麦生态系统，为122.60t CO2／hm2.a；棉花生态系统最低，为50.39t CO2／hm2.a。
荒漠林地生态系统：在夜间的11个小时内，各林地生态系统都是碳源，即净释放CO2。而在白天，云杉林地生态系统有7个小时为碳源，研究表明：云杉林地生态系统对CO2的净固定能力最弱，24小时内净释放CO2 4.22g／m2。最强的是梭梭林地生态系统，24小时净固定CO218.34 g／m2。红柳林地生态系统对CO2的固定能力稍弱于梭梭林地生态系统。从各观测样地的年固碳能力来看，梭梭林地生态系统固定量最大达到了9.29t CO2／hm2.a，红柳林地生态系统次之，为2.68t CO2／hm2.a。云杉林地生态系统总体来看是一个弱的碳源，年释放量达到8.20t CO2／hm2.a，这与传统的观点相左，尚需要进一步研究。
高山草地生态系统：围栏封育条件下，草地生态系统日CO2净固定量达到了12.76gCO2／m2·d，每天除18时和21时是弱的碳源外，其余时间均是碳汇。其中16时以前是碳的强汇，对CO2的净固定量达到12.02gCO2／m2，占到日总CO2净固定量的94.20％；自然放牧条件下，草地生态系统日CO2净固定量达到了11.52gCO2／m2·d，除9时、13时、14时和21时是弱的碳源外，其余时间均是碳汇。其中15～19时是碳的强汇，对CO2的净固定量达到9.46gCO2／m2，占到日CO2净固定量的82.00％。13、14时出现弱源的主要原因是由于植物的光合速率在中午有所下降即“午休”现象导致的。每年的5～9月份是牧草的生长期，对巴音布鲁克亚高山草地生态系统CO2的年固定量的初步估算结果表明：其CO2固定量达到7.14t CO2／hm2·a。
三工河流域土壤碳估算：新疆三工河流域总碳储量约为11.18Pg，其中有机碳约为5.43Pg，占48.54％，无机碳约为5.75Pg，占51.46％。各土壤生态系统相比较，森林土壤、草甸土壤具有较大的有机碳通量和有机碳容量，但其无机碳通量和无机碳容量均明显低于其它土壤生态系统；荒漠土壤生态系统的有机碳通量、碳容量最低，但其具有较高的无机碳储量。
亚高山草地生态系统碳估算：巴音布鲁克亚高山草地生态系统地上植物体碳总量约为7.20万t。其中地上部分约为3.20万t，约占44.44％；地下部分根系约为4.0万t，约占到55.56％。对巴音布鲁克亚高山草原生态系统的土壤有机碳进行了估算，结果表明：亚高山草原生态系统土壤有机碳的平均碳通量为16.80Ckg／m2，土壤有机碳总贮藏量约为3019.22万t。
土壤条件对凋落物分解速率的影响：壤质土上的有机物料分解速率高于粘质土和砂质土；中等土壤湿度条件下有机物料的分解速率最高；深埋方式有机物料的分解速率高于浅埋方式；中等土壤盐分条件下，有机物料的分解速率最高；不同类型凋落物，在其它条件完全相同的条件下，分解速率也不完全相同，主要是由于其木质素含量有所差异所致。本研究是在固定了其它因子的条件下，仅对单因子逐项进行了研究，因子间的交互作用尚需要进一步研究。
区域NPP的遥感估算：在AVHRR NOAA光谱数据的基础上，运用NOAA AVHRR的可见光波段、近红外波段和热红外波段来提取和反演地面参数，在地理信息系统的支持下，综合地学、生态学信息，精确估算陆地NPP，最终达到区域尺度范围内NPP动态监测的目的。
The experiment was conducted in the typical terrestrial ecosystem in arid region Sangong River drainage area in Xinjiang (and the selected grassland ecosystem is at Bayinbuluke Grassland) with Fukang Desert. Ecology Experimental Station of Xinjiang Ecological Geography Academic Institution of Chinese Academy of Sciences as the main backing, typical terrestrial ecosystem in arid region as the subject investigated. CO_2 source/sink relation of typical terrestrial ecosystem in arid region is studied systematically based on field-study data in the field, and carbon concerning Sangong River drainage area and Bayinbuluke subalpine meadow ecosystem is estimated. The main conclusions are as follows:
Oasis field ecosystem: There are certain differences among the fixation quantity of CO_2 of different types of field ecosystems. All field ecosystems are carbon source, i. e. net discharge of CO_2 during the 11 hours at night. However, there is one hour acting as carbon source for wheat-soil ecosystem and cotton-soil ecosystem in the daytime. Study shows that maize-soil ecosystem has biggest capability of CO_2 net fixation with fixation quantity of 38.47g/m~2 per hour. And wheat-soil ecosystem and cotton-soil ecosystem stand second on the list. From the point of view of annual carbon fixation quantity oasis maize-soil ecosystem is highest up to 141.66t CO_2/hm~2. a; the following one is wheat-soil ecosystem with 122.60 t CO_2 /hm~2. a; and cotton-soil ecosystem is lowest with 50.39 t CO_2/hm~2. a.
Desert forestland ecosystem: All forestland ecosystems are carbon source, i.e. net discharge of CO_2 during the 11 hours at night. However, there is 7 hours acting as carbon source for Picea schrenkiana forestland ecosystem. Study shows that Picea schrenkiana forestland ecosystem has the weakest capability of CO_2 net fixation with net discharge of 4.22g/m~2 within 24 hours. And Haloxylon ammodendron forestland ecosystem is of the strongest capability with CO_2 net fixation of 18.34g/m~2 within 24 hours. The CO~2 fixation capability of Tamarix ramosissima forestland ecosystem is slightly weaker than that of Haloxylon ammodendron forestland ecosystem. From the point of view of annual carbon fixation quantity of each observed plot, Haloxylon ammodendron forestland ecosystem is highest up to 9.29t CO_2/hm~2. a; the following one is Tamarix ramosissima forestland ecosystem with 2.68 t CO_2/hm~2. a; And Picea schrenkiana forestland ecosystem is a weak carbon source as a whole with annual discharge quantity of 8.20 t CO_2/hm~2. a which is at variance with traditional opinion and further study is needed.
Subapline meadow ecosystem: Under the condition of animal raising shut with fencing, the daily net fixation of CO_2 of grassland ecosystem is 12.76gCO_2/m~2.d, it is a carbon sink during the day except at 18:00 and 21:00 during which it is a weak source and of the rest time of the day it is an obvious strong carbon sink before 16:00 with CO_2 net fixation of 12.02g CO_2/m~2 which occupies 94.20% of daily total CO_2 net fixation. Under the natural pasturing condition, the daily net fixation of CO_2 of grassland ecosystem is 11.52gCO_2/m~2.d, it is a carbon sink during the day except at 9:00, 13:00, 14:00 and 21:00 during which it is a weak source and from 15:00 to 19:00 it is an obvious strong carbon sink with·CO_2 net fixation of 9.46gCO_2/m~2 which occupies 82.00% of daily total CO_2 net fixation. The lnain reason why a weak source appears at 13:00 and 14:00 is that photosynthesis rate of vegetation declines a little at noon, i.e. the so-called noon break. The period from May to September every year is growing period of forage grass, for which the annual CO_2 fixation of Bayinbuluke Subapline meadow ecosystem is up to 7.14 t CO_2/hm~2.a according to the preliminary estimate.
Estimation of carbon in Sangong River drainage area: Total reserves of carbon in Sangong River drainage area, Xinjiang is estimated to be about 11.18Pg, of which organic carbon is about 5.43Pg which occupying 48.54%, and inorganic carbon is about 5.75Pg which occupying 51.46% by taking soil ecosystem as basic unit, making use of GIS software, MapInfo software for statistics of corresponding soil ecosystem area in the way of field investigation and sampling analysis.
Comparing soil ecosystems with each other and the result shows that forest soil and meadow soil have bigger organic carbon flux and organic carbon capacity, but their inorganic carbon flux and inorganic carbon capacity are lower than other soil ecosystems obviously. Desert soil ecosystem has lowest organic flux and capacity, but higher inorganic carbon reserves.
Because of influence from. human factor on agricultural soil ecosystem, its inorganic carbon flux is lower than that of desert soil ecosystem markedly. But its organic carbon reserve is higher than that of desert soil ecosystem obviously owing to organic fertilizer application and some agriculture measures artificially. Different agricultural method of land usage and different agronomic crop decide that corresponding agriculture measures adopted are also not same absolutely, thus result in differences concerning the distribution of carbon in soil.
Estimation of carbon in subalpine meadow ecosystem: Aboveground biolnass on-the-spot survey is obtained in the way of actual quadrat harvesting and moisture converting. And combining with researching data from predecessors, the total carbon of aboveground vegetation in Bayinbuluke subalpine meadow ecosystem is estimated and the result shows that it is about 71.98 thousand t. Of which the aboveground part is about 31.99 thousand t occupying around 44.44%, and underground root system is about 39.99 thousand t occupying around 55.56%.
On the basis of actual measured bulk density of soil, organic carbon content of soil and investigated soil depth etc., area of corresponding grassland type is obtained in the way of using GIS MapInfo software to interpret remote sensing images, the organic carbon of soil in in Bayinbuluke subalpine meadow ecosystem is estimated by using corresponding formula. The results show that average carbon flux of soil in Bayinbuluke subalpine meadow ecosystem is 16.801Ckg/m~2, and the total organic carbon reserves of soil is about 30192.24 thousand t.
Effect of soil condition on decomposition rate of litter fall: Decomposition rate of organic matter on loamy soil is higher than that of clayey soil and sandy soil; decomposition rate of organic matter gains the highest under condition of medium moisture of soil; it is higher with deep bury than with shallow bury; it gains the highest under condition of medium saline concetnration. Different types of little falls also have different decomposition rates under same conditions because of different content of lignin mainly. Only one factor is studied in this paper under the condition that other factors are fixed. The interaction among the factors should be studies further Total NPP of terrestrial vegetation: The remote sensing model used in this paper is a parameter-based model. The parameters of the model can be inverted from the remote sensing data. Comparing with those models that compute the NPP by the correlation between NDVI and NPP, the remote sensing model based on the light energy utilization principle has three obvious advantages. On the basis of RS and GIS, the net primary production of terrestrial vegetation of China in every of ten days using the NOAA AVHRR data with five channels and 8km x 8km resolution is calculated
引文
1．《新疆森林》编辑委员会，新疆森林，中国林业出版社，北京：1989．8．
2．巴音布鲁克草原生态研究专集，干旱区研究，1991．Vol．(8)：(增刊)．
3．蔡祖聪，沈光裕．1998．土壤质地，温度和Eh对稻田甲烷排放得影响．土壤学报，35(2)：145-153
4．曹志洪，1994．化肥-环境与农业持续发展．土壤科学与农业持续发展．南京：江苏省科技出版社，183-195．
5．曹志洪，朱永官．1995．苏南稻麦两熟制下土壤养分平衡与培肥的长期实验．土壤，27(2)：60-64
6．陈德章，王明星．1995．稻田甲烷排放的初级模式．大气科学，19(6)：733-740．
7．常庆瑞主编，土地资源学，西北农林科技大学出版社，2002．11．
8．陈灵芝，黄建辉，严昌荣．1997．中国森林生态系统养分循环．气象出版社，北京．
9．陈彤编著，新疆塔城老风口防风阻雪生态工程评价与发展战略研究，新疆人民出版社，2003．8，第八章．
10．程伯容，许广山，丁桂方，张玉华和王伟．1984．Litterfall and its nutrient content of dominant types of forest ecosystem in Changbai mountmn．森林生态系统研究，4：19～24．
11．戴万宏，农田土壤空气CO_2动态和土壤—大气界面释放的研究，西北农林科技大学，博士学位论文：1～33．
12．董鸣主编，陆地生物群落调查观测与分析，中国标准出版社，北京：1997．5．
13．董云社，彭公炳．1996．温带森林土壤排放CO_2，CH_4，N_2O时空特征．地理学报，51增刊：120-128．
14．段争虎，刘新明，屈建军．1996，中国土地沙漠化对大气CO_2含量的影响．中国沙漠．10(2)：89-93．
15．方精云，刘国华，徐崇龄．1996．中国森林植被生物量和狰生产率．生态学报，16(4)：497-508．
16．方精云主编，全球生态学气候变化与响应，高等教育出版社，北京：2000．12．
17．傅声雷，蚁伟民，丁明懋．1995．鼎湖山不同植被类型下土壤微生物养分的矿化．植物生态学报，19(3)：217—224．
18．高素华．王春乙．1994．CO_2浓度升高对冬小麦，大豆籽粒成分的影响．环境科学，15(5)：24-30．
19．高云超，朱文珊，陈文新．1993．土壤微生物生物量周转的估算．生态学杂志，12(6)：6～10．
20．韩兴国，李凌浩，黄建辉．1999．生物地球化学概论．高等教育出版社，北京．
21．郝余祥．1982．土壤微生物．科学出版社，北京，1～167．
22．侯爱新，陈冠雄，吴杰．1997．稻田CH_4和N_2O排放关系及其微生物学机理和一些影响因子．应用生态学报，8(3)：270-274．
23．荒漠生态系统研究专集，干旱区研究，1990．Vol．(7)：(增刊)．
24．荒漠生态系统研究专集，干旱区研究，1992．Vol．(9)：(增刊)．
25．黄昌勇主编．2000．土壤学．中国农业出版社，北京，1—311．
26．黄承才，葛滢，常杰，卢蓉和徐青山．1999．中亚热带东部三种主要木本群落土壤呼吸的研究．生态学报，19(3)：324—328．
27．黄建辉，陈灵芝，韩兴国．1998．辽东栎枝条分解过程中几种主要营养元素的变化．植物生态学报，22(5)：398—402．
28．黄培祐编著，干旱区免灌植被及其恢复，科学出版社，北京：2002．2．
29．黄耀，2001，关于中国陆地生态系统碳循环研究的几点思考[J]，世界科技研究与发展，21世纪青年学者论坛，66-68；
30．黄志群，廖利平，高洪，汪思龙和于小军．2000．杉木根桩分解过程及几种主要营养元素的变化．应用生态学报，11(1)：40--42．
31．贾宏涛、陈冰等，防护林立地土壤肥力演替规律初步研究，林业科技，2002．12，(6)：15-17．
32．贾宏涛、蒋平安等，新疆塔城老风口工程区防护林立地土壤化学特征，土壤，2001．12，(6)：305-308．
33．蒋平安、贾宏涛、陈冰等，新疆老风口防风阻雪生态工程的土壤改良效应评价，水土保持学报，2002．9．Vol．16(3)：32-35．
34．拉夏埃尔．李博等译．植物生理生态学．1980．科学出版社，北京，1～314．
35．李长生．2001．生物地球化学的概念与方法—DNDC模型的发展．第四纪研究，2l(2)：89-99．
36．李长生．2000．土壤碳储量减少：中国农业之隐患——中美农业生态系统碳循环对比研究．第四纪研究，20(4)：345-350．
37．李迪强，孙成永，张新时．1998．中国潜在植被生产力的分布与模式．植被学报，40(6)：560-566．
38．李江风等著，新疆年轮气候年轮水文研究，气象出版社，北京：1989．11．
39．李晶，王明新，陈德章．1998．稻田甲烷排放连续测量中采样时间的选择．中国科学院研究生院学报，15(1)：24-29．
40．李克让主编，土地利用变化和温室气体净排放与陆地生态系统碳循环，气象出版社，北京：2002．5
41．李凌浩，刘先华，陈佐忠．1998．内蒙古锡林河流域羊草草原生态系统碳素循环研究．植物学报，40(10)：955-961．
42．李学垣主编，土壤化学，高等教育出版社，北京：2001．7．
43．李银鹏，季劲钧．2001，全球陆地生态系统与大气之间碳交换的模拟研究．地理学报，56(4)：379-389．
44．李玉娥，林而达，1993．影响稻田甲烷形成和排放的因子及控制技术的研究进展冲国农业气象．14(3)：50-53．
45．廖利平，高洪，汪思龙，马越强，黄志辉和子小军．2000．外加氮源对杉木叶凋落物分解及土壤养分淋失的影响．植物生态学报，24(1)：34～39．
46．林而达等，全球气候变化对中国农业影响的模拟，中国农业科学技术出版社，北京：1997．3．
47．林开敏，马祥庆，范少辉，张小全，卓仕安和金昌雄．2000．杉木人工林林下植物的消长规律．福建林学院学报，20(3)：231--234．
48．刘国华，傅伯杰。2000，中国森林碳动态及其对地球碳平衡的贡献．生态学报，20(5)：733～740
49．刘建栋，林振山，卢其尧．1998．冬小麦光和生产潜力数值模拟．地理研究，17(1)：12～19．
50．刘绍辉，方精云．1997．土壤呼吸的影响因素及全球尺度下温度的影响．生态学报，17(5)：469～476．
51．刘允芬．1998．西藏高原农田土壤CO2排放研究初报．自然资源学报，13(2)：181～186．
52．刘允芬．1995．农业生态系统碳循环研究，自然资源学报，10(1)：1～8．
53．刘允芬．1998．中国农业系统碳汇功能，农业环境保护，17(5)：197～202．
54．卢俊培，刘其汉．1988．海南岛尖峰岭热带林凋落物研究初报，植物生态学与地植物学，12(2)：104～112．
55．罗承平，薛纪渝．1996．大气中CO_2浓度增加对我国农业生产的影响，热带地理，16(3)：191～194．
56．马志贵，王金锡．1993．大熊猫栖息环境的森林凋落物动态研究，植物生态学与地植物学学报，17(2)：55～163．
57．孟范平．1994．大气CO_2浓度增加对我国农业生产的影响．农村生态环境(学报)，10(4)：62-66．
58．潘根兴、曹建华、周运超，2000，土壤碳及其在地球表层系统碳循环中的意义[J]，第四纪研究，Vol．20，No．4：325-333．
59．潘瑞炽，董愚得编著，植物生理学，高等教育出版社，北京：1995．5(第三版)．
60．钱翌，何章起主编，普通生态学，新疆科技卫生出版社(K)，乌鲁木齐，1994．3．
61．任海，彭少麟，刘鸿先，余作岳和方代有．1998．小良热带人工混交林的凋落物及其生态效益研究．应用生态学报，9(5)：458-462．
62．任荣荣编著，中国森林地理景观概貌，中国农业出版社，北京：2002．7．
63．任泳红，曹敏，唐建维，唐勇和张建侯，1999．西双版纳季节雨林与橡胶多层林凋落物动态的比较研究，植物生态学报，23(5)：418--425．
64．上官行健，王明新，沈壬兴．1993，稻田甲烷排放规律．地球科学进展，8(5)：23-35．
65．沈宏，曹志洪．1999a．土壤活性有机碳的表征及其生态效应，生态学杂志，18(3)：32—38．
66．沈宏，曹志洪，王志明．1999b．不同农田生态系统土壤碳库管理指数的研究，自然资源学报，14(3)：206--211．
67．沈宏，曹志洪．2000．不同农田生态系统土壤碳库管理指数的研究，生态学报，(20)：663-668．
68．宋文质，王少彬，苏维翰，王智平．1996．我国农土壤的主要温室气体CO_2、CH_4和N_2O排放研究．环境科学，17(1)：85-92．
69．田大伦，康文星，文仕知等著，杉木林生态系统学，科学出版社，2003．4．
70．田中正之．1992．地球在变暖(石广玉，李昌明译)．气象出版社，北京，1～132．
71．汪杏芬，李世仪，白克智和匡廷云．1998．CO_2倍增对植物生长和土壤微生物生物量碳、氮的影响，植物学报，40(12)：1169～1172．
72．汪业茂，1999，中国森林生态系统区域碳循环研究，中国科学院自然资源综合考察委员会博士研究生学位论文．
73．王风友．1989．森林凋落物量研究综述．生态学进展，6(2)：82～89．
74．王其兵，李凌浩，刘先华．1998，内蒙古锡林河流域草原土壤有机碳纪碳素的空间异质性分析．植物生态学报，22(5)：409-414．
75．王荣．1992．海洋生物泵与全球变化．海洋科学，99(1)：18—21．
76．王荣栋，尹经章主编，作物栽培学，新疆科技卫生出版社(K)，乌鲁木齐，1997．8．
77．王绍强、周成虎，1999，中国陆地土壤有机碳库的估算[J]，地理研究，Vol．18，No．4．349-355；
78．王效科，冯宗异．2000．中国森林生态系统中植物固定大气碳的潜力．生态学杂志，19(4)：72-74．
79．王修兰，1996．全球农作物对大气CO_2及其被增的吸收量估算，气象学报，54：466-426．
80．王彦辉，Peter Rademacher，Horst Folster．1999．环境因子对挪威云杉林土壤有机质分解过程中重量和碳的气态损失影响及模型，生态学报，19(5)：641～646．
81．王周琼，李述刚，程心俊著，草炭绿化荒漠的实践与机理，科学出版社，北京：2002．2．
82．王周琼，李述刚，程心俊著，荒漠绿洲农田生态系统中养分循环，科学出版社，北京：2002．2．
83．王周琼，李述刚，程心俊著，有机无机复合与荒漠化防治，科学出版社，北京：2003．10．
84．往事供，杨德保，周玉素，巴特尔，辛春兰．1995，我国西北地区“94．4'’沙尘暴成因探讨，中国沙漠，15(4)：332-338．
85．文启凯主编，农田土壤高效持续利用与管理，新疆大学出版社，2001．12．
86．吴建国，2002，土地利用变化对土壤有机碳的影响，中国林业科学研究院森林生态研究所博士论文．
87．萧祖荫，于贵瑞，1990，耕作制度基本原理与优化设计方法，辽宁科学技术出版社，沈阳，1～257．
88．谢香方主编，北疆铁路沿线区域综合开发与整治，新疆人民出版社，乌鲁木齐：1997．9．
89．谢云．1997，中国的农业发展与全球变化．北京师范大学学报(自然科学版)，33(3)：422-426
90．徐华，蔡祖聪，李小平．2000．冬作季节土地管理对水稻土CH_4排放季节变化的影响．应用生态学报，11(2)：215～218．
91．许大全，光合作用效率，上海科学技术出版社，上海．2002．6．
92．许大全，李德耀等，1987，毛竹叶片光合作用的气孔限制研究，植物生理学报，13：92～95
93．亚历山大，1983，土壤微生物导论，广西农学院农业微生物学教研组译，科学出版社，北京：1～285．
94．姚槐应，1999，红壤微生物量在土壤—黑麦草系统中的肥力意义，应用生态学报，10(6)：725～728．
95．叶笃正，陈泮勤主编，中国的全球变化预研究，地震出版社，北京：1992．11．
96．蚁伟民，丁明懋等，1994，鼎湖山黄果厚壳桂群落的凋落物及其氮素动态，植物生态学报，18(3)：228～235．
97．殷士学，1983，土壤微生物生物量及其与养分循环关系的研究进展，土壤学进展，21(4)：1～8．
98．于贵瑞，全球变化与陆地生态系统碳循环和碳蓄积，气象出版社，2003．5．
99．于强，陆佩玲，刘建栋．1999．作物光温生产力模型及南方水稻适宜生长期的数值分析，自然资源学报，14(2)：163～168．
100．袁道先，2001，地球系统的碳循环和资源环境效应，第四纪研究，Vol．21，No．3：223-230；
101．曾天勋，1986，森林养分循环，生态科学，1：69～73．
102．张家宝，史玉光等著，新疆气候变化及短期气候预测研究，气象出版社，北京：2002．7．
103．张万儒主编，中国主要造林树种土壤条件，中国科学技术出版社，北京：1998．9．
104．张小全，徐德应著，森林生长和产量生理生态模型，中国科学技术出版社，北京：2002．6．
105．张远辉，王伟强，陈立奇，2000，海洋二氧化碳的研究进展，15(5)：113～330．
106．郑循华，王明星，王跃思．1997．华东稻田CH_4和N_2O排放．大气科学，21(2)：231～237．
107．朱新广，张其德．1999．Nacl对光合作用的研究进展，物理学通报，16(4)：332-338．
108．邹琦主编，作物抗旱生理生态研究，山东科学技术出版社，济南1994．12．
109. Abrahamsen P and Hansen S.2000. DAISY: An open soil-crop-atmosphere stem model. Environmental Modeling and Software, 15: 113-330.
110. Amthor J S. 1994. Scaling CO_2-photosynthesis relationships from the leaf to the canopy. Photosynthesis Research, 39: 321～350.
111. Anderson D W, Sagger S, Bettany J R and Stewart J W B. 1981. Particle size fractions and their use in studies of soil organic matter: 1. The nature and distribution of forms of carbon, nitrogen, and sulfur. Soil Science Society of America Journal, 45: 62-72.
112. Andren O and Katter T. 1997. ICBM: the introductory carbon balances. Ecological Alpplication, 7(4): 1226-1236.
113. Arrouays D, Deslais W, Badeeau V.2001. The carbon content of topsoil and its geographical distributionin France. Soil Use and Management, 17: 7-11.
114. Atjay G L, Ketner P, 1979. Terre strait Parlay production and photomaps. In the global carbon cycle John Wiley and sons: Chichest, 129-128.
115. Aubinet M, Grelle A, Ibrom A, Rannik, Moncrieff J, Foken T, Kowalski AS, Martin P H, Berbigier P, Bernhofer C, Clement R, Elbers J, Granier A, Grunnwald T, Morgenstern K, Pilegaard K, Rebmann C, Snijders W, Valentini R and Vesala T. 2000. Esitimater of the annual net carbon and water exchage of European forests: The EUROFLUX methodology. Advances in Ecological Research, 30: 113-175.
116. Bacastow R, Maier R E. 1990. Ocean circulation model of the carbon cycle. Climate Dynamics, 4: 95-125.
117. Baker D N, J R Lambert, J M Mckinion. 1983. GOSSYM: A simulator of cotton crop growth and yield, S. C. Expt. Bull. Technical Bulletin No. 1089, S. C. Agricultural Experiment Station, December.
118. Baldochi D D, Hicks B B, Meyers T P. 1988, Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69:1331-1340.
119. Batjes N H, Sombroek W G, 1997. Possibilities for carbon sequestration in tropical and subtropical soils. Global Change Biology,3: 161-173.
120. Beare, M H, Cabrera, M L, Capbell, C A, Coleman D C.1994, Aggregate-protected and unprotected organic matter pools in conventional-and no-tillage soils. Soil Sci.SocAam.J, 58: 787-795.
121. Biederbeck V O. Janzen H H.1994, Labile soil organic matter as influenced by cropping practices in an arid enviroment. Soil Biology Biochemistry, 26:1647-1656.
122. Bohn H L.1976.Estimate of organic carbon in world soils. Sci. Sco.Am.J.40: 468-470.
123.Buringh P.1984. Original carbon in soils of the world. In: Wood well G M, (ed). The Role of Terrestrial Vegetation in the Global Carbon Cycle. John Wiley&Sons, 23:247.
124. Businger J A, 1986.Evaluation of the accuracy with which dry deposition can be measured with current micrometeorological techniques. J Clam Apply Meteorol, 25:1100-1124.
125. Buyanovsky G A, Wagner G H.1998.changing role of cultivated land in the global carbon cycle, Biol.Fertil.Soils, 27:242-245.
126. Capbell C A, McConkey B G, Zenter R P.1995.Carbon sequestration in a brown chernozem as affected by tillage and roatation. Can. J. Soil Sci.75: 449-458.
127. Capbell C A, Zentner R P. 1993.Soil organic matter as influenced by crop rotation and fertilization. Soil Sci.Am.J, 57:1034-1040.
128. Carter M R, Gregorich E G, Angers D A, Donald R G, Bolinder M A.1998.Organic C and N storage, and organic C fractions, in adjacent cultivated and forested soils of eastern Canada. Soil And Tillage Research, 47(3-4):
129. Cerling T E.1992.Use of carbon isotopes in pale sols as an indicator of the P (CO_2) of the pale atmosphere, Global Biochem.Changes, 6:312-315.
130. Christensen B T.1995.Matching measurable soil organic matter fractions with conceptual pools in simulation models of carbon turnover: revision of model structure. In: Powlson, D. S. Smith, P. and Smith, J.U. (eds), Evaluation of Soil Organic Matter Models, NATOASI Series l,38:SpeingerVerlag, Berlin, pp.143-160.
131. Cole C V, Cerri C, Minami K, Mosier A, Rosenberg N J and Sauerbeck D.1996.Agricultural options for mitigation of greenhouse gas emission, Chapter23,Climate change 1995: Impacts Adaptations and Mitigation of Climate Change,pp.745-771,Report of IPCC Working Group II,Cambridge University Press, Cambridge, U.K., p.880.
132. Houghton R A, Skole D L, Carbon. In: The Earth As Transformed by Human Action (eds Turner B L, Clark W C, Kater R Wetal). Cambridge University Press, 1990. 393-408.
133. Manabe S, Tang W and Fu L L.Century.1993 Scale effects of increased atmospheric CO_2 on the ocea- atmosphere system. Nature, 364:215-218.
134. Marl and G, Brenkert A and Olivier J G J.1999.CO_2 form fossil fuel burning: a comparison of ORNL and EDGAR estimates of national emissions. Environmental Science and Policy, In press.
135. Mathes K and Secondary TH.1985.Soil respiration during secondary sussession influences of temperature and moisture Soil Biol. Biochem, 17(2): 205- 211.
136. Mc Claugherty C A, Pastor J, Aber J D and Melillo J M.1985.Forest litter decomposition in relation to soil nitrogen dynamics and litter quality. Ecology, 66:266-275.
137. Mccarrthy J L, Brewer P G and Feldman G.1986.Global ocean flux. Oceanus, 29(4): 16-26.
138. Mchale P J, Mitchell M J and Bowles F P .1998.Soil warming in a northen hardwood forest: trace gas fluxes and leaf litter decomposition. Canadian Journal of Forest Research, 28:1365-1372.
139. Menzel A and Fabian P.1999.Growing season extended in Europe. Nature, 397:659.
140. Moina J A E, Clapp C E, shaffeer M J, Chichester F W and Larson W E .1983.NCSOIL, a model of nitrogen and carbon transformations in soil : description, calibration, and behavior. Soil Science. Society of America Journal, 47:85-91.
141. Myneni R B, keeling C D, Tucker C J, Asrar G and Nemani R R. 1997.Increased Plant growth in the northern high latitudes form 1981 to 1991.Nature, 386:698-702.
142. Nakane K, Tsubota H and yamamoto.l984.Cycling of soil carbon in a Japanese Red Pine Forest I. Before a clear-felling. Botany Managemeut, 97:39-60.
143. Niklinska M, Maryanski M and laskowski R.1999.Effect of temperature on humus respiration rate and nitrogen mineralization: implication for global climate change. Biogeochemistry, 44:239-257.
144. Oughton R A, Skole D L, Carbon. In: The Earth As Transformed by Human Action (eds Turner B L, Clark W C, Kater R Wetal). Cambridge University Press, 1990.393-408.
145.Parton W J and Rasmussen PE.1994.Long-term effects of crop management in wheat fallow: CENTURY model simulations, Soil Science Society of America Journal, 58:530-536.
146. Parton W J, Persson J and Anderson D W. 1983.Simulation of organic matter changes in Swedish soils. In : Lauenroth W K, Skogerboe G V and Flug M,(Eds.), Elsevier , Amsterboe G V and Flug M,(Eds.), Elsevier, Amsterdam,511-516.
147. Pieter P T and Peter S B.1995. 气侯变化与二氧化碳永恒.AMBIO, 24(6): 375-377.
148. Post W M , Izaurralde R C, Mann L K and Bills N.l998.Monitoring and verifying soil organic Carbon sequestration. In: Rosenberg N, Izaurralde RC, Malone EL, (Eds.), Carbon sequestration in soil .Columbus: battelle Press,41-66.
149. Post W M, Emanuel W R and Zinke P J, Stangenberger AG.1982.Soil carbon pools and life zones. Nature, 298:156-159.
150. Post W M, Emanuel W R, Zinke P Jetal. Soil carbon Pools and life zones, Nature, 1982, 298, 156-159.
151. Post W M, Peng T H, Emanuel W Retal. The Global Carbon Cycle. American Scientist, 1990, 78: 310-326.
152. Post W M, Peng T H, Emanuell W R, King A W, Date V H and DeAngelis DL.1990.The Global carbon Cycle .American Scientist,78:310-326.
153. Powlson D S and Jenkinson D S.1976.The effect of biotical treatment on metabolism in soil II. Gama irradiation, autoclaving, air-drying and fumigation. Soil Biology and Biochemistry,8:179-188.
154. Prentice I C, Farquar G D, Fasham MJ R, oulden M L, Heimann M, Jaramillo V J, Kheshgi H S, Le quere C, Scholes R J and Wallace D W R.2001.In: Houghton J T and Yihui D (Eds.), Climate Chang 2001:the scientific basis. The Intergovernmental Panel on Climate Chang (IPCC) third assessment report. Cambridge University Press, Cambridge, U K, 183-237.
155. Prentice K C and Fung IY .1990.The sensitivity of terrestrial carbon storage to climate change. Nature, 46:48-51.
156. Radke J K, Shaffer M J and Saponara J.1991.Application of the Nitrogen-Tillage-Residue-Management (NTRM) model to low-input and conventional agricultural systems. Ecological Modeling, 55:241-255.
157. Raich J W and Potter C S.1995.Global Patterns of carbon dioxide emissions form soil, Global Biogechemical Cycles, 9(1): 23-36.
158. Raich J W and Schelesinger W H.1992.The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus, 44B: 81-99.
159. Ramankutty N and Foley J A. 1999.Estimating historical changes in global land cover: Croplands form 1700 to 1992.Global Biogeochemical Cycles, 13:997-1027.
160. Ramankutty N, Foley J A.1998.Characterizin patterns of global land use: An analysis of global croplands date. Global Biogeochemical Cycles, 12:667-685.
161.Randerson J T, Thompson M V, Conway TJ, Fung I Y and Field C B.1997.The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide. Global Biogeochemical. Cycles, 11:535-560.
162. Ritchie J T, Godwin D C and Otter-Nacke S.1986.CERES-Wheat:A Simulation Model of Wheat Growth and .Department, CERES Model Description .Department of Crop and Soil Science, Michigan Sate University, Easst Lansing.
163. Rodhe H, Charlson R and CraWford E.1997.Svante Arrhenius and the Ereenhouse Effect. AMBIO, 26(1): 2-5.
164. Rojas K W, Ma L, Hanson J D and Ahuja L R. 2000.RZWQM98 Use Guide .In : Ahuja L R, RojasK W, Hanson J D, Shanffer M J and Ma L,(Eds.),Root Zone Water Resources Publication LLC, Highands Ranch, CO,327-364.
165. Sarmiento J L and Orr J C.1992.A perturation simulation of CO2 uptake in an ocean general circulation model. Journal of Geophysical, 97:3621-3645.
166. Schimel D S .1995.Terrestrial ecosystems and the carbon cycle. Global Change Biology, 1:77-91.
167. Schimel D S Brawell B H, Mckeown R, Ojima DS, Parton W J and Pulliam W. 1996.Climate and nitrogen controls on the geography and timescales of terrestrial biogeochemical cycles, 10:677-692.
168. Schmel D S, Brown V B, Hibbard K A, Lund C P and Archer S .1995.Aggretation of species properties in biogeochemical models: an experimental approach. In: Jones C G, Lawson J H J.H. Lawson, (Eds.), Ling Species and ecosystems. Chapman and Hall. New Youk, U.S.A.209-214
169. Schuze E and Heimann M.1998.Carbon and water exchange of terrestrial ecosystems. In the context of global change. Cambridge University Press, Cambridge.
170. Schuze E, Wirth C and Heimann M.2000.Managing Forest After Kyoto. Science, 289:2058-2059.
171. Semenov S M, Kounina I M and Koukhta B A.1998.An ecological analysis of anthropogenic changes in ground -level concentrations of O_3, SO_2, and CO_2 in Europe. Do lady Biological Sciences, 361:344-347.
172. Semenov S M, Kounina I M and Koukhta B A.1999.Tropospheric ozone and plant growth in Europe. Meteorology and Hydrology Publishing Center, Moscow, Russia, 208(in Russian).
173. Shaffer M J and Ma L.2001.Carbon and nitrogen dynamics in upland soils. In: Shaffer M J, Ma L and Hansed S (eds.), modeling Carbon and Nitrogen Dynamical for Soil. In: Shaffer M J, Ma L and Nitrogen Dynamics for soil Management. Lewis Publishers, Boca Raton, FL, 1-10.
174. Shaffer M J, Bartling P N S and Ascough II J C.2000a.Object-oriented simulation of integrated whole farms: GPFARM framework. Computers and Electronics in Agriculture, 28:29-49.
175. Shaffer M J, Larson WE.1987.A soil-Crop Simulation Model for nitrogen, Tillage, and Crop Residue Management. U S. Department of Agriculture, Conservation Research Report, No: 34-1:103.
176. Shaffer M J, Rojas K, DeCoursey D G and Hebson C S.2000b.Nutrient Chemsitry Processes-OMNI. In root Zone Water Quality Model. Water Resources Public Inc., Englewood, CO, chap. 5:119-144.
177. Shen S M, Pruden G and Jenkinson D S.1984.Mineralization and immobilization of nitrogen in fumigated soil and the measurement of microbial biomass nitrogen. Biology and Biochemisty, 16(5):437-444.Siegenthaler U, Sarmiento J L.1993.Atmopheric carbon dioxide and the ocean.Nature,365:119-125.
178. Siegenthaler U Oeschger H.1987. Biospheric CO_2 emissions during the past 200 yeaes reconstructed by disconsolation of ice core data. Tell us, 39B: 140-154.
179. Siegenthaler U, joos F .1992.Use of a simple model for studying oceanic tracer distributions and the global carbon cycle, Talus, 44B: 186-207.
180. Smith T M, Shugart H H, Bonan G B and Simith J B.1992.Modeling the potential response of vegetation to global Climate Change: The Ecological consequences. Academic Press, London, 93-116.
181. Sombroke W G, Nachtergaele F O, Hebel A. 1993 .Amount, dynamics and sequestering of carbon in tropical and subtropical soils. AMBIO, 22(7): 417-426.
182. Sundquist E t.1993. The global carbon dioxide budget. Science, 259:934-941.
183. Swft M J, Heal O W and Anderson J M.1979. Decomposition in terrestrial ecosystem. University of California Press, Berkeley, Calidornia, USA.
184. Tain H, MelilloJ, Kicklighter D, McGuire A D, Hellfrich J, Moore IIIB and Vorosmarty C.1998.Effect of internal climate variability on carbon storage in Amazonian ecosystems. Nature, 396:664-667.
185. Takahashi T, Goddard D and Suther land, D W.1986.Sessonal and geographic variability of carbon dioxide sink/source in the oceanic areas. Journal of Geophysical Research, 91:10 517-10 527.
186. Tans P , Fung I Y and Takahashi T.1990.Obervational constraints on the global; atmospheric carbon dioxide budget. Science, 247:1431-1438.
187. Tans P and Wallace D WR.1999.Carbon cycle research after Kyoto. Tellus, 51B: 562-571.
188. Tans P . Conway T J and Nakazawa T.1989.Latitudinal distribution of the source and sinks of atmospheric carbon dioxide derived form surface observations and an atmospheric transport model. Journal of Geophysical Resrarch, 94:5151-5173.
189. Waring R H, Schlesinger W H.1985.Forest ecosystems: concepts and management. Academic Press, New York, USA.
190. Watson R T, Rodhe H, Oeschger H and Siegenthaler U. Greenhous gases and aerosols, In: Houghton JT, Jenkins G J and Ephraums J (Eds.), Climate Change (IPPC) Scientific Assessment, Cambridge University Press, 1990,1-40.
1. Aase J K, et al. Spectral radiance estimation of leaf area and leaf phytomass of small grains and native vegetation. IEEE Transactions on Geoscience and Remote Sensing. 1986. 24(5): 685-691.
2. Achard F., Estreguil C. Forest classification of southeast Asia using NOAAAVHRR data. Remote Sensing Environment. 1995. 54: 198-208.
3. Allen R.G, L.s. . M. Smith. Crop evapotranspiration -Guidelines for computing crop water requirements -FAO Irrigation and drainage paper 56. FAO - Food and Agriculture Organization of the United Nations. Rome, 1998
4. Alward R. D. , Delting J K. , Milchunas D.G. Grassland vegetation changes and nocyurnal global warming. Science. 1999. 283:229-231.
5. Barclay, H. J .Conversion of total leaf area to projected leaf area in lodgepole pine and Douglas-fir. Tree Physiol., 1998,18:185-193.
6. Baret F., Jacquemoud S., and Hanocq J. F. The soil line concept in remote sensing. Remote Sensing Environment. 1993. 7:65-82 .
7. Bazzaz, F.A.(1979(, The physiological ecology of plant succession, Annu. Rev. Ecol. Syst. 10:351-371
8. Becker, . & Li, Z.(1990) Towards a local split window method over land surfaces. Int. J. Remote Sensing, 11.369-393
9. Begue, A.(1993), Leaf area index, intercepted photosyntheically active radiation, and spectral vegetation indices: sensitivity analysis for regularly shaped canopies, remote sens. Environment, 46:45-59
10. Benedetti R. Rossin P. On the use NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing Environment. 1993. 45:311-326
11. Black T.A., Chen J.M., Lee X., Sagar R. M. Characteristics of shortwave and longwave irradiances under a Douglas-fir forest stand. Can.J.For.Res. 1991, 12:1020-1028
12. Blackburn GA., and Milton E.J., 1997, An ecological survey of deciduous woodlands using airborne remote sensing and geographical information systems(GIS). Int. J. Remote sensing, 18(9): 1919-1935
13. Boegh E., H. Soegaard, N. Broge, C.B. Hasager, N. O. Jensen, K. Schelde, and A. Thomsen. Airborn multispectral data for quantifying leaf area indes, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment. 2002, 81:179-193.
14. Bonan G B, Pollard D, Thompson S L. Effects of boreal forest vegetation on global climate. Nature, 1992, 359:716—718
15. Box E. O Holben B.N, Kalb V. Accuracy of the AVHRR Vegetation Index as a predictor of biomass, primary productivity and net CO_2 flux. Vegetation . 1989, 80:71-79.
16. Boyd D S, et al. The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0um). International Journal of Remote Sensing. 1999. 20 (5): 1017-1023.
17. Burgess D.W.,Lewis P.,and Muller A.L., Topographic effects in AVHRR NDVI data, Remote Sens.Environ., 1995,54:223-232.
18. Cao M., Woodward F L. Dynamics responses of terrestrial ecosystem carbon cycling to global climate change. Nature. 1998,393:249-252.
19. Chameides, W.L., Kasibhatla, P.S., Yienger, J., and Levy, H., II(1994), growth of continental-scale metro-agro-plexes, regional ozone pollution and world food production, science, 264:74-77
20. Chase T M, Pielke R A, Kittel T G F, et al. Sensitivity of a general circulation model to global changes in leaf area index. J Geophys Res, 1996, 101: 7393—7408
21. Chen J M, and J Cihlar. 1996. Retrieving leaf area indes for boral conifer forests using landsat TM images. Remote Sensing of Environment. 55:153-162.
22. Chen J., sylvain G Leblance, John R. Millcr, Jim Freemantle, Sara E. Loechel, Charles L. Walthal, Kris A. Innanen, and H.Peter White, Compact Airborne Spectrongraphic imager(CASI)used for mapping biophysical paramenters of boreal forests, Journal of Geophysical Research,1999,104(D22),27945-27958.
23. Chen W, Chen J. Liu J., and Cihlar J. Approaches for reducing uncertainties in regional froest carbon balance. Global Biogeochemical Cycles.2000, 14(3):827-838.
24. Chiba Y. Simulation of CO_2 budget and ecological implications of sugi (Cryptomeria japonica) man-made forests in Japan. Ecological Modeling. 1998,111:269-281.
25. Chong D.L.S., Mougin E., and Gastellu-etchegorry J.P. 1993, Relating the global vegetation index to net primary productivity and actual evapotranspiration over Africa. Int. J. Remote Sensing, 14(8): 1517-1546
26. Choudhury B.J. A sensitivity analysis of the radiation use efficiency use efficiency for gross photosyntheiis and net carbon accumulation by wheat. Agriculture and Forestry Meteorology. 2000, 101:217-234.
27. Christopher B. Field, et al, Global Net Primary Production: Combining Ecology and Remote Sensing, Remote Sensing Environment, 1995,51:74-88
28. Cihlar J., M Heimann, and R. Olson. Terrestrial Carbon Observation: The Frascati report on in situ carbon data and information. Environment and Natural Resources Series No. 5, FAO, Rome,2002.
29. Cohen W.B and Justice C.O. Validating MODIS terrestrial ecology products:linking in situ and satellite measurements. Remote Sensing of Environmet. 1999, 70:1-3.
30. Collatz G.J., Ball J.T., Crivet C, Berry J.A. Physiological and environmental regulation boundary layer. Agriculture and Forest meteorology, 1991, 54:107-136.
31. Conway, J. 1997, Evaluating ERS-1 SAR data for the discrimination of tropical forest from other tropical vegetation types in Papua New Guinea. Int. J. Remote sensing, 18(14):2967-2984
32. Cramer and the participants of the "Postsdam '95'"NPP model intercomparison workshop. Net primary productivity model intercomparison activity (NPP). IGBP?GAIM repot series report #5, 1995.
33. Cusack .G S, et al. Calibrating airborne vegetation data for hydrological applications under dry conditions. International Journal of Remote Sensing. 1999. 20 (11): 2221 -2233.
34. DeFires, R., Hansen, M., and Sohlberg, R., 1998, Global land cover classifications at 8km spatial resolution: The use of training data derived from landsat imagery in decision tree classifiers, International Journal of Remote Sensing; 19(16):3141-3168.
35. Denning A.S., Collatz G.J., Zhang C, et al. Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model. Part 1: Surface carbon fluxes. Tellus. 1996.48B:521-542.
36. Dickinson R E, Henderson-Sellers A, Kennedy P J, et al. Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR CCM. NCAR/TN-275-STR, Boulder: National Center for Atmospheric Research, 1986
37. Dickinson R E, Henderson-Sellers A. Modeling tropical deforestation: a study of GCM land-surface parameterization. Q J R Meteorol Soc, 1988, 114: 439—462
38. Dye D.G, and Goward S.N. 1993, Photosynthetically active radiation absorbed by global land vegetation in August 1984, Int. J. Remote sensing, 18:3361-3364
39. Dye, D.G & Shibasaki, R.(1995) Intercomparison of global PAR data sets. Geophys. Res. Lett. 22, 2013-2016
40. E.Raymond Hunt,JR., Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDV(?), Int.J.Remote Sens. 1994,15:1725-1730.
41. Eck T.F., and Dye D.G, 1991, Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance. Remote sensing, environment, 38:135-146
42. Englin J., Boxall P. C, Chakraborty K. et al. Valuing the impacts of forest fires on backcountry forest recreation. Forest Science. 1996. 42(4):450-455.
43. Euskirchen E.S. Carbon fluxes in managed Great Lakes forested ecosystems: an empirical and model-based approach. Propsal for Ph.D.Research.Michigan Technological University.2001.(communications).
44. Fan S., M. Gloor, J. Mahlman,S.Pacala,J.Sarmiento, T. Takahashi, and P. Tans. A large terrestrial carbon sink in North America implied by atmospheric and oceanic carbon dioxide data and models. Science. 1998,(282):442-446.
45. Fang C. and J.B. Moncrieff. The dependence of soil CO2 efflux on temperature. Soil Biology and biochemistry. 2001,33:155-165.
46. Fang Zongyi, Xiao Qianguang et al. Monitoring of some surface properties in China by NOAA AVHRR. Edited by Ye Duzheng, Lin Hai et al. China Contribution to Global Change Studies. 146-151. 1995. Science Press, Beijing, China.
47. Field C.B., Behrenfeld M.J., Randerson J.T., and Falkowski, 1908, Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281:237-240
48. Field C.B., Randerson J.T., Malmstrom C.M., 1995. Global net primary production: combining ecology and remote sensing. Remote Sensing of Environmet, 51,1,74-88.
49. Foley J A, Prentice I C, Ramankutty N, et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem Cycles, 1996, 10(4): 603—628
50. Foley J A. Feedbacks between climate and boreal forests during the Holocene epoch. Nature, 1994, 371: 52—54
51. Franklin S E. Modeling forest net primary productivity with reduced uncertainty by remote sensing of cover typer and leaf area index. In Hunsaker C T, Goodchild M F, Frield M A, and Case T J ed Spatial uncertainty in ecology: Implications for remote sensing and GIS application. Nww Youk, Berlin, Hcidelbcrg:Springer-Verlag. 2001.284-307
52. Franklin S.E., Lavigne M.B., Deuling M.J., Wulder M.A., and Hunt E.R. 1997, Estimate of leaf area index using remote sensing and GIS data for modeling net primary production. Int. J. Remote sensing, 18(16):3459-3471
53. Friedl M.A., D.K. Mclver, J.C.F. Hodges, X.Y. Zhang, D. Muchoney, A.H. Strahler, C.E. Woodcock, S.Gopal, A Schneider, A. Cooper, A. Baccini, F. Gao and C. Schaaf. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environmetn.2002,83(1-2):287-302
54. Friedlingstein, P., Delire, C, Muller, J.F., and Gerard, J., (1992), The climate induced variation of the continues biosphere: a model simulation of the last glacial maximum geophys. Res. Lett. 19:897-900
55. Frield M A, McGwire K C, Mclver D K. An overvies of uncertainty in optical remotely sensed data for ecological applications. In Hunsaker C T, Goodchild M F, Friedl M A, and Case T J ed. Spatial uncertainty in ecology: implications for remote sensing and GIS application. Nww Youk, Brlin, Heideiberg:Springer-Verlag.2001.25 8-283
56. Friend A.D. Modelling canopy CO2 flux: are 'big-leaf simplifications justified? Global Ecology and Biogeography. 2001, (10):603-619.
57. Fuller D O. Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993. International Journal of Remote Sensing. 1998. 19(10): 2013-2018.
58. Fung, I.Y., Tucker, C.J., and Prentice, K.C.(1987), application of advanced very high resolution radiometer vegetation index to study atmosphere-biosphere exchange of CO2, J. Geophys. Res. 92:2999-3015
59. Gates, D.M.(1980) Biophysical ecology. Springer-verlag, New York.
60. Gens R., Genderen J.L.V., 1996, SAR interferometry—issues, techniques, applications. Int. J. Remote sensing, 17(10:1803-1835
61. Gilabert M A, et al. Analyses of spectral-biophysical relationships for a corn canopy. Remote Sensing Environment. 1996.55:11-20.
62. Giri C, Shrestha S. 1996, Land cover mapping and monitoring from NAA AVHRR data in Bangladesh. Int. J. Remote sensing, 17(14): 2749-2759
63. Goetz S.J., R.N. Halthore, F.G Hall, and B.L Markham. Surface temperature retrieval in a temperate grassland with multiresolution sensors [J]. Journal of Geophysical RTesearch, 1995,100(D12):25739-25410.
64. Goward, S. N. , Waring, R. H., Dye. D.G and Yang, J., Ecological remote sensing at OTTER: satelite macroscale observations, Ecol. Appl., 1994,42:322-343.
65. Goward, S. N. and Huemmrich, K. F., Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model, Remote Sensing Environ., 1992, 39:119-140
66. Goward, S.N.& Hope, A.S.(1989) Evaportranspiration from combined reflected solar and emitted terrestrial radiation: preliminary FIFE results from AVHRR data. Adv. Space Res. 9, (7)239-(7)249
67. Goward, S.N., Cruickshanks, GD. & Hope, A.S.(1985) Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape. Remote sensing Environment, 18, 137-148
68. Gower S.T., Kucharik, and J.M. Norman. Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems[J]. Remote of Environment, 1999, 70:79-51.
69. Grant C. D., Longeragan W. A. et al. The effect of burning, soil scarification and seeding on the understorey composition of 12 year-old rehabilitation bauxite mines in West Australia. Australian Forestry. 1997. 60(l):16~23.
70. Gurney K.R., R.M. Law, A.S. Denning, P.J. Rayner, D. Baker, P. Bousquest, L. Bruhwiler, Y-H Chen, et al. Towards robust regional estimates of CO2k sources and sinks using atmosphere transport models. Nature. 2002, (415):1626-630.
71. Hame T.,Salli A.,Andersson K.,and Lohi A, A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data, Int.J,Remote Sening, 1997, 18 (15):3211-3243.
72. Hanan N.P.,Prince S.D.,and Holben R.N., Atmospheric correction of AVHRR data for biophysical remote sensing of the Sahel, Remote Sens.Environ., 1995, 51:306-316.
73. Hansen M.C., R.S. DeFries,J.R.G .Townshend, R. Sohlberg, C.Dimiceli, M.Carroll. Towards and operational MODIS countinuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment. 2002, 83(1-2):303-319.
74. Harris, A.R. & Mason, I.M.(1992) An extension of the split window technique giving improved atmospheric correction and total water vapor. Int. J. Remote sensing, 13, 881-892
75. Hatfield, J.L., Asrar, G & Kanemasu, E.F. (1984) Intercepted photosynthetically active radiation in wheat canopies estimated by spectral reflectance. Remote sensing, environment, 14, 65-75
76. Haxeltine A, Prentice I C. BIOME3: an equilibrium terrestrrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochemical Cycles, 1996, 10(4): 693—709
77. Henderson-Sellers A, McGuffie K. Global climate models and "dynamic" vegetation changes. Global Change Biol, 1995, 1:63—76
78. Hobbs T J. The using of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. International Journal of Remote Sensing. 1995.16(7): 1289-1302.
79. Hoeksema Robert J., Peter K. Kitanidis. Prediction of transmissivities, heads, and seepage velocities using mathematical modeling and geostatistics. Advances in Water Resources. 1989, 12(2) 90-102.
80. Holdridge L R. Determination of world formations from simple climaic data. Science, 1947, 105: 367～368
81. Hollinger D. Y., S. M. Goltz, E. A. Davidson, J. T. Lee, K. Tu, and H. T. Valentine. Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest. Global Change Biology. 1999, 6: 891-902
82. Houghton J T, Jenkins G T, Ephraums J J. Climate change: the IPCC scientific assessment. Cambridge: Cambridge University Press, 1990
83. Huete A R, Tucker C J. Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. International Journal of Remote Sensing. 1991. 12(6): 1223～1242.
84. Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing Environment. 1988. 25: 295～309.
85. Hunt E. R., Jr., S. C., Nemani R., Keeling C. D., Otto RD., and Running S. W. Global net carbon exchange and intr-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmosiheric transport model. Global Biogeochem. Cycles. 1996, 10: 431-456
86. Hunt, E. R., Relationship between woody biomass and PAR cofiversion efficiency for estimating net primary production from NDVI, Int. J. Remote Sensing, 1994, 15: 1725-1730
87. Imhoff M. L., 1995, Radar backscatter and biomass saturation: ramifications for global biomass inventory. IEEE Transactions on geoscience and remote Sensing, 32(2): 511-518
88. Jackson R D; Huete A R. Interpreting vegetation indices. Preventive Veterinary Medicine. 1991. 11: 185～200.
89. Jakubauskas M. E., 1996, Thematic Mapper characterization of lodgepole pine serial stages in Yellowstone National Park, USA, remote sensing, environment, 56: 118-132
90. Jeremy Grist et al, On the use of NDVI for estimating rainfall fields in the Karahari of Botswana, Journal of Arid Environment, 1994, 35: 105～214.
91. Justice, C. O., Eck, T., Tanre. D. & Holben, B. N. (1991) The effect of water vapor on the normalized difference vegetation index derived for the Sahelian region from NOAA AVHRR data. Int. J. Remote sensing, 12, 1165-1188
92. Kai-Wei Juanga Yue-Shin Chen, Dar-Yuan Lee. Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environmental Pollution. 2004, 127: 229-238.
93. Kaufman Y. J., and Tanre D., Atmospherically resistant vegetation index(ARVI) for EOS-MODEIs, IEEE Transactions on Geoscience and Remote Sensing. 1992, 30(2): 261-270
94. Kellomaki S. and K. -Y. Wang. Short-term environmental controls on carbon dioxide flux in a boreal coniferous forest: model computation compared with measurements by eddy covariance. Ecological Modelling. 2000, 128: 63-88.
95. Kleespies, T. J. & McMillin, L. M. (199) Retrieval of precipitable water from observations on split window over varying surface temperatures. J. Appl. Met. 29, 851-862
96. Knyazikhin Y., J. V. Martonchik, D. J. and N. Gobron. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data. Journal of Geophysical Research. 1998. 103(D24): 32239-32256.
97. Kumar, M., and Monteith, J. L. (1981), remote sensing of Crop growth, in plants and the daylight spectrum (H. Smith, Ed.), Academic, London, pp. 133-144
98. Kurosu T., and Fujita M. 1997, The identification of rice fields using multi-temporal ERS-I C band SAR data. Int. J. Remote sensing, 18(14): 2953-2963
99. Kurz W. A., M. J. Apps. A 70-year. retrospective analysis of carobn fluxes in the Canadian forest sector. Ecol. Appl. 1999, (9): 526-547.
100. Kyriakidis P. C. and J. L. Dungan. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological-modedl predictions. Environmental and Ecological statistics. 2001, 8: 311-330.
101. Lachowski H., et al. Faster, better data for burned watersheds needing emergency rehab. Journal Forestry. 1997. 4-8.
102. Lafont S, Kergoat L, Dedieu G, et al. Spatial and temporal variability of land CO_2 fluxes estimated with remote sensing and analysis data over western Eurasia. Tellus. 2002, 54B(In press).
103. Law B. E., M. Williams, P. M. Anthoni, D. D. Baldocchi and M. H. Unsworth. Measuring and modeling seasonal variation of carbon dioxide and water vapour exchange of a Pinus ponderosa forest Subject to soil water deficit. Glboal Changc Biology. 2000, (6): 613-630.
104. Lawerence R L, Ripple W J. Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St. Helens, Washington. Remote Sensing Environment. 1998. 64:91-102.
105. Li, X. & Strahler. A.H.(1985) Geometric-optical modeling of a conifer forest canopy. IEEE Trans. Geoscience, remote sensing, Ge-23, 705-721
106. Liu J., and Buheaosier, 2000, Study on spatioal-temporal feature of modern land-use change in China: using remote sensing technique. Quaternary Sciences, 3, 229-239 in Chinese with English abstract.
107. Lloyd C R. The measurement and modeling of the carbon dioxide exchange at a high Arctis site in Svalbard. Global Change Biology, 2001, 7:405-426.
108. Luo, Tinxiang, Neilson, Ronald P., Tian, Hanqin, Vo Ro smarty, Charles J., Zhu, Huazhong & Liu, Shirong. A mode! for seasonality and distribution of leaf area index of forests and its application to China. Journal of Vegetation Science. 2002, 13:817-830.
109. Malo A R. Et al, A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index, Journal of Arid Environment 1990,19: 1-24.
110. Mao Zi Jun. Summary of estimation methods and research advances of the carbon balance of forest ecosystems. Acta Phytoecologica Sinica. 毛子军,森林生态系统碳平衡估测方法及其研究进展。植物生态学报. 2002, 26 (6): 731-738.
111. Markkanen T, U. Rannik, P. Keronen, T. Suni and T. Vesala. Eddy covariance fluxes over a boreal Scots pine forest. Boreal Envionment Reasearch. 2001, 6:65-78.
112. McDocald A J, et al. Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sensing Environment. 1998.66:250-272.
113. Melillo J M. Global climate change and terrestrial net primary production. Nature, 1993, 363: 234-240
114. Melilo J. M., Houghton R. A., Kicklighter D. W. and McGuire A. D. Tropical deforestation and the global carbon budget. Annual Revies of Energy in the Environment. 1999,21:293-310.
115. Moghaddam M., Durden S., and Zebker H. 1994, Radar measurement of forested areas during OTTER. Remote sensing, environment, 47:154-166
116. Monteith, J. L., Solar radiation and productivity in tropical ecosystem, J. Appl. Ecol., 1972,9:747-766
117. Morison J. I. L., M. T. F. Piedade, E. Mu Her, S. P. Long W. J. Junk, and M. B. Jones. Very high productivity of the C4 aquatic grass Echinochloa polystachya in the Amazon flood plain confirmed by net ecosystem CO2 flux measurements. Oecologia. 2000,125:400-411.
118. Myneni R B, Dong J, Tucker C J, et al. A large carbon sink in the woody biomass of Northern forests. PNAS, 2001,98:14784-14789.
119. Myneni R. B., Keeling C. D., Tucker C. J., Asrar G, and Nemani R.R., Increased plant growth in the northern high latitudes from 1981 to 1991, Nature, 1997, 386-398
120. Myneni, R. B., R. R. Nemani, and W.W. Running, Estimation of glpobal leaf area index and absorbed PAR using radiative transfer model, IEEE Trans. Geosci. Remote Sens., 35,1380-1393,1997
121. Neilson R P, King G A, Koerper G Toeards a rule-based biome model. Land Ecol, 1992, 7(1): 27-43
122. Nemani, R., Pierce, L., Running, S., and Goward, S.N., Developing satellite-derived estimates of surface moisture status, J. Appl. Met., 1993, 32:548-557
123. Nicholson S E . the influence of soil type on the relationship between NDVI, Rainfall, and soil moisture in semiarid Botswana, I, NDVI response to rainfall, Remote Sensing Environment, 1994, 50: 107-120.
124. Nijs I, J. Roy, J. L. Salager and J. Fabreguettes. Elevated CO2 alters carbon fluxes in early successional Mediteranean ecosystems. Global Change Biology. 2000, 6:981-994.
125. Pacala S W, Hurtt G C, Baker D, et al. Consistent land-and atmosphere-based US carbon sink estimates. Science, 2001,292:2316-2320.
126. Parton W J, Scurlock J M O, Ojima D S, et al. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem Cycles, 1993, 7(4): 785~809
127. Perry C.R.,and Lantenschlager L.F., Functional equivalence of spectral vegetation indices. Remote Sens. Environ.,1984,14:1888-1898
128. Pickering N. B., Hansen J. W., Jones J. W., Wells C.W., Chan V.K., and Godwin D.C. 1994, WeatherMan: a utility for managing and generating daily weather data. Agronomy J.86:332-337
129. Pinol J., Filella I., Ogaya R., and Penuelas J.Groud -based spectroradiometric estimation of live fine fuel moisture of Mediterranean plants. Agricultural and forest meteorology. 1998, 90:173-186
130. Pinty R.B.,and Verstraete M.M., GEMI:A non-liner index to monitor global vegetation from satellite, Vegetation, 1991,101:15-20.
131. Pitman J. I., Absorption of Photosyntherically Adctive Radiation, Radiation Use Efficiency and Spectral Reflectance of Bracken [Pteridium aquilinum (L.) Kuhn] Canopies. Annals of Botany. 2000, 85(b):101-lll.
132. Pollard D, Thompson S L. Use of a land-surface-transfer scheme (LSX) in a global climate model (GENESIS): the response to doubling stomatal resistance. Global Planet Change, 1995, 10: 129-161
133. Potter C. S. Klooster S. de Carvalho C. R, et al. Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region. J. Geophys. Res., 2001, 106:10423-10446.
134. Potter, C. S., Randerson, J. T, Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A., and Klooster, S.A., Terestrial ecosystem production: a process model based on global satellite and surface data, Global Biogeochem. Cycles, 1993,7:811-841
135. Prentice I C, Cramer W, Harrison S P, et al. A global biome model based on plant physiology and dominance, soil properties and climate. J Biogeogr, 1992, 19: 117—134
136. Prentice K C. Bioclimate distribution of vegetation for general circulation model studies. J Geophys Res, 1990,95: 11811 —11830
137. Prince, S. D. 1991a. Satellite remote sensing of primary production: comparsion od results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing. 12(6): 1301-1311
138. Prince, S. D. and Goward, S. N., Global primary production: a remote sensing approach, Journal of Biogeography, 1995, 22: 815-835
139. Prince, S.D.(1991), A model of regional primary production for use with coarse-resolution satellite data, Int. J. Remote sensing, 12:1313-1330
140. Privette J. L, R. B. Myneni, Y. Knyazikhin, M. Mukelabai, G Roberts. Y. Tian, Y. Wang, S. G Leblance. Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment. 2002,83(l-2):232-243.
141. Privette J. L, Fowler C.Wick GA.,Baldwin D.,and Emery W.J., Effects of orbital drift on advanced very high resolution radiometer products normalized difference vegetation index and sea surface temperature, Remote Sens.Environ.l995,53:164-l71.
142. Purevdorj T, et al. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing. 1998.19(18): 3519-3535.
143. Qi J.,Chehbouni A.,Huttc A.R.,Herr Y.H.,And Sorooshian S., A modified soil adjusted vegetation index, Remote Sensing Environment, 1994,48:119-126.
144. Quarmby N A, et al. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing. 1993. 14(2): 199-210.
145. Raich J. W. and A. Tuf(?)kcioglu. Vegetation and soil respiration: correlations and controls. Biogeochemistry. 2000,48:71-90.
146. Raich, J.W., Rastetter, E.B., Melillo, J.M., et al.(199l), Potential net primary production in South America, Ecol. Appl. 1:399-429
147. Ranson K.J., and Sun G.Q. 1994, Northern forest classification using temporal multifrequency and multipolarimetric SAR images. Remote sensing, environment, 47: 142-153
148. Rasmussen M S .Developing simple operational, consistent NDVI-vegetation models by applying environmental and climatic information: part I. Assessment of net primary production International Journal of Remote Sensing. 1998.19(1):97-117.
149. Rasmussen M S Developing simple operational, consistent NDVI-vegetation models by applying environmental and climatic information: part II. Crop yield assessment International Journal of Remote Sensing. 1998.19(1):119—139.
150. Rastetter E. B., King A. W., Cosby B. J., et al. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Application. 1992, 2:55-70.
151. Raymond, E. and Hunt.JR., Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI, Int.J.Remote Sens. 1994,15:1725-1730
152. Reich P. B., David P. Turner, and Paul Bolstad. Modeling of Net Primary Production (NPP) at the Landscape Scale and Its Application in Validation of EOS NPP Products[J.]. Remote Sensing of Environment, 1999,70:69-81.
153. Richardson A.J.,and Wiegand C.L., Distinguishing vegetation from soil background information., Photogrammetric Engineering and Remote Sensing,1977,43:1541-1552.
154. Rodriguez Murillo J. C. Temporal variations in the carbon budget of forest ecosystems in Spain. Ecological Applications. 1997,7(2):461-469.
155. Rondeaux G,Steven M.,and Garet F., Optimization of soil-adjusted vegetation indices, Remote Sensing Environment, 1996,55:95-107.
156. Roscnzwcig C, Parry M L. Potential impact of climate change on world food supply. Nature, 1994, 367: 133-138
157. Roscnzwcig W L. Net primary productivity of terrestrial communities: prediction from climatological data[J]. Ameri Nat, 1968,102:67—74.
158. Roujcan J.L.,and Brcon F.M., Estimation PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sensing Environment, 1995,51:375-384
159. Rowntree R. Modeling fire and nutrient flux in the lake Tahoe basin. Journal Forestry. 1998. 6-11.
160. Roy D.P., Investigation of maximum Normalized Difference Vegetation Index(NAVI) and the maximum surface temperature(Ts) AVHRR compositing procedures for the extraction of NDVI and Ts over forest, Int.J.Remote Sensing, 1997,18(11): 13 83-2401.
161. Roy D.P.,Kennedy P.,and Folving S, Combination of the Normalized Difference Vegetation Index and surface temperature for regional scale European Forest cover mapping using AVHRR data, Remote Sensing Environment, 1997,18(5): 1189-1195.
162. Ruimy A., and Saugier B., 1994, Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophysical research, 97: 18515-18521
163. Ruimy A., L. Kergoat, A. Bondeau and the participants of the Potsdam NPP model intercomparison. Comparing global models of terrestrial net primary productivity(NPP): analysis of differences in light absorption and light-use efficiency. Global Change Biology. 1999,5(Suppl.):56-64.
164. Ruimy, A., Dedieu, G, and Saugier, B.(1994), Methodology for the estimation of terrestrial net primary production from remotely sensed data, J. Geophys. Res. 99:5263-6284
165. Running S W, Hunt Jr E R. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer J R, Field C, eds. Scaling Processes Between Leaf and Landscape Levels. San Diego: Academic Press, 1993. 141 —158
166. Running S. W., Coughlan J. C. A general model of forest ecosystem processes for regional application I. Hydrological balance, canopy gas exchange and primary production processes. Ecological Modeling. 1998,42:125-154.
167. Running, S.W., and Nenani R.R., 1988. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote sensing, environment, 24:347-367
168. Running, S.W., Loveland, T.R., and Pierce, L.L.(1994), A vegetation classification logic based on remote sensing for use in general biogeochemical models, Ambio 23:77-81
169. Running, S.W., Nemani R.R., Peterson D., Band L., Potts D.F., Poerce L.L., Spanner M.A., 1989, Mapping regional forest evaportranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology. 70(4): 1090-1101
170. Running, S.W., Petersson D.L., Spanner M.A., and Teuber K.B. 1986. Remote sensing of coniferous forest leaf area. Ecology. 67(1):273-276
171. Runyon, J., Waring, R. H., Goward, S.N., and Welles, J.M., Environmental limits on net primary production and light-use efficiency across the Oregon transect, Ecol. Appl., 1993,4:226-237
172. Runyon, J., Waring, R.H., Goward, S.N., and Welles, J.M. (1993), environmental limits on net primary production and light-use efficiency across the Oregon transect, Ecol. Appl. 4:226-237
173. Ryan M. G A simple method for estimating gross carbon budgest for vegetation in forest ecosystems. Tree Physiology. 1991,9:255-266.
174. S. Moulin, A. Bondeau, and R. Delecolle, Combining agricultural crop models and satellite observations: from field to regional scales, Int.J.Remote Sens. 1998,19:1021-1036
175. Sala, O.E., Biondini, M.E., and Lauenroth, W.K.(1988), Bias in estimates of primary production: an analytical solution, Ecol. Model, 44:43-55
176. Samuel N. Goward, Yongkang Xue and Kevin P. Czajkowski Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model, Remote Sensing of Environmetn Volume 79. Issuse 2-3 Pages 145-354(February 2002) Pages 225-542
177. Schimel D. S., House J. I. et al. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature. 2001, 414:169-172.
178. Schneider K. and W. Mauser. Using remote sensing data to model water, carbon and nitrogen fluxes with PROMET-V. In: M. Owe, G D'Urso, E. Zilioli eds. Remote Sensing for Agriculture, Ecosystem, and Hydrology II. Proceedings of SPIE. 2001, 4171:12-23.
179. Scurlock, J. M. O., Cramer, W, Olson, R. J., Paton, W. J., and Prince, S. D. Terrestrial NPP: Towards a consistant data set for global model evaluation. Ecol. Appl., 1999 9:913-919.
180. Sellers P J, Berry J A, Collatz G J, et al. Canopy reflectance, photosynthesis and transpiration, III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens Environ, 1992, 42: 187—216
181. Sellers P J, Mintz Y, Sud Y C, et al. A simple biosphere model (SiB) for use within general circulation models. J Atmos Sci, 1986, 43(6): 505—531
182. Sellers, P.J.(1985) Canopy reflectance, photosynthesis, and transpiration. Int. J. Remote sensing, 6, 1335-1371
183. Shabanov N., Y Knyazikhin, and R Myneni. Monitoring Green Leaf Area of Global Vegetation with the MODIS Sensor Onboard the EOS-Terra and Aqua Platforms. http.
184. Skole D., and Tucker C. 1993, Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988, Science, 260:1905-1910
185. Smith J. E. and L. S. Heath. Identifying Influences on Model Uncertainty: An Application Using a Forest Carbon Budget. Model. Environmental Management. 2001,27:253-267.
186. Smith, R.C.G & Choudhury. B.J. (1991) Analysis of normalized difference and surface temperature observations over south-eastern Australia. Int. J. Remote sensing, 12, 2021-2044
187. Soegaard H. Fluxes of carbon dioxide, water vapour and sensible heat in a boreal agricultural area of Sweden-scaled from canopy to landscape level. Agricultural and Forest Meteorology. 1999,98-99:463-478.
188. Spurr S H, Barnes B V. Forest Ecology. New York: Ronald Press, 1973
189. Stith T. Gower, et al, Direct and indirect estimation of leaf area index, FAPAR and net primary production of terrestrial ecosystems, Remote Sensing Environment, 1999,70:29-51.
190. Strahler A. Global Land Cover: Approaches to Validation. GLC2000 Meeting JRC Ispra 3,2002.
191. Tanaka H. And Nakashizuka T, 1997, Fifteen years of canopy dynamics analyzed by aerial photographs in a temperate deciduous forest, Janpan. Ecology, 78(2):612-2620
192. Tanaka K. Multi-layer model of CO2 exchange in a plang community coupled with the water budget of leaf surfaces. Ecological Modelling. 2000,(147):85-104.
193. Tanre D.,Deroo C.,Duhaut P.,et al.,Description of a computer code simulation the satellite signal in the solar spectrum:the 5S code. Int.J.Remote Sens.,1990,11:659-668.
194. Tate K. R., N. A. Scott, A. Parshotam, L. Brown, R. H. Wilde, D. J. Giltrap, N. A. Trustrum. B. Gomez and D. J. Ross. A multi-scale analysis of a terrestrial carbon budget: Is New Zealand a source or sink of carbon? Agriculture, Ecosystems And Environment. 2000,82(1-3):229-246(abstract)
195. Thomlinson J. R., P. V. Bolstad, and W. B. Cohen. Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sensing of Environment. 1999,70:16-28.
196. Tian H. Q., J. M. Melillo, D. W. Kicklighter, A. D McGuire, J. Helfrich III, B. Moore III and C. J. Vo ro smartly. Climate and biotic controls on annual carbon storage in Amazonian ecosystems. Global Ecology and Biogegraphy. 2000, 9:315-335.
197. Toan T.L., Beaudoin A., Riom J., and Guyon D. 1992, Relating forest biomass to SAR data. IEEE transactions on geoscience and remote sensing, 30(2):403-411
198. Todd S W , et al. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing. 1998. 19(3): 427-438.
199. Tucker C.J., Fung I.Y., Keeling C.D., and Gammon R.H. 1986, Relationship between atmospheric CO2 variations and a satellite derived vegetation index, Nature. 319:195-199
200. Tucker C.J.,Red and photographic infred liner combinations for monitoring vegetation. Remote sens.Environ.,1979,8:127-150
201. Turner D. P., W. B. Cohen, R. E. Kennedy, K. S. Fassnacht, and J. M. Briggs. Relationships between leaf area index and Landsat TM spcctaral vegetation indices across three temperate One sites. Remote Sensing of Environmetn. 1999,70:52:68.
202. Verhoef A. and S. J. Allen. A SVAT scheme describing energe and CO2 fluxes for multi-component vegetation: calibration and test for a Sahelian savannah. Ecological Modelling. 2000, 127:245-267.
203. Verma K S, et al. Gram yield estimation through SVI under variable soil and management conditions. International Journal of Remote Sensing. 1998. 19(13): 2469-2476.
204. Vitousek, P.M., and Walker, .R.(1989), Biological invasion by Myrica faya in Hawaii: plant demography, nitrogen fixation, ecosystem effects, Ecol. Monogr. 59:247-265
205. Waddington J. M. and N. T. Roulet. Carbon balance of a boreal patterned peatland. Global Change Biolgoy. 2000,6:87-97.
206. Wang G;uanxing, George G(?)rtner, Vivek Singh, Svetlana, Pablo Parysow, Alan Anderson. Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall-runoff erosivity R factor. Ecological Modelling. 2002,153:143-155.
207. Webb W L , The primary productivity and bio-control of forest, grassland and desert ecosystems in the US[J].Natural Resoures Study (A Special Issue on Foreign Ecological Researches), 1985.1 —31 .
208. Wcssman C. A., ct al., Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature, 1988,335:154-156.
209. Whittak(?)r, R.H., and Likens, G.E.(1975), Primary production: the biosphere and man, in primary productivity of the Biosphere (H.Lieth and R.H. Whittakcr, Eds.), Springer v(?)rlag, Berlin, Pp.305-328
210. Wign(?)ron J P, ct al. A Simple approach to monitor crop biomass from C-Band Radar data. Remote Sensing Environment. 1999. 69: 179～188.
211. Wood F B Jr. The need for systems research on global climate change. Systems Research, 1988, 5: 225～240
212. Woodward F I. Climate and Plant Distribution. Cambridge: Cambridge University Press, 1987
213. Xia Li, A two-axis vegetation index(TWVI), Int. J. Remote Sens. 1994, 15: 1447-1458.
214. Xu H., Steven M. D., and Jaggard K. W. 1996, Monitoring leaf area of sugar beet using ERS-I SAR data In. t J. Remote sensing, 17(17): 3401-3410
215. YAN H, WANG CH Y, and NIU ZH. Comparative study on temperature trend in China by using remote sensing and traditional data. Journal of Remote Sensing(In English)(遥感学报) 2002, 6(supp.): 132-138.
216. Yan X, Zhao S. Simulating the sensitivity of Changbai Mt forests to climatic change. J Environ Sci (China), 1996, 8(3): 357～370
217. Zamolodchikov D. and Dmitriv Karelin. An empirical model of carbon fluxes in Russian tundra. Global Change Biology. 2001, 7: 147-161
218. Zhou G, Zhang X. A New NPP Model. In: Ye D Z, et al, eds. China Contribution to Global Change Studies-China Global Change Report No.2, Chinese National Committee for the International Geosphere-Biosphere Programmes. Beijing: Science Press, 1995. 197～202
Uchijima Z, Seino H. Agroclimatic evaluation of net primary productivity of nature vegetation. (1)Chikugo model for evaluating primary productivity. J Agricultural Meteorology, 1985, 40: 343～352
219．布和敖斯尔，刘纪远，吴祖南．基于季相及经度特征的中国土地覆盖变化遥感研究．地理学报．1998．第53卷增刊：52～60
220．陈利军，刘高焕，励惠国．中国植被净第一性生产力遥感动态监测．遥感学报．2002，6(2)：129-135
221．方红亮，田庆久．高光谱遥感在植被监测中的研究综述．遥感技术与应用．1998．13(1)：62～69
222．方精云，陈安平．中国森林植被碳库的动态变化及其意义．2001，植物学报．2001，43(9)：967-973．
223．方精云，朴世龙，赵淑清．CO2失汇与北半球中高纬度陆地生态系统的碳汇．植物生态学报．2001，25(5)：594-602
224．冯险峰，GIS支持下的中国陆地生物量遥感动态监测研究，陕西师范大学硕士研究生学位论文．2000年6月
225．符淙斌，温刚等．我国大陆植被变化的气象卫星遥感．科学通报．1992．16：1486～1488
226．高琼，李建东，郑慧莹．碱化草地景观动态及其对气候变化的响应与多样性和空间格局的关系．植物学报，1996，38(1)：18～30
227．高志强，中国土地利用／土地覆盖时空变化及成因综合分析，中国科学院地理科学与资源研究所博士后研究报告，2000，4
228．郭亮等，用气象卫星遥感方法监测中国季风区气候敏感带蒸散量的年际变化，植物学报，1997，39(9)：841-844
229．郭志华等，GIS和RS支持下广东省植被吸收PAR的估算及其时空分布，生态学报，1999，19(3)：19-26
230．侯光良等，用筑后模型估算我国植物气候生产力，自然资源学报，1990．5(1)：60-65
231．侯学煜．中国植被地理分布的规律性。西北植物研究，1981，1：1-11
232．蒋高明，陆地生态系统净第一性内生产力对全球变化的响应，植物资源与环境，1995，4(4)：53-59
233．李迪强，孙成永，张新时．中国潜在植被生产力的分布与模拟．植物学报．1998．40(6)：560～566
234．李小文和王锦地著．1995．植被光学遥感模型与植被结构参数化[M]．北京：科学出版社，18-86．
235．刘纪远，庄大方等．基于GIS的中国东北植被综合分类研究．遥感学报．1998．2(4)：285～291
236．刘纪远主编．中国资源环境遥感宏观调查与动态研究．北京：中国科学技术出版社．1996．1-43
237．刘明亮．中国土地利用／土地覆盖变化与陆地生态系统植被碳库和生产力研究．中国科学陆军遥感应用研究所博士学位论文．北京：中国科学院遥感应用研究所．2001．
238．刘绍辉，方精云，清田信．北京山地温带森林的土壤呼吸．植物生态学报．1998，22(2)：119-126．
239．刘绍辉，方精云．土壤呼吸的影响因素及全球尺度下温度的影响．生态学报．1997，17(5)：469-476．
240．刘世荣、郭泉水、王兵，中国森林生产力对气候变化响应的预测研究，生态学报，Vol．18 No．5：478-483，1998
241．刘勇洪，牛铮，王长耀．基于MODIS数据的决策树分类方法应用与研究．遥感学报．(在审稿中)．2004a
242．罗毅，于强，欧阳竹，唐登银．SPAC系统中的水热CO2通量与光合作用的综合模型(Ⅰ)模型建立．水利学报，2001a，(2)．http://jhe.ches.org.cn/journal/slxb/200102．
243．彭少麟，侯爱敏，周国逸，气候变化对陆地生态系统第一性生产力的影响研究综述，地球科学进展，2000．Vol．15，No．6：717-722
244．朴世龙，方精云，郭庆华．1982-1999年我国植被净第一性生产力及其时空变化．北京大学学报，2001，37(4)：563-569
245．盛永伟等，利用气象卫星植被指数进行顽固植被的宏观分类，科学通报，1995，40(1)：68-71
246．史培军，宫鹏等．2000．土地利用／覆盖变化研究的方法与实践．北京：科学出版社．
247．孙睿，朱启疆，气候变化对中国陆地植被净第一性生产力影响的初步研究，遥感学报，2001，5(1)：58-61
248．孙睿，朱启疆。中国陆地植被第一性生产力及季节变化的研究．地理学报，2000，55(1)：36-55．
249．田庆久，闵祥军．植被指数研究进展．地球科学进展．1998．13(4)：327～333
250．王绍强，许琢，周成虎．土地覆被变化对陆地碳循环的影响——以黄河咕角洲河口地区为例．遥感学报．2001，5(2)：142-148．
251．王绍强，周成虎，李克让，朱松丽，黄方红．中国土壤有机碳库及空间分布特征分析．地理学报2000，55(5)：533-544．
252．温刚．利用AVHRR植被指数数据集分析中国东部季风区的物候季节特征．遥感学报．1998．2(4)：270～275
253．肖乾广等，用NOAA气象卫星的AVHRR遥感资料估算中国的净第一性生产力，植物学报，1996，38(1)：35-39
254．延昊，王长耀，牛铮，陈永华．2001．利用遥感和常规资料对比研究中国地面温度变化．气候与环境研究，6：446-455
255．延昊，王长耀，牛铮，颜春燕．2002．利用AVHRR数据研究中国地表温度变化．遥感学报，6：132-138
256．于贵瑞，牛栋，王球凤．《联合国气候变化框架公约》谈判中的焦点问题．资源科学．2001，23(6)：10-16．YU G R，NIU D，and WANG Q F．Focal issues in the negotiation of UnitedNationsFramework Convention on Climate Change．Resource Science．
257．于强，谢贤群，孙淑芬．植物光合生产力与冠层蒸散模拟研究进展．生态学报．1999，19(5)：744
258．张仁华，孙晓敏，朱治林，苏红波，陈刚．遥感区域地表植被二氧化碳通量的机理及其应用．中国科学(D辑)．2000，30(2)：215-224．[Zhang Renhua，Sun Xiaomin，Zhu Zhilin，Su Hongbo，Chen Gang．Remote sensing mechanism and application of carbon flux of regional vegetation[J]．Science in China(Serice D)，2000，30(2)：215-224．]
259．张宪洲．我国自然植被净第一性生产力的估算和分布．自然资源，1993，(1)：15～21．
260．张新时．植被的PEP(可能蒸散)指标与植被-气候分类(二)，几种主要方法与PEP程序介绍．植物生态学与地植物学报，1989，13(4)：107～207
261．张永强，刘昌明，沈彦俊，于沪宁．农田生态系统CO2通量的转换计算．应用生态学报．2001，12(5)：726-730．
262．周广胜，张新时，全球变化的中国气侯-植被分类研究，植物学报，1996，38(1)：8～17
263．周广胜，张新时，全球变化的中国自然植被的净第一性生产力研究，植物生态学报，1995，20(1)：9～17
264．朱志辉，我国植被净第一性生产力估计模型，科学通报，1993，Vol．38，No．15：1422-1426
2．巴音布鲁克草原生态研究专集，干旱区研究，1991．Vol．(8)：(增刊)．
3．蔡祖聪，沈光裕．1998．土壤质地，温度和Eh对稻田甲烷排放得影响．土壤学报，35(2)：145-153
4．曹志洪，1994．化肥-环境与农业持续发展．土壤科学与农业持续发展．南京：江苏省科技出版社，183-195．
5．曹志洪，朱永官．1995．苏南稻麦两熟制下土壤养分平衡与培肥的长期实验．土壤，27(2)：60-64
6．陈德章，王明星．1995．稻田甲烷排放的初级模式．大气科学，19(6)：733-740．
7．常庆瑞主编，土地资源学，西北农林科技大学出版社，2002．11．
8．陈灵芝，黄建辉，严昌荣．1997．中国森林生态系统养分循环．气象出版社，北京．
9．陈彤编著，新疆塔城老风口防风阻雪生态工程评价与发展战略研究，新疆人民出版社，2003．8，第八章．
10．程伯容，许广山，丁桂方，张玉华和王伟．1984．Litterfall and its nutrient content of dominant types of forest ecosystem in Changbai mountmn．森林生态系统研究，4：19～24．
11．戴万宏，农田土壤空气CO_2动态和土壤—大气界面释放的研究，西北农林科技大学，博士学位论文：1～33．
12．董鸣主编，陆地生物群落调查观测与分析，中国标准出版社，北京：1997．5．
13．董云社，彭公炳．1996．温带森林土壤排放CO_2，CH_4，N_2O时空特征．地理学报，51增刊：120-128．
14．段争虎，刘新明，屈建军．1996，中国土地沙漠化对大气CO_2含量的影响．中国沙漠．10(2)：89-93．
15．方精云，刘国华，徐崇龄．1996．中国森林植被生物量和狰生产率．生态学报，16(4)：497-508．
16．方精云主编，全球生态学气候变化与响应，高等教育出版社，北京：2000．12．
17．傅声雷，蚁伟民，丁明懋．1995．鼎湖山不同植被类型下土壤微生物养分的矿化．植物生态学报，19(3)：217—224．
18．高素华．王春乙．1994．CO_2浓度升高对冬小麦，大豆籽粒成分的影响．环境科学，15(5)：24-30．
19．高云超，朱文珊，陈文新．1993．土壤微生物生物量周转的估算．生态学杂志，12(6)：6～10．
20．韩兴国，李凌浩，黄建辉．1999．生物地球化学概论．高等教育出版社，北京．
21．郝余祥．1982．土壤微生物．科学出版社，北京，1～167．
22．侯爱新，陈冠雄，吴杰．1997．稻田CH_4和N_2O排放关系及其微生物学机理和一些影响因子．应用生态学报，8(3)：270-274．
23．荒漠生态系统研究专集，干旱区研究，1990．Vol．(7)：(增刊)．
24．荒漠生态系统研究专集，干旱区研究，1992．Vol．(9)：(增刊)．
25．黄昌勇主编．2000．土壤学．中国农业出版社，北京，1—311．
26．黄承才，葛滢，常杰，卢蓉和徐青山．1999．中亚热带东部三种主要木本群落土壤呼吸的研究．生态学报，19(3)：324—328．
27．黄建辉，陈灵芝，韩兴国．1998．辽东栎枝条分解过程中几种主要营养元素的变化．植物生态学报，22(5)：398—402．
28．黄培祐编著，干旱区免灌植被及其恢复，科学出版社，北京：2002．2．
29．黄耀，2001，关于中国陆地生态系统碳循环研究的几点思考[J]，世界科技研究与发展，21世纪青年学者论坛，66-68；
30．黄志群，廖利平，高洪，汪思龙和于小军．2000．杉木根桩分解过程及几种主要营养元素的变化．应用生态学报，11(1)：40--42．
31．贾宏涛、陈冰等，防护林立地土壤肥力演替规律初步研究，林业科技，2002．12，(6)：15-17．
32．贾宏涛、蒋平安等，新疆塔城老风口工程区防护林立地土壤化学特征，土壤，2001．12，(6)：305-308．
33．蒋平安、贾宏涛、陈冰等，新疆老风口防风阻雪生态工程的土壤改良效应评价，水土保持学报，2002．9．Vol．16(3)：32-35．
34．拉夏埃尔．李博等译．植物生理生态学．1980．科学出版社，北京，1～314．
35．李长生．2001．生物地球化学的概念与方法—DNDC模型的发展．第四纪研究，2l(2)：89-99．
36．李长生．2000．土壤碳储量减少：中国农业之隐患——中美农业生态系统碳循环对比研究．第四纪研究，20(4)：345-350．
37．李迪强，孙成永，张新时．1998．中国潜在植被生产力的分布与模式．植被学报，40(6)：560-566．
38．李江风等著，新疆年轮气候年轮水文研究，气象出版社，北京：1989．11．
39．李晶，王明新，陈德章．1998．稻田甲烷排放连续测量中采样时间的选择．中国科学院研究生院学报，15(1)：24-29．
40．李克让主编，土地利用变化和温室气体净排放与陆地生态系统碳循环，气象出版社，北京：2002．5
41．李凌浩，刘先华，陈佐忠．1998．内蒙古锡林河流域羊草草原生态系统碳素循环研究．植物学报，40(10)：955-961．
42．李学垣主编，土壤化学，高等教育出版社，北京：2001．7．
43．李银鹏，季劲钧．2001，全球陆地生态系统与大气之间碳交换的模拟研究．地理学报，56(4)：379-389．
44．李玉娥，林而达，1993．影响稻田甲烷形成和排放的因子及控制技术的研究进展冲国农业气象．14(3)：50-53．
45．廖利平，高洪，汪思龙，马越强，黄志辉和子小军．2000．外加氮源对杉木叶凋落物分解及土壤养分淋失的影响．植物生态学报，24(1)：34～39．
46．林而达等，全球气候变化对中国农业影响的模拟，中国农业科学技术出版社，北京：1997．3．
47．林开敏，马祥庆，范少辉，张小全，卓仕安和金昌雄．2000．杉木人工林林下植物的消长规律．福建林学院学报，20(3)：231--234．
48．刘国华，傅伯杰。2000，中国森林碳动态及其对地球碳平衡的贡献．生态学报，20(5)：733～740
49．刘建栋，林振山，卢其尧．1998．冬小麦光和生产潜力数值模拟．地理研究，17(1)：12～19．
50．刘绍辉，方精云．1997．土壤呼吸的影响因素及全球尺度下温度的影响．生态学报，17(5)：469～476．
51．刘允芬．1998．西藏高原农田土壤CO2排放研究初报．自然资源学报，13(2)：181～186．
52．刘允芬．1995．农业生态系统碳循环研究，自然资源学报，10(1)：1～8．
53．刘允芬．1998．中国农业系统碳汇功能，农业环境保护，17(5)：197～202．
54．卢俊培，刘其汉．1988．海南岛尖峰岭热带林凋落物研究初报，植物生态学与地植物学，12(2)：104～112．
55．罗承平，薛纪渝．1996．大气中CO_2浓度增加对我国农业生产的影响，热带地理，16(3)：191～194．
56．马志贵，王金锡．1993．大熊猫栖息环境的森林凋落物动态研究，植物生态学与地植物学学报，17(2)：55～163．
57．孟范平．1994．大气CO_2浓度增加对我国农业生产的影响．农村生态环境(学报)，10(4)：62-66．
58．潘根兴、曹建华、周运超，2000，土壤碳及其在地球表层系统碳循环中的意义[J]，第四纪研究，Vol．20，No．4：325-333．
59．潘瑞炽，董愚得编著，植物生理学，高等教育出版社，北京：1995．5(第三版)．
60．钱翌，何章起主编，普通生态学，新疆科技卫生出版社(K)，乌鲁木齐，1994．3．
61．任海，彭少麟，刘鸿先，余作岳和方代有．1998．小良热带人工混交林的凋落物及其生态效益研究．应用生态学报，9(5)：458-462．
62．任荣荣编著，中国森林地理景观概貌，中国农业出版社，北京：2002．7．
63．任泳红，曹敏，唐建维，唐勇和张建侯，1999．西双版纳季节雨林与橡胶多层林凋落物动态的比较研究，植物生态学报，23(5)：418--425．
64．上官行健，王明新，沈壬兴．1993，稻田甲烷排放规律．地球科学进展，8(5)：23-35．
65．沈宏，曹志洪．1999a．土壤活性有机碳的表征及其生态效应，生态学杂志，18(3)：32—38．
66．沈宏，曹志洪，王志明．1999b．不同农田生态系统土壤碳库管理指数的研究，自然资源学报，14(3)：206--211．
67．沈宏，曹志洪．2000．不同农田生态系统土壤碳库管理指数的研究，生态学报，(20)：663-668．
68．宋文质，王少彬，苏维翰，王智平．1996．我国农土壤的主要温室气体CO_2、CH_4和N_2O排放研究．环境科学，17(1)：85-92．
69．田大伦，康文星，文仕知等著，杉木林生态系统学，科学出版社，2003．4．
70．田中正之．1992．地球在变暖(石广玉，李昌明译)．气象出版社，北京，1～132．
71．汪杏芬，李世仪，白克智和匡廷云．1998．CO_2倍增对植物生长和土壤微生物生物量碳、氮的影响，植物学报，40(12)：1169～1172．
72．汪业茂，1999，中国森林生态系统区域碳循环研究，中国科学院自然资源综合考察委员会博士研究生学位论文．
73．王风友．1989．森林凋落物量研究综述．生态学进展，6(2)：82～89．
74．王其兵，李凌浩，刘先华．1998，内蒙古锡林河流域草原土壤有机碳纪碳素的空间异质性分析．植物生态学报，22(5)：409-414．
75．王荣．1992．海洋生物泵与全球变化．海洋科学，99(1)：18—21．
76．王荣栋，尹经章主编，作物栽培学，新疆科技卫生出版社(K)，乌鲁木齐，1997．8．
77．王绍强、周成虎，1999，中国陆地土壤有机碳库的估算[J]，地理研究，Vol．18，No．4．349-355；
78．王效科，冯宗异．2000．中国森林生态系统中植物固定大气碳的潜力．生态学杂志，19(4)：72-74．
79．王修兰，1996．全球农作物对大气CO_2及其被增的吸收量估算，气象学报，54：466-426．
80．王彦辉，Peter Rademacher，Horst Folster．1999．环境因子对挪威云杉林土壤有机质分解过程中重量和碳的气态损失影响及模型，生态学报，19(5)：641～646．
81．王周琼，李述刚，程心俊著，草炭绿化荒漠的实践与机理，科学出版社，北京：2002．2．
82．王周琼，李述刚，程心俊著，荒漠绿洲农田生态系统中养分循环，科学出版社，北京：2002．2．
83．王周琼，李述刚，程心俊著，有机无机复合与荒漠化防治，科学出版社，北京：2003．10．
84．往事供，杨德保，周玉素，巴特尔，辛春兰．1995，我国西北地区“94．4'’沙尘暴成因探讨，中国沙漠，15(4)：332-338．
85．文启凯主编，农田土壤高效持续利用与管理，新疆大学出版社，2001．12．
86．吴建国，2002，土地利用变化对土壤有机碳的影响，中国林业科学研究院森林生态研究所博士论文．
87．萧祖荫，于贵瑞，1990，耕作制度基本原理与优化设计方法，辽宁科学技术出版社，沈阳，1～257．
88．谢香方主编，北疆铁路沿线区域综合开发与整治，新疆人民出版社，乌鲁木齐：1997．9．
89．谢云．1997，中国的农业发展与全球变化．北京师范大学学报(自然科学版)，33(3)：422-426
90．徐华，蔡祖聪，李小平．2000．冬作季节土地管理对水稻土CH_4排放季节变化的影响．应用生态学报，11(2)：215～218．
91．许大全，光合作用效率，上海科学技术出版社，上海．2002．6．
92．许大全，李德耀等，1987，毛竹叶片光合作用的气孔限制研究，植物生理学报，13：92～95
93．亚历山大，1983，土壤微生物导论，广西农学院农业微生物学教研组译，科学出版社，北京：1～285．
94．姚槐应，1999，红壤微生物量在土壤—黑麦草系统中的肥力意义，应用生态学报，10(6)：725～728．
95．叶笃正，陈泮勤主编，中国的全球变化预研究，地震出版社，北京：1992．11．
96．蚁伟民，丁明懋等，1994，鼎湖山黄果厚壳桂群落的凋落物及其氮素动态，植物生态学报，18(3)：228～235．
97．殷士学，1983，土壤微生物生物量及其与养分循环关系的研究进展，土壤学进展，21(4)：1～8．
98．于贵瑞，全球变化与陆地生态系统碳循环和碳蓄积，气象出版社，2003．5．
99．于强，陆佩玲，刘建栋．1999．作物光温生产力模型及南方水稻适宜生长期的数值分析，自然资源学报，14(2)：163～168．
100．袁道先，2001，地球系统的碳循环和资源环境效应，第四纪研究，Vol．21，No．3：223-230；
101．曾天勋，1986，森林养分循环，生态科学，1：69～73．
102．张家宝，史玉光等著，新疆气候变化及短期气候预测研究，气象出版社，北京：2002．7．
103．张万儒主编，中国主要造林树种土壤条件，中国科学技术出版社，北京：1998．9．
104．张小全，徐德应著，森林生长和产量生理生态模型，中国科学技术出版社，北京：2002．6．
105．张远辉，王伟强，陈立奇，2000，海洋二氧化碳的研究进展，15(5)：113～330．
106．郑循华，王明星，王跃思．1997．华东稻田CH_4和N_2O排放．大气科学，21(2)：231～237．
107．朱新广，张其德．1999．Nacl对光合作用的研究进展，物理学通报，16(4)：332-338．
108．邹琦主编，作物抗旱生理生态研究，山东科学技术出版社，济南1994．12．
109. Abrahamsen P and Hansen S.2000. DAISY: An open soil-crop-atmosphere stem model. Environmental Modeling and Software, 15: 113-330.
110. Amthor J S. 1994. Scaling CO_2-photosynthesis relationships from the leaf to the canopy. Photosynthesis Research, 39: 321～350.
111. Anderson D W, Sagger S, Bettany J R and Stewart J W B. 1981. Particle size fractions and their use in studies of soil organic matter: 1. The nature and distribution of forms of carbon, nitrogen, and sulfur. Soil Science Society of America Journal, 45: 62-72.
112. Andren O and Katter T. 1997. ICBM: the introductory carbon balances. Ecological Alpplication, 7(4): 1226-1236.
113. Arrouays D, Deslais W, Badeeau V.2001. The carbon content of topsoil and its geographical distributionin France. Soil Use and Management, 17: 7-11.
114. Atjay G L, Ketner P, 1979. Terre strait Parlay production and photomaps. In the global carbon cycle John Wiley and sons: Chichest, 129-128.
115. Aubinet M, Grelle A, Ibrom A, Rannik, Moncrieff J, Foken T, Kowalski AS, Martin P H, Berbigier P, Bernhofer C, Clement R, Elbers J, Granier A, Grunnwald T, Morgenstern K, Pilegaard K, Rebmann C, Snijders W, Valentini R and Vesala T. 2000. Esitimater of the annual net carbon and water exchage of European forests: The EUROFLUX methodology. Advances in Ecological Research, 30: 113-175.
116. Bacastow R, Maier R E. 1990. Ocean circulation model of the carbon cycle. Climate Dynamics, 4: 95-125.
117. Baker D N, J R Lambert, J M Mckinion. 1983. GOSSYM: A simulator of cotton crop growth and yield, S. C. Expt. Bull. Technical Bulletin No. 1089, S. C. Agricultural Experiment Station, December.
118. Baldochi D D, Hicks B B, Meyers T P. 1988, Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69:1331-1340.
119. Batjes N H, Sombroek W G, 1997. Possibilities for carbon sequestration in tropical and subtropical soils. Global Change Biology,3: 161-173.
120. Beare, M H, Cabrera, M L, Capbell, C A, Coleman D C.1994, Aggregate-protected and unprotected organic matter pools in conventional-and no-tillage soils. Soil Sci.SocAam.J, 58: 787-795.
121. Biederbeck V O. Janzen H H.1994, Labile soil organic matter as influenced by cropping practices in an arid enviroment. Soil Biology Biochemistry, 26:1647-1656.
122. Bohn H L.1976.Estimate of organic carbon in world soils. Sci. Sco.Am.J.40: 468-470.
123.Buringh P.1984. Original carbon in soils of the world. In: Wood well G M, (ed). The Role of Terrestrial Vegetation in the Global Carbon Cycle. John Wiley&Sons, 23:247.
124. Businger J A, 1986.Evaluation of the accuracy with which dry deposition can be measured with current micrometeorological techniques. J Clam Apply Meteorol, 25:1100-1124.
125. Buyanovsky G A, Wagner G H.1998.changing role of cultivated land in the global carbon cycle, Biol.Fertil.Soils, 27:242-245.
126. Capbell C A, McConkey B G, Zenter R P.1995.Carbon sequestration in a brown chernozem as affected by tillage and roatation. Can. J. Soil Sci.75: 449-458.
127. Capbell C A, Zentner R P. 1993.Soil organic matter as influenced by crop rotation and fertilization. Soil Sci.Am.J, 57:1034-1040.
128. Carter M R, Gregorich E G, Angers D A, Donald R G, Bolinder M A.1998.Organic C and N storage, and organic C fractions, in adjacent cultivated and forested soils of eastern Canada. Soil And Tillage Research, 47(3-4):
129. Cerling T E.1992.Use of carbon isotopes in pale sols as an indicator of the P (CO_2) of the pale atmosphere, Global Biochem.Changes, 6:312-315.
130. Christensen B T.1995.Matching measurable soil organic matter fractions with conceptual pools in simulation models of carbon turnover: revision of model structure. In: Powlson, D. S. Smith, P. and Smith, J.U. (eds), Evaluation of Soil Organic Matter Models, NATOASI Series l,38:SpeingerVerlag, Berlin, pp.143-160.
131. Cole C V, Cerri C, Minami K, Mosier A, Rosenberg N J and Sauerbeck D.1996.Agricultural options for mitigation of greenhouse gas emission, Chapter23,Climate change 1995: Impacts Adaptations and Mitigation of Climate Change,pp.745-771,Report of IPCC Working Group II,Cambridge University Press, Cambridge, U.K., p.880.
132. Houghton R A, Skole D L, Carbon. In: The Earth As Transformed by Human Action (eds Turner B L, Clark W C, Kater R Wetal). Cambridge University Press, 1990. 393-408.
133. Manabe S, Tang W and Fu L L.Century.1993 Scale effects of increased atmospheric CO_2 on the ocea- atmosphere system. Nature, 364:215-218.
134. Marl and G, Brenkert A and Olivier J G J.1999.CO_2 form fossil fuel burning: a comparison of ORNL and EDGAR estimates of national emissions. Environmental Science and Policy, In press.
135. Mathes K and Secondary TH.1985.Soil respiration during secondary sussession influences of temperature and moisture Soil Biol. Biochem, 17(2): 205- 211.
136. Mc Claugherty C A, Pastor J, Aber J D and Melillo J M.1985.Forest litter decomposition in relation to soil nitrogen dynamics and litter quality. Ecology, 66:266-275.
137. Mccarrthy J L, Brewer P G and Feldman G.1986.Global ocean flux. Oceanus, 29(4): 16-26.
138. Mchale P J, Mitchell M J and Bowles F P .1998.Soil warming in a northen hardwood forest: trace gas fluxes and leaf litter decomposition. Canadian Journal of Forest Research, 28:1365-1372.
139. Menzel A and Fabian P.1999.Growing season extended in Europe. Nature, 397:659.
140. Moina J A E, Clapp C E, shaffeer M J, Chichester F W and Larson W E .1983.NCSOIL, a model of nitrogen and carbon transformations in soil : description, calibration, and behavior. Soil Science. Society of America Journal, 47:85-91.
141. Myneni R B, keeling C D, Tucker C J, Asrar G and Nemani R R. 1997.Increased Plant growth in the northern high latitudes form 1981 to 1991.Nature, 386:698-702.
142. Nakane K, Tsubota H and yamamoto.l984.Cycling of soil carbon in a Japanese Red Pine Forest I. Before a clear-felling. Botany Managemeut, 97:39-60.
143. Niklinska M, Maryanski M and laskowski R.1999.Effect of temperature on humus respiration rate and nitrogen mineralization: implication for global climate change. Biogeochemistry, 44:239-257.
144. Oughton R A, Skole D L, Carbon. In: The Earth As Transformed by Human Action (eds Turner B L, Clark W C, Kater R Wetal). Cambridge University Press, 1990.393-408.
145.Parton W J and Rasmussen PE.1994.Long-term effects of crop management in wheat fallow: CENTURY model simulations, Soil Science Society of America Journal, 58:530-536.
146. Parton W J, Persson J and Anderson D W. 1983.Simulation of organic matter changes in Swedish soils. In : Lauenroth W K, Skogerboe G V and Flug M,(Eds.), Elsevier , Amsterboe G V and Flug M,(Eds.), Elsevier, Amsterdam,511-516.
147. Pieter P T and Peter S B.1995. 气侯变化与二氧化碳永恒.AMBIO, 24(6): 375-377.
148. Post W M , Izaurralde R C, Mann L K and Bills N.l998.Monitoring and verifying soil organic Carbon sequestration. In: Rosenberg N, Izaurralde RC, Malone EL, (Eds.), Carbon sequestration in soil .Columbus: battelle Press,41-66.
149. Post W M, Emanuel W R and Zinke P J, Stangenberger AG.1982.Soil carbon pools and life zones. Nature, 298:156-159.
150. Post W M, Emanuel W R, Zinke P Jetal. Soil carbon Pools and life zones, Nature, 1982, 298, 156-159.
151. Post W M, Peng T H, Emanuel W Retal. The Global Carbon Cycle. American Scientist, 1990, 78: 310-326.
152. Post W M, Peng T H, Emanuell W R, King A W, Date V H and DeAngelis DL.1990.The Global carbon Cycle .American Scientist,78:310-326.
153. Powlson D S and Jenkinson D S.1976.The effect of biotical treatment on metabolism in soil II. Gama irradiation, autoclaving, air-drying and fumigation. Soil Biology and Biochemistry,8:179-188.
154. Prentice I C, Farquar G D, Fasham MJ R, oulden M L, Heimann M, Jaramillo V J, Kheshgi H S, Le quere C, Scholes R J and Wallace D W R.2001.In: Houghton J T and Yihui D (Eds.), Climate Chang 2001:the scientific basis. The Intergovernmental Panel on Climate Chang (IPCC) third assessment report. Cambridge University Press, Cambridge, U K, 183-237.
155. Prentice K C and Fung IY .1990.The sensitivity of terrestrial carbon storage to climate change. Nature, 46:48-51.
156. Radke J K, Shaffer M J and Saponara J.1991.Application of the Nitrogen-Tillage-Residue-Management (NTRM) model to low-input and conventional agricultural systems. Ecological Modeling, 55:241-255.
157. Raich J W and Potter C S.1995.Global Patterns of carbon dioxide emissions form soil, Global Biogechemical Cycles, 9(1): 23-36.
158. Raich J W and Schelesinger W H.1992.The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus, 44B: 81-99.
159. Ramankutty N and Foley J A. 1999.Estimating historical changes in global land cover: Croplands form 1700 to 1992.Global Biogeochemical Cycles, 13:997-1027.
160. Ramankutty N, Foley J A.1998.Characterizin patterns of global land use: An analysis of global croplands date. Global Biogeochemical Cycles, 12:667-685.
161.Randerson J T, Thompson M V, Conway TJ, Fung I Y and Field C B.1997.The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide. Global Biogeochemical. Cycles, 11:535-560.
162. Ritchie J T, Godwin D C and Otter-Nacke S.1986.CERES-Wheat:A Simulation Model of Wheat Growth and .Department, CERES Model Description .Department of Crop and Soil Science, Michigan Sate University, Easst Lansing.
163. Rodhe H, Charlson R and CraWford E.1997.Svante Arrhenius and the Ereenhouse Effect. AMBIO, 26(1): 2-5.
164. Rojas K W, Ma L, Hanson J D and Ahuja L R. 2000.RZWQM98 Use Guide .In : Ahuja L R, RojasK W, Hanson J D, Shanffer M J and Ma L,(Eds.),Root Zone Water Resources Publication LLC, Highands Ranch, CO,327-364.
165. Sarmiento J L and Orr J C.1992.A perturation simulation of CO2 uptake in an ocean general circulation model. Journal of Geophysical, 97:3621-3645.
166. Schimel D S .1995.Terrestrial ecosystems and the carbon cycle. Global Change Biology, 1:77-91.
167. Schimel D S Brawell B H, Mckeown R, Ojima DS, Parton W J and Pulliam W. 1996.Climate and nitrogen controls on the geography and timescales of terrestrial biogeochemical cycles, 10:677-692.
168. Schmel D S, Brown V B, Hibbard K A, Lund C P and Archer S .1995.Aggretation of species properties in biogeochemical models: an experimental approach. In: Jones C G, Lawson J H J.H. Lawson, (Eds.), Ling Species and ecosystems. Chapman and Hall. New Youk, U.S.A.209-214
169. Schuze E and Heimann M.1998.Carbon and water exchange of terrestrial ecosystems. In the context of global change. Cambridge University Press, Cambridge.
170. Schuze E, Wirth C and Heimann M.2000.Managing Forest After Kyoto. Science, 289:2058-2059.
171. Semenov S M, Kounina I M and Koukhta B A.1998.An ecological analysis of anthropogenic changes in ground -level concentrations of O_3, SO_2, and CO_2 in Europe. Do lady Biological Sciences, 361:344-347.
172. Semenov S M, Kounina I M and Koukhta B A.1999.Tropospheric ozone and plant growth in Europe. Meteorology and Hydrology Publishing Center, Moscow, Russia, 208(in Russian).
173. Shaffer M J and Ma L.2001.Carbon and nitrogen dynamics in upland soils. In: Shaffer M J, Ma L and Hansed S (eds.), modeling Carbon and Nitrogen Dynamical for Soil. In: Shaffer M J, Ma L and Nitrogen Dynamics for soil Management. Lewis Publishers, Boca Raton, FL, 1-10.
174. Shaffer M J, Bartling P N S and Ascough II J C.2000a.Object-oriented simulation of integrated whole farms: GPFARM framework. Computers and Electronics in Agriculture, 28:29-49.
175. Shaffer M J, Larson WE.1987.A soil-Crop Simulation Model for nitrogen, Tillage, and Crop Residue Management. U S. Department of Agriculture, Conservation Research Report, No: 34-1:103.
176. Shaffer M J, Rojas K, DeCoursey D G and Hebson C S.2000b.Nutrient Chemsitry Processes-OMNI. In root Zone Water Quality Model. Water Resources Public Inc., Englewood, CO, chap. 5:119-144.
177. Shen S M, Pruden G and Jenkinson D S.1984.Mineralization and immobilization of nitrogen in fumigated soil and the measurement of microbial biomass nitrogen. Biology and Biochemisty, 16(5):437-444.Siegenthaler U, Sarmiento J L.1993.Atmopheric carbon dioxide and the ocean.Nature,365:119-125.
178. Siegenthaler U Oeschger H.1987. Biospheric CO_2 emissions during the past 200 yeaes reconstructed by disconsolation of ice core data. Tell us, 39B: 140-154.
179. Siegenthaler U, joos F .1992.Use of a simple model for studying oceanic tracer distributions and the global carbon cycle, Talus, 44B: 186-207.
180. Smith T M, Shugart H H, Bonan G B and Simith J B.1992.Modeling the potential response of vegetation to global Climate Change: The Ecological consequences. Academic Press, London, 93-116.
181. Sombroke W G, Nachtergaele F O, Hebel A. 1993 .Amount, dynamics and sequestering of carbon in tropical and subtropical soils. AMBIO, 22(7): 417-426.
182. Sundquist E t.1993. The global carbon dioxide budget. Science, 259:934-941.
183. Swft M J, Heal O W and Anderson J M.1979. Decomposition in terrestrial ecosystem. University of California Press, Berkeley, Calidornia, USA.
184. Tain H, MelilloJ, Kicklighter D, McGuire A D, Hellfrich J, Moore IIIB and Vorosmarty C.1998.Effect of internal climate variability on carbon storage in Amazonian ecosystems. Nature, 396:664-667.
185. Takahashi T, Goddard D and Suther land, D W.1986.Sessonal and geographic variability of carbon dioxide sink/source in the oceanic areas. Journal of Geophysical Research, 91:10 517-10 527.
186. Tans P , Fung I Y and Takahashi T.1990.Obervational constraints on the global; atmospheric carbon dioxide budget. Science, 247:1431-1438.
187. Tans P and Wallace D WR.1999.Carbon cycle research after Kyoto. Tellus, 51B: 562-571.
188. Tans P . Conway T J and Nakazawa T.1989.Latitudinal distribution of the source and sinks of atmospheric carbon dioxide derived form surface observations and an atmospheric transport model. Journal of Geophysical Resrarch, 94:5151-5173.
189. Waring R H, Schlesinger W H.1985.Forest ecosystems: concepts and management. Academic Press, New York, USA.
190. Watson R T, Rodhe H, Oeschger H and Siegenthaler U. Greenhous gases and aerosols, In: Houghton JT, Jenkins G J and Ephraums J (Eds.), Climate Change (IPPC) Scientific Assessment, Cambridge University Press, 1990,1-40.
1. Aase J K, et al. Spectral radiance estimation of leaf area and leaf phytomass of small grains and native vegetation. IEEE Transactions on Geoscience and Remote Sensing. 1986. 24(5): 685-691.
2. Achard F., Estreguil C. Forest classification of southeast Asia using NOAAAVHRR data. Remote Sensing Environment. 1995. 54: 198-208.
3. Allen R.G, L.s. . M. Smith. Crop evapotranspiration -Guidelines for computing crop water requirements -FAO Irrigation and drainage paper 56. FAO - Food and Agriculture Organization of the United Nations. Rome, 1998
4. Alward R. D. , Delting J K. , Milchunas D.G. Grassland vegetation changes and nocyurnal global warming. Science. 1999. 283:229-231.
5. Barclay, H. J .Conversion of total leaf area to projected leaf area in lodgepole pine and Douglas-fir. Tree Physiol., 1998,18:185-193.
6. Baret F., Jacquemoud S., and Hanocq J. F. The soil line concept in remote sensing. Remote Sensing Environment. 1993. 7:65-82 .
7. Bazzaz, F.A.(1979(, The physiological ecology of plant succession, Annu. Rev. Ecol. Syst. 10:351-371
8. Becker, . & Li, Z.(1990) Towards a local split window method over land surfaces. Int. J. Remote Sensing, 11.369-393
9. Begue, A.(1993), Leaf area index, intercepted photosyntheically active radiation, and spectral vegetation indices: sensitivity analysis for regularly shaped canopies, remote sens. Environment, 46:45-59
10. Benedetti R. Rossin P. On the use NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing Environment. 1993. 45:311-326
11. Black T.A., Chen J.M., Lee X., Sagar R. M. Characteristics of shortwave and longwave irradiances under a Douglas-fir forest stand. Can.J.For.Res. 1991, 12:1020-1028
12. Blackburn GA., and Milton E.J., 1997, An ecological survey of deciduous woodlands using airborne remote sensing and geographical information systems(GIS). Int. J. Remote sensing, 18(9): 1919-1935
13. Boegh E., H. Soegaard, N. Broge, C.B. Hasager, N. O. Jensen, K. Schelde, and A. Thomsen. Airborn multispectral data for quantifying leaf area indes, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment. 2002, 81:179-193.
14. Bonan G B, Pollard D, Thompson S L. Effects of boreal forest vegetation on global climate. Nature, 1992, 359:716—718
15. Box E. O Holben B.N, Kalb V. Accuracy of the AVHRR Vegetation Index as a predictor of biomass, primary productivity and net CO_2 flux. Vegetation . 1989, 80:71-79.
16. Boyd D S, et al. The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0um). International Journal of Remote Sensing. 1999. 20 (5): 1017-1023.
17. Burgess D.W.,Lewis P.,and Muller A.L., Topographic effects in AVHRR NDVI data, Remote Sens.Environ., 1995,54:223-232.
18. Cao M., Woodward F L. Dynamics responses of terrestrial ecosystem carbon cycling to global climate change. Nature. 1998,393:249-252.
19. Chameides, W.L., Kasibhatla, P.S., Yienger, J., and Levy, H., II(1994), growth of continental-scale metro-agro-plexes, regional ozone pollution and world food production, science, 264:74-77
20. Chase T M, Pielke R A, Kittel T G F, et al. Sensitivity of a general circulation model to global changes in leaf area index. J Geophys Res, 1996, 101: 7393—7408
21. Chen J M, and J Cihlar. 1996. Retrieving leaf area indes for boral conifer forests using landsat TM images. Remote Sensing of Environment. 55:153-162.
22. Chen J., sylvain G Leblance, John R. Millcr, Jim Freemantle, Sara E. Loechel, Charles L. Walthal, Kris A. Innanen, and H.Peter White, Compact Airborne Spectrongraphic imager(CASI)used for mapping biophysical paramenters of boreal forests, Journal of Geophysical Research,1999,104(D22),27945-27958.
23. Chen W, Chen J. Liu J., and Cihlar J. Approaches for reducing uncertainties in regional froest carbon balance. Global Biogeochemical Cycles.2000, 14(3):827-838.
24. Chiba Y. Simulation of CO_2 budget and ecological implications of sugi (Cryptomeria japonica) man-made forests in Japan. Ecological Modeling. 1998,111:269-281.
25. Chong D.L.S., Mougin E., and Gastellu-etchegorry J.P. 1993, Relating the global vegetation index to net primary productivity and actual evapotranspiration over Africa. Int. J. Remote Sensing, 14(8): 1517-1546
26. Choudhury B.J. A sensitivity analysis of the radiation use efficiency use efficiency for gross photosyntheiis and net carbon accumulation by wheat. Agriculture and Forestry Meteorology. 2000, 101:217-234.
27. Christopher B. Field, et al, Global Net Primary Production: Combining Ecology and Remote Sensing, Remote Sensing Environment, 1995,51:74-88
28. Cihlar J., M Heimann, and R. Olson. Terrestrial Carbon Observation: The Frascati report on in situ carbon data and information. Environment and Natural Resources Series No. 5, FAO, Rome,2002.
29. Cohen W.B and Justice C.O. Validating MODIS terrestrial ecology products:linking in situ and satellite measurements. Remote Sensing of Environmet. 1999, 70:1-3.
30. Collatz G.J., Ball J.T., Crivet C, Berry J.A. Physiological and environmental regulation boundary layer. Agriculture and Forest meteorology, 1991, 54:107-136.
31. Conway, J. 1997, Evaluating ERS-1 SAR data for the discrimination of tropical forest from other tropical vegetation types in Papua New Guinea. Int. J. Remote sensing, 18(14):2967-2984
32. Cramer and the participants of the "Postsdam '95'"NPP model intercomparison workshop. Net primary productivity model intercomparison activity (NPP). IGBP?GAIM repot series report #5, 1995.
33. Cusack .G S, et al. Calibrating airborne vegetation data for hydrological applications under dry conditions. International Journal of Remote Sensing. 1999. 20 (11): 2221 -2233.
34. DeFires, R., Hansen, M., and Sohlberg, R., 1998, Global land cover classifications at 8km spatial resolution: The use of training data derived from landsat imagery in decision tree classifiers, International Journal of Remote Sensing; 19(16):3141-3168.
35. Denning A.S., Collatz G.J., Zhang C, et al. Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model. Part 1: Surface carbon fluxes. Tellus. 1996.48B:521-542.
36. Dickinson R E, Henderson-Sellers A, Kennedy P J, et al. Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR CCM. NCAR/TN-275-STR, Boulder: National Center for Atmospheric Research, 1986
37. Dickinson R E, Henderson-Sellers A. Modeling tropical deforestation: a study of GCM land-surface parameterization. Q J R Meteorol Soc, 1988, 114: 439—462
38. Dye D.G, and Goward S.N. 1993, Photosynthetically active radiation absorbed by global land vegetation in August 1984, Int. J. Remote sensing, 18:3361-3364
39. Dye, D.G & Shibasaki, R.(1995) Intercomparison of global PAR data sets. Geophys. Res. Lett. 22, 2013-2016
40. E.Raymond Hunt,JR., Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDV(?), Int.J.Remote Sens. 1994,15:1725-1730.
41. Eck T.F., and Dye D.G, 1991, Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance. Remote sensing, environment, 38:135-146
42. Englin J., Boxall P. C, Chakraborty K. et al. Valuing the impacts of forest fires on backcountry forest recreation. Forest Science. 1996. 42(4):450-455.
43. Euskirchen E.S. Carbon fluxes in managed Great Lakes forested ecosystems: an empirical and model-based approach. Propsal for Ph.D.Research.Michigan Technological University.2001.(communications).
44. Fan S., M. Gloor, J. Mahlman,S.Pacala,J.Sarmiento, T. Takahashi, and P. Tans. A large terrestrial carbon sink in North America implied by atmospheric and oceanic carbon dioxide data and models. Science. 1998,(282):442-446.
45. Fang C. and J.B. Moncrieff. The dependence of soil CO2 efflux on temperature. Soil Biology and biochemistry. 2001,33:155-165.
46. Fang Zongyi, Xiao Qianguang et al. Monitoring of some surface properties in China by NOAA AVHRR. Edited by Ye Duzheng, Lin Hai et al. China Contribution to Global Change Studies. 146-151. 1995. Science Press, Beijing, China.
47. Field C.B., Behrenfeld M.J., Randerson J.T., and Falkowski, 1908, Primary production of the biosphere: integrating terrestrial and oceanic components. Science, 281:237-240
48. Field C.B., Randerson J.T., Malmstrom C.M., 1995. Global net primary production: combining ecology and remote sensing. Remote Sensing of Environmet, 51,1,74-88.
49. Foley J A, Prentice I C, Ramankutty N, et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem Cycles, 1996, 10(4): 603—628
50. Foley J A. Feedbacks between climate and boreal forests during the Holocene epoch. Nature, 1994, 371: 52—54
51. Franklin S E. Modeling forest net primary productivity with reduced uncertainty by remote sensing of cover typer and leaf area index. In Hunsaker C T, Goodchild M F, Frield M A, and Case T J ed Spatial uncertainty in ecology: Implications for remote sensing and GIS application. Nww Youk, Berlin, Hcidelbcrg:Springer-Verlag. 2001.284-307
52. Franklin S.E., Lavigne M.B., Deuling M.J., Wulder M.A., and Hunt E.R. 1997, Estimate of leaf area index using remote sensing and GIS data for modeling net primary production. Int. J. Remote sensing, 18(16):3459-3471
53. Friedl M.A., D.K. Mclver, J.C.F. Hodges, X.Y. Zhang, D. Muchoney, A.H. Strahler, C.E. Woodcock, S.Gopal, A Schneider, A. Cooper, A. Baccini, F. Gao and C. Schaaf. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environmetn.2002,83(1-2):287-302
54. Friedlingstein, P., Delire, C, Muller, J.F., and Gerard, J., (1992), The climate induced variation of the continues biosphere: a model simulation of the last glacial maximum geophys. Res. Lett. 19:897-900
55. Frield M A, McGwire K C, Mclver D K. An overvies of uncertainty in optical remotely sensed data for ecological applications. In Hunsaker C T, Goodchild M F, Friedl M A, and Case T J ed. Spatial uncertainty in ecology: implications for remote sensing and GIS application. Nww Youk, Brlin, Heideiberg:Springer-Verlag.2001.25 8-283
56. Friend A.D. Modelling canopy CO2 flux: are 'big-leaf simplifications justified? Global Ecology and Biogeography. 2001, (10):603-619.
57. Fuller D O. Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993. International Journal of Remote Sensing. 1998. 19(10): 2013-2018.
58. Fung, I.Y., Tucker, C.J., and Prentice, K.C.(1987), application of advanced very high resolution radiometer vegetation index to study atmosphere-biosphere exchange of CO2, J. Geophys. Res. 92:2999-3015
59. Gates, D.M.(1980) Biophysical ecology. Springer-verlag, New York.
60. Gens R., Genderen J.L.V., 1996, SAR interferometry—issues, techniques, applications. Int. J. Remote sensing, 17(10:1803-1835
61. Gilabert M A, et al. Analyses of spectral-biophysical relationships for a corn canopy. Remote Sensing Environment. 1996.55:11-20.
62. Giri C, Shrestha S. 1996, Land cover mapping and monitoring from NAA AVHRR data in Bangladesh. Int. J. Remote sensing, 17(14): 2749-2759
63. Goetz S.J., R.N. Halthore, F.G Hall, and B.L Markham. Surface temperature retrieval in a temperate grassland with multiresolution sensors [J]. Journal of Geophysical RTesearch, 1995,100(D12):25739-25410.
64. Goward, S. N. , Waring, R. H., Dye. D.G and Yang, J., Ecological remote sensing at OTTER: satelite macroscale observations, Ecol. Appl., 1994,42:322-343.
65. Goward, S. N. and Huemmrich, K. F., Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model, Remote Sensing Environ., 1992, 39:119-140
66. Goward, S.N.& Hope, A.S.(1989) Evaportranspiration from combined reflected solar and emitted terrestrial radiation: preliminary FIFE results from AVHRR data. Adv. Space Res. 9, (7)239-(7)249
67. Goward, S.N., Cruickshanks, GD. & Hope, A.S.(1985) Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape. Remote sensing Environment, 18, 137-148
68. Gower S.T., Kucharik, and J.M. Norman. Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems[J]. Remote of Environment, 1999, 70:79-51.
69. Grant C. D., Longeragan W. A. et al. The effect of burning, soil scarification and seeding on the understorey composition of 12 year-old rehabilitation bauxite mines in West Australia. Australian Forestry. 1997. 60(l):16~23.
70. Gurney K.R., R.M. Law, A.S. Denning, P.J. Rayner, D. Baker, P. Bousquest, L. Bruhwiler, Y-H Chen, et al. Towards robust regional estimates of CO2k sources and sinks using atmosphere transport models. Nature. 2002, (415):1626-630.
71. Hame T.,Salli A.,Andersson K.,and Lohi A, A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data, Int.J,Remote Sening, 1997, 18 (15):3211-3243.
72. Hanan N.P.,Prince S.D.,and Holben R.N., Atmospheric correction of AVHRR data for biophysical remote sensing of the Sahel, Remote Sens.Environ., 1995, 51:306-316.
73. Hansen M.C., R.S. DeFries,J.R.G .Townshend, R. Sohlberg, C.Dimiceli, M.Carroll. Towards and operational MODIS countinuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment. 2002, 83(1-2):303-319.
74. Harris, A.R. & Mason, I.M.(1992) An extension of the split window technique giving improved atmospheric correction and total water vapor. Int. J. Remote sensing, 13, 881-892
75. Hatfield, J.L., Asrar, G & Kanemasu, E.F. (1984) Intercepted photosynthetically active radiation in wheat canopies estimated by spectral reflectance. Remote sensing, environment, 14, 65-75
76. Haxeltine A, Prentice I C. BIOME3: an equilibrium terrestrrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochemical Cycles, 1996, 10(4): 693—709
77. Henderson-Sellers A, McGuffie K. Global climate models and "dynamic" vegetation changes. Global Change Biol, 1995, 1:63—76
78. Hobbs T J. The using of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. International Journal of Remote Sensing. 1995.16(7): 1289-1302.
79. Hoeksema Robert J., Peter K. Kitanidis. Prediction of transmissivities, heads, and seepage velocities using mathematical modeling and geostatistics. Advances in Water Resources. 1989, 12(2) 90-102.
80. Holdridge L R. Determination of world formations from simple climaic data. Science, 1947, 105: 367～368
81. Hollinger D. Y., S. M. Goltz, E. A. Davidson, J. T. Lee, K. Tu, and H. T. Valentine. Seasonal patterns and environmental control of carbon dioxide and water vapour exchange in an ecotonal boreal forest. Global Change Biology. 1999, 6: 891-902
82. Houghton J T, Jenkins G T, Ephraums J J. Climate change: the IPCC scientific assessment. Cambridge: Cambridge University Press, 1990
83. Huete A R, Tucker C J. Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. International Journal of Remote Sensing. 1991. 12(6): 1223～1242.
84. Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing Environment. 1988. 25: 295～309.
85. Hunt E. R., Jr., S. C., Nemani R., Keeling C. D., Otto RD., and Running S. W. Global net carbon exchange and intr-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmosiheric transport model. Global Biogeochem. Cycles. 1996, 10: 431-456
86. Hunt, E. R., Relationship between woody biomass and PAR cofiversion efficiency for estimating net primary production from NDVI, Int. J. Remote Sensing, 1994, 15: 1725-1730
87. Imhoff M. L., 1995, Radar backscatter and biomass saturation: ramifications for global biomass inventory. IEEE Transactions on geoscience and remote Sensing, 32(2): 511-518
88. Jackson R D; Huete A R. Interpreting vegetation indices. Preventive Veterinary Medicine. 1991. 11: 185～200.
89. Jakubauskas M. E., 1996, Thematic Mapper characterization of lodgepole pine serial stages in Yellowstone National Park, USA, remote sensing, environment, 56: 118-132
90. Jeremy Grist et al, On the use of NDVI for estimating rainfall fields in the Karahari of Botswana, Journal of Arid Environment, 1994, 35: 105～214.
91. Justice, C. O., Eck, T., Tanre. D. & Holben, B. N. (1991) The effect of water vapor on the normalized difference vegetation index derived for the Sahelian region from NOAA AVHRR data. Int. J. Remote sensing, 12, 1165-1188
92. Kai-Wei Juanga Yue-Shin Chen, Dar-Yuan Lee. Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environmental Pollution. 2004, 127: 229-238.
93. Kaufman Y. J., and Tanre D., Atmospherically resistant vegetation index(ARVI) for EOS-MODEIs, IEEE Transactions on Geoscience and Remote Sensing. 1992, 30(2): 261-270
94. Kellomaki S. and K. -Y. Wang. Short-term environmental controls on carbon dioxide flux in a boreal coniferous forest: model computation compared with measurements by eddy covariance. Ecological Modelling. 2000, 128: 63-88.
95. Kleespies, T. J. & McMillin, L. M. (199) Retrieval of precipitable water from observations on split window over varying surface temperatures. J. Appl. Met. 29, 851-862
96. Knyazikhin Y., J. V. Martonchik, D. J. and N. Gobron. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data. Journal of Geophysical Research. 1998. 103(D24): 32239-32256.
97. Kumar, M., and Monteith, J. L. (1981), remote sensing of Crop growth, in plants and the daylight spectrum (H. Smith, Ed.), Academic, London, pp. 133-144
98. Kurosu T., and Fujita M. 1997, The identification of rice fields using multi-temporal ERS-I C band SAR data. Int. J. Remote sensing, 18(14): 2953-2963
99. Kurz W. A., M. J. Apps. A 70-year. retrospective analysis of carobn fluxes in the Canadian forest sector. Ecol. Appl. 1999, (9): 526-547.
100. Kyriakidis P. C. and J. L. Dungan. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological-modedl predictions. Environmental and Ecological statistics. 2001, 8: 311-330.
101. Lachowski H., et al. Faster, better data for burned watersheds needing emergency rehab. Journal Forestry. 1997. 4-8.
102. Lafont S, Kergoat L, Dedieu G, et al. Spatial and temporal variability of land CO_2 fluxes estimated with remote sensing and analysis data over western Eurasia. Tellus. 2002, 54B(In press).
103. Law B. E., M. Williams, P. M. Anthoni, D. D. Baldocchi and M. H. Unsworth. Measuring and modeling seasonal variation of carbon dioxide and water vapour exchange of a Pinus ponderosa forest Subject to soil water deficit. Glboal Changc Biology. 2000, (6): 613-630.
104. Lawerence R L, Ripple W J. Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St. Helens, Washington. Remote Sensing Environment. 1998. 64:91-102.
105. Li, X. & Strahler. A.H.(1985) Geometric-optical modeling of a conifer forest canopy. IEEE Trans. Geoscience, remote sensing, Ge-23, 705-721
106. Liu J., and Buheaosier, 2000, Study on spatioal-temporal feature of modern land-use change in China: using remote sensing technique. Quaternary Sciences, 3, 229-239 in Chinese with English abstract.
107. Lloyd C R. The measurement and modeling of the carbon dioxide exchange at a high Arctis site in Svalbard. Global Change Biology, 2001, 7:405-426.
108. Luo, Tinxiang, Neilson, Ronald P., Tian, Hanqin, Vo Ro smarty, Charles J., Zhu, Huazhong & Liu, Shirong. A mode! for seasonality and distribution of leaf area index of forests and its application to China. Journal of Vegetation Science. 2002, 13:817-830.
109. Malo A R. Et al, A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index, Journal of Arid Environment 1990,19: 1-24.
110. Mao Zi Jun. Summary of estimation methods and research advances of the carbon balance of forest ecosystems. Acta Phytoecologica Sinica. 毛子军,森林生态系统碳平衡估测方法及其研究进展。植物生态学报. 2002, 26 (6): 731-738.
111. Markkanen T, U. Rannik, P. Keronen, T. Suni and T. Vesala. Eddy covariance fluxes over a boreal Scots pine forest. Boreal Envionment Reasearch. 2001, 6:65-78.
112. McDocald A J, et al. Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sensing Environment. 1998.66:250-272.
113. Melillo J M. Global climate change and terrestrial net primary production. Nature, 1993, 363: 234-240
114. Melilo J. M., Houghton R. A., Kicklighter D. W. and McGuire A. D. Tropical deforestation and the global carbon budget. Annual Revies of Energy in the Environment. 1999,21:293-310.
115. Moghaddam M., Durden S., and Zebker H. 1994, Radar measurement of forested areas during OTTER. Remote sensing, environment, 47:154-166
116. Monteith, J. L., Solar radiation and productivity in tropical ecosystem, J. Appl. Ecol., 1972,9:747-766
117. Morison J. I. L., M. T. F. Piedade, E. Mu Her, S. P. Long W. J. Junk, and M. B. Jones. Very high productivity of the C4 aquatic grass Echinochloa polystachya in the Amazon flood plain confirmed by net ecosystem CO2 flux measurements. Oecologia. 2000,125:400-411.
118. Myneni R B, Dong J, Tucker C J, et al. A large carbon sink in the woody biomass of Northern forests. PNAS, 2001,98:14784-14789.
119. Myneni R. B., Keeling C. D., Tucker C. J., Asrar G, and Nemani R.R., Increased plant growth in the northern high latitudes from 1981 to 1991, Nature, 1997, 386-398
120. Myneni, R. B., R. R. Nemani, and W.W. Running, Estimation of glpobal leaf area index and absorbed PAR using radiative transfer model, IEEE Trans. Geosci. Remote Sens., 35,1380-1393,1997
121. Neilson R P, King G A, Koerper G Toeards a rule-based biome model. Land Ecol, 1992, 7(1): 27-43
122. Nemani, R., Pierce, L., Running, S., and Goward, S.N., Developing satellite-derived estimates of surface moisture status, J. Appl. Met., 1993, 32:548-557
123. Nicholson S E . the influence of soil type on the relationship between NDVI, Rainfall, and soil moisture in semiarid Botswana, I, NDVI response to rainfall, Remote Sensing Environment, 1994, 50: 107-120.
124. Nijs I, J. Roy, J. L. Salager and J. Fabreguettes. Elevated CO2 alters carbon fluxes in early successional Mediteranean ecosystems. Global Change Biology. 2000, 6:981-994.
125. Pacala S W, Hurtt G C, Baker D, et al. Consistent land-and atmosphere-based US carbon sink estimates. Science, 2001,292:2316-2320.
126. Parton W J, Scurlock J M O, Ojima D S, et al. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem Cycles, 1993, 7(4): 785~809
127. Perry C.R.,and Lantenschlager L.F., Functional equivalence of spectral vegetation indices. Remote Sens. Environ.,1984,14:1888-1898
128. Pickering N. B., Hansen J. W., Jones J. W., Wells C.W., Chan V.K., and Godwin D.C. 1994, WeatherMan: a utility for managing and generating daily weather data. Agronomy J.86:332-337
129. Pinol J., Filella I., Ogaya R., and Penuelas J.Groud -based spectroradiometric estimation of live fine fuel moisture of Mediterranean plants. Agricultural and forest meteorology. 1998, 90:173-186
130. Pinty R.B.,and Verstraete M.M., GEMI:A non-liner index to monitor global vegetation from satellite, Vegetation, 1991,101:15-20.
131. Pitman J. I., Absorption of Photosyntherically Adctive Radiation, Radiation Use Efficiency and Spectral Reflectance of Bracken [Pteridium aquilinum (L.) Kuhn] Canopies. Annals of Botany. 2000, 85(b):101-lll.
132. Pollard D, Thompson S L. Use of a land-surface-transfer scheme (LSX) in a global climate model (GENESIS): the response to doubling stomatal resistance. Global Planet Change, 1995, 10: 129-161
133. Potter C. S. Klooster S. de Carvalho C. R, et al. Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region. J. Geophys. Res., 2001, 106:10423-10446.
134. Potter, C. S., Randerson, J. T, Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A., and Klooster, S.A., Terestrial ecosystem production: a process model based on global satellite and surface data, Global Biogeochem. Cycles, 1993,7:811-841
135. Prentice I C, Cramer W, Harrison S P, et al. A global biome model based on plant physiology and dominance, soil properties and climate. J Biogeogr, 1992, 19: 117—134
136. Prentice K C. Bioclimate distribution of vegetation for general circulation model studies. J Geophys Res, 1990,95: 11811 —11830
137. Prince, S. D. 1991a. Satellite remote sensing of primary production: comparsion od results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing. 12(6): 1301-1311
138. Prince, S. D. and Goward, S. N., Global primary production: a remote sensing approach, Journal of Biogeography, 1995, 22: 815-835
139. Prince, S.D.(1991), A model of regional primary production for use with coarse-resolution satellite data, Int. J. Remote sensing, 12:1313-1330
140. Privette J. L, R. B. Myneni, Y. Knyazikhin, M. Mukelabai, G Roberts. Y. Tian, Y. Wang, S. G Leblance. Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment. 2002,83(l-2):232-243.
141. Privette J. L, Fowler C.Wick GA.,Baldwin D.,and Emery W.J., Effects of orbital drift on advanced very high resolution radiometer products normalized difference vegetation index and sea surface temperature, Remote Sens.Environ.l995,53:164-l71.
142. Purevdorj T, et al. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing. 1998.19(18): 3519-3535.
143. Qi J.,Chehbouni A.,Huttc A.R.,Herr Y.H.,And Sorooshian S., A modified soil adjusted vegetation index, Remote Sensing Environment, 1994,48:119-126.
144. Quarmby N A, et al. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing. 1993. 14(2): 199-210.
145. Raich J. W. and A. Tuf(?)kcioglu. Vegetation and soil respiration: correlations and controls. Biogeochemistry. 2000,48:71-90.
146. Raich, J.W., Rastetter, E.B., Melillo, J.M., et al.(199l), Potential net primary production in South America, Ecol. Appl. 1:399-429
147. Ranson K.J., and Sun G.Q. 1994, Northern forest classification using temporal multifrequency and multipolarimetric SAR images. Remote sensing, environment, 47: 142-153
148. Rasmussen M S .Developing simple operational, consistent NDVI-vegetation models by applying environmental and climatic information: part I. Assessment of net primary production International Journal of Remote Sensing. 1998.19(1):97-117.
149. Rasmussen M S Developing simple operational, consistent NDVI-vegetation models by applying environmental and climatic information: part II. Crop yield assessment International Journal of Remote Sensing. 1998.19(1):119—139.
150. Rastetter E. B., King A. W., Cosby B. J., et al. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Application. 1992, 2:55-70.
151. Raymond, E. and Hunt.JR., Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI, Int.J.Remote Sens. 1994,15:1725-1730
152. Reich P. B., David P. Turner, and Paul Bolstad. Modeling of Net Primary Production (NPP) at the Landscape Scale and Its Application in Validation of EOS NPP Products[J.]. Remote Sensing of Environment, 1999,70:69-81.
153. Richardson A.J.,and Wiegand C.L., Distinguishing vegetation from soil background information., Photogrammetric Engineering and Remote Sensing,1977,43:1541-1552.
154. Rodriguez Murillo J. C. Temporal variations in the carbon budget of forest ecosystems in Spain. Ecological Applications. 1997,7(2):461-469.
155. Rondeaux G,Steven M.,and Garet F., Optimization of soil-adjusted vegetation indices, Remote Sensing Environment, 1996,55:95-107.
156. Roscnzwcig C, Parry M L. Potential impact of climate change on world food supply. Nature, 1994, 367: 133-138
157. Roscnzwcig W L. Net primary productivity of terrestrial communities: prediction from climatological data[J]. Ameri Nat, 1968,102:67—74.
158. Roujcan J.L.,and Brcon F.M., Estimation PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sensing Environment, 1995,51:375-384
159. Rowntree R. Modeling fire and nutrient flux in the lake Tahoe basin. Journal Forestry. 1998. 6-11.
160. Roy D.P., Investigation of maximum Normalized Difference Vegetation Index(NAVI) and the maximum surface temperature(Ts) AVHRR compositing procedures for the extraction of NDVI and Ts over forest, Int.J.Remote Sensing, 1997,18(11): 13 83-2401.
161. Roy D.P.,Kennedy P.,and Folving S, Combination of the Normalized Difference Vegetation Index and surface temperature for regional scale European Forest cover mapping using AVHRR data, Remote Sensing Environment, 1997,18(5): 1189-1195.
162. Ruimy A., and Saugier B., 1994, Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophysical research, 97: 18515-18521
163. Ruimy A., L. Kergoat, A. Bondeau and the participants of the Potsdam NPP model intercomparison. Comparing global models of terrestrial net primary productivity(NPP): analysis of differences in light absorption and light-use efficiency. Global Change Biology. 1999,5(Suppl.):56-64.
164. Ruimy, A., Dedieu, G, and Saugier, B.(1994), Methodology for the estimation of terrestrial net primary production from remotely sensed data, J. Geophys. Res. 99:5263-6284
165. Running S W, Hunt Jr E R. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer J R, Field C, eds. Scaling Processes Between Leaf and Landscape Levels. San Diego: Academic Press, 1993. 141 —158
166. Running S. W., Coughlan J. C. A general model of forest ecosystem processes for regional application I. Hydrological balance, canopy gas exchange and primary production processes. Ecological Modeling. 1998,42:125-154.
167. Running, S.W., and Nenani R.R., 1988. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote sensing, environment, 24:347-367
168. Running, S.W., Loveland, T.R., and Pierce, L.L.(1994), A vegetation classification logic based on remote sensing for use in general biogeochemical models, Ambio 23:77-81
169. Running, S.W., Nemani R.R., Peterson D., Band L., Potts D.F., Poerce L.L., Spanner M.A., 1989, Mapping regional forest evaportranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology. 70(4): 1090-1101
170. Running, S.W., Petersson D.L., Spanner M.A., and Teuber K.B. 1986. Remote sensing of coniferous forest leaf area. Ecology. 67(1):273-276
171. Runyon, J., Waring, R. H., Goward, S.N., and Welles, J.M., Environmental limits on net primary production and light-use efficiency across the Oregon transect, Ecol. Appl., 1993,4:226-237
172. Runyon, J., Waring, R.H., Goward, S.N., and Welles, J.M. (1993), environmental limits on net primary production and light-use efficiency across the Oregon transect, Ecol. Appl. 4:226-237
173. Ryan M. G A simple method for estimating gross carbon budgest for vegetation in forest ecosystems. Tree Physiology. 1991,9:255-266.
174. S. Moulin, A. Bondeau, and R. Delecolle, Combining agricultural crop models and satellite observations: from field to regional scales, Int.J.Remote Sens. 1998,19:1021-1036
175. Sala, O.E., Biondini, M.E., and Lauenroth, W.K.(1988), Bias in estimates of primary production: an analytical solution, Ecol. Model, 44:43-55
176. Samuel N. Goward, Yongkang Xue and Kevin P. Czajkowski Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model, Remote Sensing of Environmetn Volume 79. Issuse 2-3 Pages 145-354(February 2002) Pages 225-542
177. Schimel D. S., House J. I. et al. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature. 2001, 414:169-172.
178. Schneider K. and W. Mauser. Using remote sensing data to model water, carbon and nitrogen fluxes with PROMET-V. In: M. Owe, G D'Urso, E. Zilioli eds. Remote Sensing for Agriculture, Ecosystem, and Hydrology II. Proceedings of SPIE. 2001, 4171:12-23.
179. Scurlock, J. M. O., Cramer, W, Olson, R. J., Paton, W. J., and Prince, S. D. Terrestrial NPP: Towards a consistant data set for global model evaluation. Ecol. Appl., 1999 9:913-919.
180. Sellers P J, Berry J A, Collatz G J, et al. Canopy reflectance, photosynthesis and transpiration, III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens Environ, 1992, 42: 187—216
181. Sellers P J, Mintz Y, Sud Y C, et al. A simple biosphere model (SiB) for use within general circulation models. J Atmos Sci, 1986, 43(6): 505—531
182. Sellers, P.J.(1985) Canopy reflectance, photosynthesis, and transpiration. Int. J. Remote sensing, 6, 1335-1371
183. Shabanov N., Y Knyazikhin, and R Myneni. Monitoring Green Leaf Area of Global Vegetation with the MODIS Sensor Onboard the EOS-Terra and Aqua Platforms. http.
184. Skole D., and Tucker C. 1993, Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988, Science, 260:1905-1910
185. Smith J. E. and L. S. Heath. Identifying Influences on Model Uncertainty: An Application Using a Forest Carbon Budget. Model. Environmental Management. 2001,27:253-267.
186. Smith, R.C.G & Choudhury. B.J. (1991) Analysis of normalized difference and surface temperature observations over south-eastern Australia. Int. J. Remote sensing, 12, 2021-2044
187. Soegaard H. Fluxes of carbon dioxide, water vapour and sensible heat in a boreal agricultural area of Sweden-scaled from canopy to landscape level. Agricultural and Forest Meteorology. 1999,98-99:463-478.
188. Spurr S H, Barnes B V. Forest Ecology. New York: Ronald Press, 1973
189. Stith T. Gower, et al, Direct and indirect estimation of leaf area index, FAPAR and net primary production of terrestrial ecosystems, Remote Sensing Environment, 1999,70:29-51.
190. Strahler A. Global Land Cover: Approaches to Validation. GLC2000 Meeting JRC Ispra 3,2002.
191. Tanaka H. And Nakashizuka T, 1997, Fifteen years of canopy dynamics analyzed by aerial photographs in a temperate deciduous forest, Janpan. Ecology, 78(2):612-2620
192. Tanaka K. Multi-layer model of CO2 exchange in a plang community coupled with the water budget of leaf surfaces. Ecological Modelling. 2000,(147):85-104.
193. Tanre D.,Deroo C.,Duhaut P.,et al.,Description of a computer code simulation the satellite signal in the solar spectrum:the 5S code. Int.J.Remote Sens.,1990,11:659-668.
194. Tate K. R., N. A. Scott, A. Parshotam, L. Brown, R. H. Wilde, D. J. Giltrap, N. A. Trustrum. B. Gomez and D. J. Ross. A multi-scale analysis of a terrestrial carbon budget: Is New Zealand a source or sink of carbon? Agriculture, Ecosystems And Environment. 2000,82(1-3):229-246(abstract)
195. Thomlinson J. R., P. V. Bolstad, and W. B. Cohen. Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sensing of Environment. 1999,70:16-28.
196. Tian H. Q., J. M. Melillo, D. W. Kicklighter, A. D McGuire, J. Helfrich III, B. Moore III and C. J. Vo ro smartly. Climate and biotic controls on annual carbon storage in Amazonian ecosystems. Global Ecology and Biogegraphy. 2000, 9:315-335.
197. Toan T.L., Beaudoin A., Riom J., and Guyon D. 1992, Relating forest biomass to SAR data. IEEE transactions on geoscience and remote sensing, 30(2):403-411
198. Todd S W , et al. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing. 1998. 19(3): 427-438.
199. Tucker C.J., Fung I.Y., Keeling C.D., and Gammon R.H. 1986, Relationship between atmospheric CO2 variations and a satellite derived vegetation index, Nature. 319:195-199
200. Tucker C.J.,Red and photographic infred liner combinations for monitoring vegetation. Remote sens.Environ.,1979,8:127-150
201. Turner D. P., W. B. Cohen, R. E. Kennedy, K. S. Fassnacht, and J. M. Briggs. Relationships between leaf area index and Landsat TM spcctaral vegetation indices across three temperate One sites. Remote Sensing of Environmetn. 1999,70:52:68.
202. Verhoef A. and S. J. Allen. A SVAT scheme describing energe and CO2 fluxes for multi-component vegetation: calibration and test for a Sahelian savannah. Ecological Modelling. 2000, 127:245-267.
203. Verma K S, et al. Gram yield estimation through SVI under variable soil and management conditions. International Journal of Remote Sensing. 1998. 19(13): 2469-2476.
204. Vitousek, P.M., and Walker, .R.(1989), Biological invasion by Myrica faya in Hawaii: plant demography, nitrogen fixation, ecosystem effects, Ecol. Monogr. 59:247-265
205. Waddington J. M. and N. T. Roulet. Carbon balance of a boreal patterned peatland. Global Change Biolgoy. 2000,6:87-97.
206. Wang G;uanxing, George G(?)rtner, Vivek Singh, Svetlana, Pablo Parysow, Alan Anderson. Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall-runoff erosivity R factor. Ecological Modelling. 2002,153:143-155.
207. Webb W L , The primary productivity and bio-control of forest, grassland and desert ecosystems in the US[J].Natural Resoures Study (A Special Issue on Foreign Ecological Researches), 1985.1 —31 .
208. Wcssman C. A., ct al., Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature, 1988,335:154-156.
209. Whittak(?)r, R.H., and Likens, G.E.(1975), Primary production: the biosphere and man, in primary productivity of the Biosphere (H.Lieth and R.H. Whittakcr, Eds.), Springer v(?)rlag, Berlin, Pp.305-328
210. Wign(?)ron J P, ct al. A Simple approach to monitor crop biomass from C-Band Radar data. Remote Sensing Environment. 1999. 69: 179～188.
211. Wood F B Jr. The need for systems research on global climate change. Systems Research, 1988, 5: 225～240
212. Woodward F I. Climate and Plant Distribution. Cambridge: Cambridge University Press, 1987
213. Xia Li, A two-axis vegetation index(TWVI), Int. J. Remote Sens. 1994, 15: 1447-1458.
214. Xu H., Steven M. D., and Jaggard K. W. 1996, Monitoring leaf area of sugar beet using ERS-I SAR data In. t J. Remote sensing, 17(17): 3401-3410
215. YAN H, WANG CH Y, and NIU ZH. Comparative study on temperature trend in China by using remote sensing and traditional data. Journal of Remote Sensing(In English)(遥感学报) 2002, 6(supp.): 132-138.
216. Yan X, Zhao S. Simulating the sensitivity of Changbai Mt forests to climatic change. J Environ Sci (China), 1996, 8(3): 357～370
217. Zamolodchikov D. and Dmitriv Karelin. An empirical model of carbon fluxes in Russian tundra. Global Change Biology. 2001, 7: 147-161
218. Zhou G, Zhang X. A New NPP Model. In: Ye D Z, et al, eds. China Contribution to Global Change Studies-China Global Change Report No.2, Chinese National Committee for the International Geosphere-Biosphere Programmes. Beijing: Science Press, 1995. 197～202
Uchijima Z, Seino H. Agroclimatic evaluation of net primary productivity of nature vegetation. (1)Chikugo model for evaluating primary productivity. J Agricultural Meteorology, 1985, 40: 343～352
219．布和敖斯尔，刘纪远，吴祖南．基于季相及经度特征的中国土地覆盖变化遥感研究．地理学报．1998．第53卷增刊：52～60
220．陈利军，刘高焕，励惠国．中国植被净第一性生产力遥感动态监测．遥感学报．2002，6(2)：129-135
221．方红亮，田庆久．高光谱遥感在植被监测中的研究综述．遥感技术与应用．1998．13(1)：62～69
222．方精云，陈安平．中国森林植被碳库的动态变化及其意义．2001，植物学报．2001，43(9)：967-973．
223．方精云，朴世龙，赵淑清．CO2失汇与北半球中高纬度陆地生态系统的碳汇．植物生态学报．2001，25(5)：594-602
224．冯险峰，GIS支持下的中国陆地生物量遥感动态监测研究，陕西师范大学硕士研究生学位论文．2000年6月
225．符淙斌，温刚等．我国大陆植被变化的气象卫星遥感．科学通报．1992．16：1486～1488
226．高琼，李建东，郑慧莹．碱化草地景观动态及其对气候变化的响应与多样性和空间格局的关系．植物学报，1996，38(1)：18～30
227．高志强，中国土地利用／土地覆盖时空变化及成因综合分析，中国科学院地理科学与资源研究所博士后研究报告，2000，4
228．郭亮等，用气象卫星遥感方法监测中国季风区气候敏感带蒸散量的年际变化，植物学报，1997，39(9)：841-844
229．郭志华等，GIS和RS支持下广东省植被吸收PAR的估算及其时空分布，生态学报，1999，19(3)：19-26
230．侯光良等，用筑后模型估算我国植物气候生产力，自然资源学报，1990．5(1)：60-65
231．侯学煜．中国植被地理分布的规律性。西北植物研究，1981，1：1-11
232．蒋高明，陆地生态系统净第一性内生产力对全球变化的响应，植物资源与环境，1995，4(4)：53-59
233．李迪强，孙成永，张新时．中国潜在植被生产力的分布与模拟．植物学报．1998．40(6)：560～566
234．李小文和王锦地著．1995．植被光学遥感模型与植被结构参数化[M]．北京：科学出版社，18-86．
235．刘纪远，庄大方等．基于GIS的中国东北植被综合分类研究．遥感学报．1998．2(4)：285～291
236．刘纪远主编．中国资源环境遥感宏观调查与动态研究．北京：中国科学技术出版社．1996．1-43
237．刘明亮．中国土地利用／土地覆盖变化与陆地生态系统植被碳库和生产力研究．中国科学陆军遥感应用研究所博士学位论文．北京：中国科学院遥感应用研究所．2001．
238．刘绍辉，方精云，清田信．北京山地温带森林的土壤呼吸．植物生态学报．1998，22(2)：119-126．
239．刘绍辉，方精云．土壤呼吸的影响因素及全球尺度下温度的影响．生态学报．1997，17(5)：469-476．
240．刘世荣、郭泉水、王兵，中国森林生产力对气候变化响应的预测研究，生态学报，Vol．18 No．5：478-483，1998
241．刘勇洪，牛铮，王长耀．基于MODIS数据的决策树分类方法应用与研究．遥感学报．(在审稿中)．2004a
242．罗毅，于强，欧阳竹，唐登银．SPAC系统中的水热CO2通量与光合作用的综合模型(Ⅰ)模型建立．水利学报，2001a，(2)．http://jhe.ches.org.cn/journal/slxb/200102．
243．彭少麟，侯爱敏，周国逸，气候变化对陆地生态系统第一性生产力的影响研究综述，地球科学进展，2000．Vol．15，No．6：717-722
244．朴世龙，方精云，郭庆华．1982-1999年我国植被净第一性生产力及其时空变化．北京大学学报，2001，37(4)：563-569
245．盛永伟等，利用气象卫星植被指数进行顽固植被的宏观分类，科学通报，1995，40(1)：68-71
246．史培军，宫鹏等．2000．土地利用／覆盖变化研究的方法与实践．北京：科学出版社．
247．孙睿，朱启疆，气候变化对中国陆地植被净第一性生产力影响的初步研究，遥感学报，2001，5(1)：58-61
248．孙睿，朱启疆。中国陆地植被第一性生产力及季节变化的研究．地理学报，2000，55(1)：36-55．
249．田庆久，闵祥军．植被指数研究进展．地球科学进展．1998．13(4)：327～333
250．王绍强，许琢，周成虎．土地覆被变化对陆地碳循环的影响——以黄河咕角洲河口地区为例．遥感学报．2001，5(2)：142-148．
251．王绍强，周成虎，李克让，朱松丽，黄方红．中国土壤有机碳库及空间分布特征分析．地理学报2000，55(5)：533-544．
252．温刚．利用AVHRR植被指数数据集分析中国东部季风区的物候季节特征．遥感学报．1998．2(4)：270～275
253．肖乾广等，用NOAA气象卫星的AVHRR遥感资料估算中国的净第一性生产力，植物学报，1996，38(1)：35-39
254．延昊，王长耀，牛铮，陈永华．2001．利用遥感和常规资料对比研究中国地面温度变化．气候与环境研究，6：446-455
255．延昊，王长耀，牛铮，颜春燕．2002．利用AVHRR数据研究中国地表温度变化．遥感学报，6：132-138
256．于贵瑞，牛栋，王球凤．《联合国气候变化框架公约》谈判中的焦点问题．资源科学．2001，23(6)：10-16．YU G R，NIU D，and WANG Q F．Focal issues in the negotiation of UnitedNationsFramework Convention on Climate Change．Resource Science．
257．于强，谢贤群，孙淑芬．植物光合生产力与冠层蒸散模拟研究进展．生态学报．1999，19(5)：744
258．张仁华，孙晓敏，朱治林，苏红波，陈刚．遥感区域地表植被二氧化碳通量的机理及其应用．中国科学(D辑)．2000，30(2)：215-224．[Zhang Renhua，Sun Xiaomin，Zhu Zhilin，Su Hongbo，Chen Gang．Remote sensing mechanism and application of carbon flux of regional vegetation[J]．Science in China(Serice D)，2000，30(2)：215-224．]
259．张宪洲．我国自然植被净第一性生产力的估算和分布．自然资源，1993，(1)：15～21．
260．张新时．植被的PEP(可能蒸散)指标与植被-气候分类(二)，几种主要方法与PEP程序介绍．植物生态学与地植物学报，1989，13(4)：107～207
261．张永强，刘昌明，沈彦俊，于沪宁．农田生态系统CO2通量的转换计算．应用生态学报．2001，12(5)：726-730．
262．周广胜，张新时，全球变化的中国气侯-植被分类研究，植物学报，1996，38(1)：8～17
263．周广胜，张新时，全球变化的中国自然植被的净第一性生产力研究，植物生态学报，1995，20(1)：9～17
264．朱志辉，我国植被净第一性生产力估计模型，科学通报，1993，Vol．38，No．15：1422-1426