基于统计与MODIS数据的水稻遥感估产方法研究
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摘要
中国水稻种植面积居世界第二,总产量居世界第一。水稻种植面积的变化、产量丰欠等重要农情信息历来受到各级政府和社会公众的高度重视,是国家制定粮食政策和经济计划的重要依据。及时了解、准确掌握水稻种植面积、产量等对各级政府制定农业生产和农村政策。确保我国乃至世界粮食安全具有重要的意义。国家统计局承担着全国水稻产量的调查职能。经过几十年的努力,形成了较完善的统计调查方法体系。然而,随着我国社会经济的快速发展,政府决策部门、社会公众等统计服务对象对统计的需求提高,以及统计部门面临的问题和压力,如何吸收利用先进技术。逐步改善统计调查手段、降低调查成本、提高调查精度是当前面临和亟待解决的首要问题之一。以遥感为主的3S高新技术具有全覆盖、及时、客观等特点。为改进和提升传统统计手段提供了良好的技术支撑。水稻遥感估产经过多年的发展研究,随着遥感数据的更新以及技术的发展,结合统计局统计抽样调查地块实割实测水稻标准亩产数据,研究统计与遥感相结合的水稻遥感估产,实现统计部门在水稻面积产量监测及预报等业务化运行等方面具有重要意义。
湖南省水稻种植面积及总产都居全国首位,且其地形地貌和水稻种植制度在中国水稻种植区具有典型性,因此,本研究以湖南省为研究区,利用MODIS不同数据产品(MOD09A1、MOD09GA、MYD09GA、MOD13Q1、MYD13Q1、MOD15A2、MOD17A2、MOD17A3),在完成水稻遥感估产分区及水稻面积提取的基础上,结合县级、乡镇级统计数据及统计局统计抽样调查地块实割实测数据,以及基于像元水平的MODIS GPP/NPP,研究不同数据完备条件下,水稻遥感估产方法。主要方法与结论如下:
(1)基于空间分析与两维图论树算法相结合的湖南省水稻遥感估产分区
在分析研究区(湖南省)基本特征的基础上,以水稻种植制度、稻土此、水稻单产水平、地形及县域空间位置作为分区指标,采用空间分析与两维图论树算法相结合的方法,将湖南省分为两个一级区,五个二级区。
(2)基于水稻典型发育期光谱特征与多时相MODIS数据湖南省水稻面积遥感信息提取
在对MOD09A1数据受云污染的像元进行基于MODIS数据的质量评价(Qualityassessment:QA)信息的相邻时相最大值法插补的基础上,利用水稻移栽期、抽穗期的典型光谱特征、以及相应的EVI与LSWI的关系,排除非水稻植被及其它因素的影响。根据研究区水稻种植制度特点,分别对2000-2008年早稻、晚稻及一季稻面积进行提取。并利用县级统计数据、基于高分辨率遥感影像数字化的水稻田数据、1:1万的土地利用数据,对提取结果进行验证评估。结果显示:湖南省水稻主要分布在洞庭湖流域及湖南省中部地区,相对于以前的研究,本研究的提取精度有较大的提高。
(3)基于水稻遥感估产分区、水稻面积提取、县级统计数据的省级水稻总产遥感估算
在水稻遥感估产分区、水稻面积提取的基础上,利用1:25万县级行政区划图,提取2000-2008年每年的早稻、晚稻及一季稻对应的各县各时相MOD09A1数据,选取EVI为估产参数。利用水稻生长发育期数据,选择移栽期、孕穗期、抽穗期、乳熟期及成熟期为水稻遥感估产的主要生长期,以未基于分区与基于分区两种思路,建立2000-2007年各县EVI平均值乘以第三章提取的水稻面积的结果(AEVI)与统计总产的一次线性、二次非线性及各生育期逐步回归模型。通过误差分析,选择水稻总产最优遥感拟合模型。在此基础上,利用基于2000-2007年数据得到的水稻总产最优遥感拟合模型预测2008年的省级水稻总产。结果显示:基于分区的估产模型要比未基于分区的水稻遥感估产模型要好,且生育期主要集中于孕穗期到乳熟期,说明这段时间是水稻产量形成的最关键时期。另外。二次非线性模型及逐步回归模型的结果要好。误差分析结果显示,水稻遥感估产模型的拟合结果比预测结果要好,两者省级相对误差都小于5%。
(4)基于统计局统计抽样调查地块实割实测数据、MOD13Q1与MYD13Q1EVI的省级水稻单产遥感估算
基于国家抽样县抽样地块的实测数据是统计部门计算水稻单产的基础数据,本研究根据地块的空间位置,提取各地块所在地的3×3像元网格对应的MOD13Q1与MYD13Q1相结合的EVI数据,选择移栽期、孕穗期、抽穗期、乳熟期及成熟期的EVI,建立地块实测标准亩产与EVI的一次线性、二次非线性及各生育期逐步回归模型。通过误差分析,选择水稻单产最优遥感拟合模型。在此基础上,利用上一年的水稻总产最优遥感拟合模型预测下一年的省级水稻单产。根据水稻面积提取结果,得到省级水稻总产。与统计值进行比较,结果显示,在省级水平上,基于地块的水稻单产与总产遥感拟合与预测结果相对误差都接近5%。
(5)基于乡镇级统计数据、MOD13Q1与MYD13Q1 EVI的的县级水稻总产遥感估算
选择醴陵市为研究区,分析基于乡镇级统计数据的水稻遥感估产。选择分别由Terra与Aqua卫星获取得到的MOD13Q1与MYD13Q1 EVI作为水稻估产遥感指标。其空间分辨率约为250 m。时间分辨为8 d。选择移栽期、孕穗期、抽穗期、乳熟期及成熟期的遥感数据,建立EVI乘以水稻面积的结果(AEVI)与乡镇级水稻统计总产的一次线性、二次非线性及各生育期逐步回归模型。通过误差分析,选择水稻总产最优遥感拟合模型。在此基础上,利用上一年的水稻总产最优遥感拟合模型预测下一年的醴陵市水稻总产。估产结果显示,各拟合模型相对误差都小于0.1%。预测结果误差相对于拟合结果较大,但相对误差仍然小于5%。
(6)基于像元水平的MODIS GPP/NPP数据的水稻遥感估产、及像元内水稻面积比对估产精度的影响
在前人研究的基础上,总结得到基于MODIS GPP/NPP水稻估产方法。利用MODIS 8d的、年的GPP/NPP产品及产品使用说明,估算8 d的NPP,并利用此结果与8d的GPP产品分别进行基于GPP/NPP的水稻遥感估产。借助MODIS GPP/NPP产品生成1×1km网格。研究像元内不同水稻面积比对估产结果的影响。研究结果表明:利用估算年NPP的方法、8 d的LAI和PSNnet来估算每8 d的NPP的结果比MODIS年NPP值较大。基于MODIS NPP的水稻遥感估产结果相对于统计值较小.而基于MODI GPP水稻遥感估产与统计值接近。其估产精度随着像元内水稻面积的增加而减小。像元内水稻面积超过80%,其相对误差小于20%或10%。
综上所述,本研究的主要结论如下:基于县级、乡镇级、实测地块数据、MODISGPP数据的水稻遥感估产的结果基本上都能达到95%的精度。从提高精度方面考虑,在乡镇级统计数据具备的前提下,利用基于乡镇统计数据及MOD13Q1、MYD13Q1EVI的方法是最优选择。从水稻估产业务化运行角度来考虑,在地块实测产量及空间位置数据都具备的前提下,利用基于统计局统计抽样调查地块实割实测数据及MOD13Q1、MYD13Q1 EVI的方法较好。在统计局统计抽样调查地块实割实测数据不具备的情况下,利用分区与水稻面积提取结果、以及县级统计数据的水稻遥感估产方法。而对于大面积水稻种植区,利用基于像元水平的MODIS GPP/NPP估产方法较好。
China is a large agricultural country,rice area and total output occupies second and first place in the world,respectively.All levels Chinese government and social public pay high attention to the variation of rice area and yield,which information is the very important for food economic policy decision.Therefore,it is very essential to know about the rice area and yield in time.Chinese Bureau of Statistics undertakes the nationwide rice yield investigation.After several decades'effort,a statistic and investigation system has been formed.However,with the development of social economy,government decision department,social public and some other statistic service objects have increased their demands for the statistic results,as well as other problems and pressures,it is urgent to improve the traditional investigating method using the advanced technologies.3S technologies,especially remote sensing,features the large area coverage,in-time, objectivity and so on,provide very good tools for improving the traditional statistic method.Remote sensing rice yield estimation has been studied for several years. However,with the new satellite data and technologies updated,the study of rice yield estimation approach using the integrated temporal and spatial resolution remote sensing data is very helpful to implement the rice area and yield estimation on vocational operation.
Hunan province was selected as the study area in this thesis,where the rice area and total yield occupy the first place in China,and features the typical topography and rice cropping system.After rice yield estimation division and rice detection,the purpose of this study is to build different models and estimate rice yield using different MODIS products(MOD09A1,MOD09GA,MYD09GA,MOD13Q1,MYD13Q1,MOD15A2, MOD17A2,MOD17A3) at the different level.The summary of the major chapters in this thesis as follows:
1 The study of rice yield estimation division based on spatial analysis and two-dimensional optimal tree clusters in Hunan province
Rice planting system,unit yield of rice,the ratio between rice and total land area,and the ratio between plain and total land area were selected as the main factors for rice yield estimation division.Two first-grade zoning,and five-grade zoning in Hunan province were got based on the integration of spatial analysis and two-dimensional optimal tree clusters,
2 Detection and estimation of paddy rice area based on rice typical phenology spectral characteristics and multi temporal MODIS data in Hunan province
The periods of rice transplanting and heading were selected as the key stages,the pixel of MOD09A1with clould contamination was filled by QA information and adjacent maximum fitting method.Using the relationship between EVI and LSWI during the transplanting and heading periods,non-paddy rice plant and other disturbing factors were removed,the early,late and singlerice from 2000 to 2008 were extracted.Our results were validated with finer resolution(2.5m) Satellite Pour l'Observation de la Terre 5 High Resolution Geometric(SPOT 5 HRG) data,land-use data at the scale of 1/10000 and county-level statistical rice area.The results showed that three paddy rice crop patterns could be discriminated and their spatial distribution quantified.We show the area of single crop rice have increased annually and almost doubling in extent from 2000 to 2008,but unique declines in the extent of early and late paddy rice.These results were more accurate than previous satellite-based methods.
3 Provincial rice total yield estimation based on the results of division and rice detection using remote sensing and county level statistic data
Based on rice yield estimation division and rice area detection,MOD09A1 EVI corresponding the rice area detection from 2000 to 2008 was extracted using the administrative map at the scale of 1/250 000 at the county level.Transplanting,booting, heading,milky and harvest stage were selected as the key periods for rice yield estimation,EVI multiplied by county rice area and county rice total yield were took as the independent and dependent variables respectively,and then different models were made based on division results and not using MODIS and statistics data from 2000 to 2007.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield in 2008 were obtained.The results showed that the accuracy of rice yield estimation based on division was better than without division,and almost all optimal models focused on the period from booting to milky,and the quadratic nonlinear and stepwise models are better than linear models.The estimation yield had a significant positive correlation with statistical data,and the relative errors for rice estimation and forecasting models were less than 5%.
4 Provincial rice unit yield estimation using MOD13Q1,MYD13Q1 EVI and plot level measured data
One of the very important sources for statistic data for Chinese Statistics Bureau is the measured data from sampling plot.3×3 window size MOD13Q1 and MYD13Q1 combined EVI corresponding with those plots in Hunan province were extracted. Transplanting,booting,heading,milky and harvest stage were selected as the key periods for rice yield estimation,and the different type models were built.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield were obtained.According to the results of rice detection,the provincial rice total yield was obtained.Results showed that the relative errors of forecasting rice yield and were less than 5%.
5 County rice yield estimation using MOD13Q1,MYD13Q1 EVI and township level statistic data
According to remote sensing rice yield estimation based on county level statistic data, one mountain county(Liling) with a large error was selected to further analysis the rice yield estimation using township level statistic data.MOD13Q1 and MYD13Q1 combined EVI corresponding with the rice area based on 1/10000 scale land-use data was extracted. Transplanting,booting,heading,milky and harvest stage were selected as the key periods for rice yield estimation,EVI multiplied by township rice area and township rice total yield were took as the independent and dependent variables,and then different type models were built.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield were obtained.The results show that the relative errors for rice estimation and forecasting models is less than 0.1%and5%, respectively.
6 Rice yield estimation based on MODIS GPP/NPP data at the pixel level,and the impact of the rice area percentage in one pixel on the accuracy of rice yield estimation
Based on previous researches,algorithms for rice yield estimation using MODIS GPP/NPP data were studied.8-day NPP was calculated using 8-day LAI,PSNnet data and MODIS17 user's guide,and then rice yield estimation was performed using 8-day MODIS GPP/NPP and rice growth period data.We used MODIS GPP image to create 1×1km grid,and calculated the percentage of paddy rice area in each grid,and then the impact of different percentage of paddy rice area in each grid on rice yield estimation result was studied.The result showed that:there is a significant positive correlation between 8-day estimation NPP results and MODIS annual NPP product.(P＜0.01),and the rice yield estimation based on NPP data was low compared to statistic data,but the results of MODIS GPP is better.The relative errors between rice yield from estimation and statistic data decrease with the percentage of rice area in the 1×1km increasing,and they are less than 20%,even 10%when the percentage is larger than 80%
According to the above researches,the main conclusions are as follows:the relative error of the rice yield estimation based on county,township level statistic data,plot measured data,and MODIS GPP was less than 5%.However,for the sake of improving rice yield estimation accuracy,the models based on the integration of Terra and Aqua data at the township level are the best choice.Considering rice area and yield estimation on vocational operation,the methods based on the integration of Terra and Aqua data and measured data are the best choice.If the plot measured data aren't available,the rice yield estimation methods,with the support of zoning and rice area detection,and based on the county level statistic data are perfect.For the large paddy rice area,the rice yield estimation algorithms based on MODIS GPP/NPP at the level of pixel are dependable.
湖南省水稻种植面积及总产都居全国首位,且其地形地貌和水稻种植制度在中国水稻种植区具有典型性,因此,本研究以湖南省为研究区,利用MODIS不同数据产品(MOD09A1、MOD09GA、MYD09GA、MOD13Q1、MYD13Q1、MOD15A2、MOD17A2、MOD17A3),在完成水稻遥感估产分区及水稻面积提取的基础上,结合县级、乡镇级统计数据及统计局统计抽样调查地块实割实测数据,以及基于像元水平的MODIS GPP/NPP,研究不同数据完备条件下,水稻遥感估产方法。主要方法与结论如下:
(1)基于空间分析与两维图论树算法相结合的湖南省水稻遥感估产分区
在分析研究区(湖南省)基本特征的基础上,以水稻种植制度、稻土此、水稻单产水平、地形及县域空间位置作为分区指标,采用空间分析与两维图论树算法相结合的方法,将湖南省分为两个一级区,五个二级区。
(2)基于水稻典型发育期光谱特征与多时相MODIS数据湖南省水稻面积遥感信息提取
在对MOD09A1数据受云污染的像元进行基于MODIS数据的质量评价(Qualityassessment:QA)信息的相邻时相最大值法插补的基础上,利用水稻移栽期、抽穗期的典型光谱特征、以及相应的EVI与LSWI的关系,排除非水稻植被及其它因素的影响。根据研究区水稻种植制度特点,分别对2000-2008年早稻、晚稻及一季稻面积进行提取。并利用县级统计数据、基于高分辨率遥感影像数字化的水稻田数据、1:1万的土地利用数据,对提取结果进行验证评估。结果显示:湖南省水稻主要分布在洞庭湖流域及湖南省中部地区,相对于以前的研究,本研究的提取精度有较大的提高。
(3)基于水稻遥感估产分区、水稻面积提取、县级统计数据的省级水稻总产遥感估算
在水稻遥感估产分区、水稻面积提取的基础上,利用1:25万县级行政区划图,提取2000-2008年每年的早稻、晚稻及一季稻对应的各县各时相MOD09A1数据,选取EVI为估产参数。利用水稻生长发育期数据,选择移栽期、孕穗期、抽穗期、乳熟期及成熟期为水稻遥感估产的主要生长期,以未基于分区与基于分区两种思路,建立2000-2007年各县EVI平均值乘以第三章提取的水稻面积的结果(AEVI)与统计总产的一次线性、二次非线性及各生育期逐步回归模型。通过误差分析,选择水稻总产最优遥感拟合模型。在此基础上,利用基于2000-2007年数据得到的水稻总产最优遥感拟合模型预测2008年的省级水稻总产。结果显示:基于分区的估产模型要比未基于分区的水稻遥感估产模型要好,且生育期主要集中于孕穗期到乳熟期,说明这段时间是水稻产量形成的最关键时期。另外。二次非线性模型及逐步回归模型的结果要好。误差分析结果显示,水稻遥感估产模型的拟合结果比预测结果要好,两者省级相对误差都小于5%。
(4)基于统计局统计抽样调查地块实割实测数据、MOD13Q1与MYD13Q1EVI的省级水稻单产遥感估算
基于国家抽样县抽样地块的实测数据是统计部门计算水稻单产的基础数据,本研究根据地块的空间位置,提取各地块所在地的3×3像元网格对应的MOD13Q1与MYD13Q1相结合的EVI数据,选择移栽期、孕穗期、抽穗期、乳熟期及成熟期的EVI,建立地块实测标准亩产与EVI的一次线性、二次非线性及各生育期逐步回归模型。通过误差分析,选择水稻单产最优遥感拟合模型。在此基础上,利用上一年的水稻总产最优遥感拟合模型预测下一年的省级水稻单产。根据水稻面积提取结果,得到省级水稻总产。与统计值进行比较,结果显示,在省级水平上,基于地块的水稻单产与总产遥感拟合与预测结果相对误差都接近5%。
(5)基于乡镇级统计数据、MOD13Q1与MYD13Q1 EVI的的县级水稻总产遥感估算
选择醴陵市为研究区,分析基于乡镇级统计数据的水稻遥感估产。选择分别由Terra与Aqua卫星获取得到的MOD13Q1与MYD13Q1 EVI作为水稻估产遥感指标。其空间分辨率约为250 m。时间分辨为8 d。选择移栽期、孕穗期、抽穗期、乳熟期及成熟期的遥感数据,建立EVI乘以水稻面积的结果(AEVI)与乡镇级水稻统计总产的一次线性、二次非线性及各生育期逐步回归模型。通过误差分析,选择水稻总产最优遥感拟合模型。在此基础上,利用上一年的水稻总产最优遥感拟合模型预测下一年的醴陵市水稻总产。估产结果显示,各拟合模型相对误差都小于0.1%。预测结果误差相对于拟合结果较大,但相对误差仍然小于5%。
(6)基于像元水平的MODIS GPP/NPP数据的水稻遥感估产、及像元内水稻面积比对估产精度的影响
在前人研究的基础上,总结得到基于MODIS GPP/NPP水稻估产方法。利用MODIS 8d的、年的GPP/NPP产品及产品使用说明,估算8 d的NPP,并利用此结果与8d的GPP产品分别进行基于GPP/NPP的水稻遥感估产。借助MODIS GPP/NPP产品生成1×1km网格。研究像元内不同水稻面积比对估产结果的影响。研究结果表明:利用估算年NPP的方法、8 d的LAI和PSNnet来估算每8 d的NPP的结果比MODIS年NPP值较大。基于MODIS NPP的水稻遥感估产结果相对于统计值较小.而基于MODI GPP水稻遥感估产与统计值接近。其估产精度随着像元内水稻面积的增加而减小。像元内水稻面积超过80%,其相对误差小于20%或10%。
综上所述,本研究的主要结论如下:基于县级、乡镇级、实测地块数据、MODISGPP数据的水稻遥感估产的结果基本上都能达到95%的精度。从提高精度方面考虑,在乡镇级统计数据具备的前提下,利用基于乡镇统计数据及MOD13Q1、MYD13Q1EVI的方法是最优选择。从水稻估产业务化运行角度来考虑,在地块实测产量及空间位置数据都具备的前提下,利用基于统计局统计抽样调查地块实割实测数据及MOD13Q1、MYD13Q1 EVI的方法较好。在统计局统计抽样调查地块实割实测数据不具备的情况下,利用分区与水稻面积提取结果、以及县级统计数据的水稻遥感估产方法。而对于大面积水稻种植区,利用基于像元水平的MODIS GPP/NPP估产方法较好。
China is a large agricultural country,rice area and total output occupies second and first place in the world,respectively.All levels Chinese government and social public pay high attention to the variation of rice area and yield,which information is the very important for food economic policy decision.Therefore,it is very essential to know about the rice area and yield in time.Chinese Bureau of Statistics undertakes the nationwide rice yield investigation.After several decades'effort,a statistic and investigation system has been formed.However,with the development of social economy,government decision department,social public and some other statistic service objects have increased their demands for the statistic results,as well as other problems and pressures,it is urgent to improve the traditional investigating method using the advanced technologies.3S technologies,especially remote sensing,features the large area coverage,in-time, objectivity and so on,provide very good tools for improving the traditional statistic method.Remote sensing rice yield estimation has been studied for several years. However,with the new satellite data and technologies updated,the study of rice yield estimation approach using the integrated temporal and spatial resolution remote sensing data is very helpful to implement the rice area and yield estimation on vocational operation.
Hunan province was selected as the study area in this thesis,where the rice area and total yield occupy the first place in China,and features the typical topography and rice cropping system.After rice yield estimation division and rice detection,the purpose of this study is to build different models and estimate rice yield using different MODIS products(MOD09A1,MOD09GA,MYD09GA,MOD13Q1,MYD13Q1,MOD15A2, MOD17A2,MOD17A3) at the different level.The summary of the major chapters in this thesis as follows:
1 The study of rice yield estimation division based on spatial analysis and two-dimensional optimal tree clusters in Hunan province
Rice planting system,unit yield of rice,the ratio between rice and total land area,and the ratio between plain and total land area were selected as the main factors for rice yield estimation division.Two first-grade zoning,and five-grade zoning in Hunan province were got based on the integration of spatial analysis and two-dimensional optimal tree clusters,
2 Detection and estimation of paddy rice area based on rice typical phenology spectral characteristics and multi temporal MODIS data in Hunan province
The periods of rice transplanting and heading were selected as the key stages,the pixel of MOD09A1with clould contamination was filled by QA information and adjacent maximum fitting method.Using the relationship between EVI and LSWI during the transplanting and heading periods,non-paddy rice plant and other disturbing factors were removed,the early,late and singlerice from 2000 to 2008 were extracted.Our results were validated with finer resolution(2.5m) Satellite Pour l'Observation de la Terre 5 High Resolution Geometric(SPOT 5 HRG) data,land-use data at the scale of 1/10000 and county-level statistical rice area.The results showed that three paddy rice crop patterns could be discriminated and their spatial distribution quantified.We show the area of single crop rice have increased annually and almost doubling in extent from 2000 to 2008,but unique declines in the extent of early and late paddy rice.These results were more accurate than previous satellite-based methods.
3 Provincial rice total yield estimation based on the results of division and rice detection using remote sensing and county level statistic data
Based on rice yield estimation division and rice area detection,MOD09A1 EVI corresponding the rice area detection from 2000 to 2008 was extracted using the administrative map at the scale of 1/250 000 at the county level.Transplanting,booting, heading,milky and harvest stage were selected as the key periods for rice yield estimation,EVI multiplied by county rice area and county rice total yield were took as the independent and dependent variables respectively,and then different models were made based on division results and not using MODIS and statistics data from 2000 to 2007.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield in 2008 were obtained.The results showed that the accuracy of rice yield estimation based on division was better than without division,and almost all optimal models focused on the period from booting to milky,and the quadratic nonlinear and stepwise models are better than linear models.The estimation yield had a significant positive correlation with statistical data,and the relative errors for rice estimation and forecasting models were less than 5%.
4 Provincial rice unit yield estimation using MOD13Q1,MYD13Q1 EVI and plot level measured data
One of the very important sources for statistic data for Chinese Statistics Bureau is the measured data from sampling plot.3×3 window size MOD13Q1 and MYD13Q1 combined EVI corresponding with those plots in Hunan province were extracted. Transplanting,booting,heading,milky and harvest stage were selected as the key periods for rice yield estimation,and the different type models were built.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield were obtained.According to the results of rice detection,the provincial rice total yield was obtained.Results showed that the relative errors of forecasting rice yield and were less than 5%.
5 County rice yield estimation using MOD13Q1,MYD13Q1 EVI and township level statistic data
According to remote sensing rice yield estimation based on county level statistic data, one mountain county(Liling) with a large error was selected to further analysis the rice yield estimation using township level statistic data.MOD13Q1 and MYD13Q1 combined EVI corresponding with the rice area based on 1/10000 scale land-use data was extracted. Transplanting,booting,heading,milky and harvest stage were selected as the key periods for rice yield estimation,EVI multiplied by township rice area and township rice total yield were took as the independent and dependent variables,and then different type models were built.The optimal rice yield estimation models were selected after errors analysis,and then the rice forecasting yield were obtained.The results show that the relative errors for rice estimation and forecasting models is less than 0.1%and5%, respectively.
6 Rice yield estimation based on MODIS GPP/NPP data at the pixel level,and the impact of the rice area percentage in one pixel on the accuracy of rice yield estimation
Based on previous researches,algorithms for rice yield estimation using MODIS GPP/NPP data were studied.8-day NPP was calculated using 8-day LAI,PSNnet data and MODIS17 user's guide,and then rice yield estimation was performed using 8-day MODIS GPP/NPP and rice growth period data.We used MODIS GPP image to create 1×1km grid,and calculated the percentage of paddy rice area in each grid,and then the impact of different percentage of paddy rice area in each grid on rice yield estimation result was studied.The result showed that:there is a significant positive correlation between 8-day estimation NPP results and MODIS annual NPP product.(P＜0.01),and the rice yield estimation based on NPP data was low compared to statistic data,but the results of MODIS GPP is better.The relative errors between rice yield from estimation and statistic data decrease with the percentage of rice area in the 1×1km increasing,and they are less than 20%,even 10%when the percentage is larger than 80%
According to the above researches,the main conclusions are as follows:the relative error of the rice yield estimation based on county,township level statistic data,plot measured data,and MODIS GPP was less than 5%.However,for the sake of improving rice yield estimation accuracy,the models based on the integration of Terra and Aqua data at the township level are the best choice.Considering rice area and yield estimation on vocational operation,the methods based on the integration of Terra and Aqua data and measured data are the best choice.If the plot measured data aren't available,the rice yield estimation methods,with the support of zoning and rice area detection,and based on the county level statistic data are perfect.For the large paddy rice area,the rice yield estimation algorithms based on MODIS GPP/NPP at the level of pixel are dependable.
引文
[1] Abou-ismail, O., Huang, J.F., Wang, R.C., Rice yield estimation by integrating remote sensing with rice growth simulation model [J]. Pedosphere, 2004, 14 (4): 519-526.
[2] Badhwar,G.D., Crop emergence date determination from spectral data. Photogrammetrc Engineering and Remote Sensing [J], 1980, 46:369-377.
[3] Barrs, H.D., Prathapar, S.A., An inexpensive and effective basis for monitoring rice areas using GIS and remote sensing [J]. Australian Journal of Experimental Agriculture, 1994,34: 1079-1083
[4] Basnyat, P., McConkey, B., Lafond, G.R., Moulin, A., Pelcat, Y., Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies[J]. Canadian Journal of Plant Science, 2004, 28 (1):97-103.
[5] Boschetti, M., Confalonieri, R., Stroppiana, D., Bocchi, S., Brivio, P.A., Agro-ecological modelling for monitoring rice productions: contribution of field experiment and multi-temporal EO data. Proceedings of SPIE, 2005 , 5976: 597601-1
[6] Casanova, D., Epema, G.F., Goudriaan, J., Monitoring rice reflectance at field level for estimating biomass and LAI [J]. Field Crops Research , 1998, 55: 83-92 .
[7] Chang, K.W., Shen, Y., Lo, J.C., Predicting rice yield using canopy reflectance measured at booting stage [J]. Agronomy Journal,2005, 97 (3):872-878.
[8] Chapin, F., Stuart, III, Matson, P.A., Mooney, H.A., Principles of Terrestrial Ecosystem Ecology [M]. Birkhauser, 2002, 137-140.
[9] Chen, J., Jonsson, P., Tamura, M., Gu, Z.H., Matsushita, B., Eklundh, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter [J]. Remote Sensing of Environment, 2004, 91: 332-344.
[10] Chen, C., Mcnairn, H., A neural network integrated approach for rice crop monitoring [J]. International Journal of Remote Sensing. 2006, 27 (7): 1367-1393.
[11] Cihlar, J., Identification of contaminated pixels in AVHRR composite images for studies of land biosphere [J]. Remote Sensing of Environment, 1996, 56: 149-153.
[12]Clevers, JGPW, Van Leeuwen, HJC, Combined use of optical and microwave remote sensing data for crop growth monitoring [J]. Remote Sensing of Environment, 1996, 56:42-51
[13]Dadhwal, V.K., Sridhar, V.N., A non-linear regression form for vegetation index-crop yield relation incorporating acquisition date normalization [J]. International Journal of Remote Sensing.1997, 18(6):1403-1408.
[14]David, B., Lobell, A., Gregory, P., Asner, a, 1.J., Ivan Ortiz-M.B., Tracy L., Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties [J]. Agriculture, Ecosystems and Environment. 2003, 94 205-220.
[15]Denier, Van Der Gon H., Changes in CH_4emission from rice fields from 1960s to 1990s: 1. Impacts of modern rice technology [J]. Global Biogeochemical Cycles. 2000,1:61-72.
[16]Doraiswamy, P. C., Cook, P. W., Spring wheat yield assessment using NOAA AVHRR data [J]. Canadian Journal of Remote Sensing. 1995, 21: 43-51.
[17]Doraiswamy, PC., Hatfield, J.L., Jacksona,T.J., Akhmedov, B., Pruegerb, J. S. A., Crop condition and yield simulations using Landsat and MODIS [J]. Remote Sensing of Environment. 2004, 92: 548-559
[18]Fang, H.L., Wu, B.F., Liu, H.Y., Huang, X., Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year [J]. International Journal of Remote Sensing. 1998, 19(3): 521-525.
[19]Fang, H. L., Rice crop area estimation of an administrative division in China using remote sensing data [J]. International Journal of Remote Sensing. 1998, 17: 3411- 3419.
[20] Gao, B.C., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 1996, 58. 257-266
[21 ] Genovese, C. V., Negre. T, Passera, G., A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting: a case study on Spain[J]. Agronomie, 2001, 21: 91-111.
[22]Gillian, L. G., John, F.M., Jerry, M., Gendrin, A., Cerri, C.C., Cerri, C.E.P., Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil [J]. Remote Sensing of Environment, 2008, 112: 576-587.
[23]Hatfield,J.L., Remote sensing estimators of potential and actual crop yield[J]. Remote Sensing of Environment. 1983, 13(4): 301-311.
[24] Hayes, M. J., Decker, W. L., Using NOAA AVHRR data to estimate maize production in the US combelt [J]. International Journal of Remote Sensing. 1996, 17: 3189-3200.
[25]Hayes, M. J., Decker, W.L., Using satellite and real-time weather data to predict maize production [J].International Journal of Biometeorology. 1998,42 (1): 10-15
[26]Huete, A.R., A soil-adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment. 1988, 25: 295-309
[27] Huete, A. R., Liu, H. Q., Batchily, K., Van leeuwen W., A comparison of vegetation indices global set of TM images for EOS-MODIS [J]. Remote Sensing of Environment. 1997, 59: 440-451.
[28]Huete, A.R., Didan, K., Miura, T., Rodriguez, X., Gao, X., Ferreira, L.G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment. 2002, 83: 195-213.
[29]Idso, S.B., Jackson, R.D., Reginato, R.J., Remote sensing for agricultural water management and crop yield prediction [J]. Agricultural Water Management. 1977, 1(4):299-310.
[30]International Rice Research Institute, 1993-1995 IRRI Rice Almanac [M]. Manila, International Rice Research Institute. 1993.
[31]Ishitsuka, N., Saito, G., Ogawa, S., Area determination of rice paddy using satellite SAR data [J]. 9th International Space Conference of Pacific-Basin-Societies. 2002,223-231.
[32]Jonsson, P., Eklundh, L., Seasonality extraction by function fitting to time-series of satellite sensor data [J]. IEEE Transactions on Geoscience and Remote Sensing. 2002, 40(8): 1824-1832.
[33]Kaufman, Y. J., Tanre, D., Atmospherically resistant vegetation index (ARVI) for EOS-MODIS [J]. IEEE Transactions on Geoscience and Remote Sensing. 1992, 30: 261-270.
[34]Kurosu, T., Fujita, M. Chiba, K., The identification of rice fields using multitemporal ERS-1 C band SAR data [J]. International Journal of Remote Sensing.1997, 18: 2953-2965
[35] Lang, S.L., Fang, H.L., Hoogenboom, G., Teasdale, J., Cavigelli, M, Estimation of crop yield at the regional scale from MODIS observations [J]. IEEE International Geoscience and Remote Sensing Symposium, 2004,1625-1628.
[36] Le Toan, T., Ribbes, F., Wang, L., Floury, N., Ding, K., Kong, J., Fujita, M., Kurosu T., Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results [J]. IEEE Transactions on Geoscience and Remote Sensing. 1997, 1, 41-56,
[37]Li, C.S., Qiu, J.J., Frolking, S., Xiao, X.M., Salas, W., Moore, B., Boles, S., Huang, Y., Sass, R., Reduced methane emissions from large-scale changes in water management of China's rice paddies during 1980-2000[J]. Geophysical Research Letters, 2002, 29(20): 1972.
[38]Liu, H.Q., Huete, A.R., A feedback based modification of the NDV I to m inim ize canopy background and atmospheric noise [J]. IEEE Transactions on Geoscience and Remote Sensing 1995, 33: 457- 465.
[39] Liu, J., Liu, M., Zhuang, D., Zhang, Z., Deng, X., Study on spatial patterns of land use change in China during 1995-2000 [J]. Sciences in China : Series D Earth Sciences, 2003, 46, 373-384.
[40] Magd, I.A., Tanton, T., Remote sensing and GIS for estimation of irrigation crop water demand [J]. International Journal of Remote Sensing, 2005, 26 (11): 2359-2370.
[41] Maas, S. J., Use of remotely-sensed information in agricultural crop growth models [J]. Ecological Modeling, 1998, 41:274 - 268.
[42] Matthews, R., Wassmann, R., Modelling the impacts of climate change and methane emission reductions on rice production: a review [J]. European Journal of Agronomy.2003, 19, 573-598.
[43]McCloy, K. R., Smith, F. R., Robinson, M. R., Monitoring rice areas using Landsat MSS data [J]. International Journal of Remote Sensing, 1987 , 8, 741-749.
[44] Moulin, S., Fischer, A., Dedieu, G., Delecolle, R., Temporal variations in satellite reflectances at field and regional scales compared with values simulated by lining crop growth and SAIL models [J]. Remote Sensing of Environment, 1995, 54:261 - 272.
[45] Moulin, S., Bondeau, A., Delecolle, R., Combining agricultural crop models and satellite observations: From field to regional scales [J]. International Journal of Remote Sensing, 1996, 17 (5): 945 - 956.
[46] Murthy, C.S., Thiruvengadachari, S., Raji, P.V., Jonna, S., Improved ground sampling and crop yield estimation using satellite data [J]. International Journal of Remote Sensing, 1998, 19 (6): 1021 - 1036.
[47] Neue, H., Boonjawat, J., Methane emissions from rice fields. Asian change in the context of global climate change [M]. Cambridge: Cambridge University Press, 1998:187-209.
[48] Okamoto, K., Kawashima, H., Estimation of rice -planted area in the tropical zone using a combination of optical and microwave satellite sensor data [J]. International Journal of Remote Sensing, 1999, 20: 1045-1048.
[49] Panigrahy, S., Chakraborty, M., Sharma, S. A., Kundu ,N, Ghose, S. C., Pal, M., Early estimation of rice area using temporal ERS-1 synthetic aperture radar data-a case study for the Howrah and Hughly districts of West Bengal, India [J]. International Journal of Remote Sensing, 1997 , 18, 1827-1833.
[50] Patel, N.K., Singh T. P., Sahai, B., Spectral response of rice crop and its relation to yield and yield attribytes[J]. International Journal of Remote Sensing, 1985, 6(3): 657-664.
[51] Patel, N.K., Ravi, N., Navalgund, R.R., Dash, R.N., Das, K.C., Patnaik, S., Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa [J]. International Journal of Remote Sensing, 1991, 12: 2259-2266.
[52] Peng, D.L., Huang, J.F., Jin, H.M., Monitoring the Sequential Cropping Index of Arable Land in Zhejiang Province of China Using MODIS-NDV [J] Agricultural Sciences in China, 2007, 6 (2)208-213.
[53]Philipson, N.R., Operational Interpretation of AVHRR Vegetation Indices for World Crop Information [J]. Photogrammetric Engineering and Remote Sensing, 1998, 54(1): 55-59.
[54] Prasad, A.K., Chai, L., Singh, R.P., Kafatos, M., Crop yield estimation model for Iowa using remote sensing and surface parameters [J]. International Journal of Applied Earth Observation and Geoinformation. 2006, 8(1): 26-33.
[55]Quarmby, N., Milnes, M., Hindle, T. Silleos N., The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction [J]. International Journal of Remote Sensing, 1993,14: 199-210
[56]Rahman, A., Roytman, L., Krakauer, N.Y., Nizamuddin, M., Goldberg, M., Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh [J]. Sensors, 9(4):2968-2975.
[57]Reevers, M. C., Zhao, M.S., Running, S. W., Usefulness and limits of MODIS GPP for estimating wheat yield [J]. International Journal of Remote Sensing, 2005, 26, (7): 1403-1421
[58] Ren, J.Q., Li, S., Chen, Z.X., Zhou, Q.B., Tang, H.J., Regional yield prediction for winter wheat based on crop biomass estimation using multi-source data[J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2007,805-808.
[59] Ribbes, F., Toan, T. L., Rice field mapping and monitoring with RADARSAT data [J]. International Journal of Remote Sensing, 1999, 20: 745-765.
[60] Rouse, J., Haas, R., Schell, J., Deering, D., Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, 1973, 1,309-317.
[61] Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., A crop phenology detection method using time-series MODIS data [J]. Remote Sensing of Environment, 2005, 96: 366-374.
[62] Savitzky, A., Golay, M. J.E., Smoothing and differentiation of data by simplified least squares procedures [J]. Analytical Chemistry, 1964, 36(8): 1627-1639.
[63] Sellers, P.J., Tucker, C.J., Collatz, G.J., Los, S.O., Justice, CO., Dazlich, D.A., Randall, D.A., A global 1° by 1° NDVI data set for climate studies: Part II. The generation of global fields of terrestrial biophysical parameters from the NDVI [J]. International Journal of Remote Sensing, 1994, 15(17): 3519-3545.
[64] Shibayama, M., Akiyama, T., Seasonal visible- Near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass [J]. Remote Sensing of Environment, 1989, 27: 119-127 .
[65] Singh, R., Semwal, D.P., Rai, A., Chhikara, R.S., Small area estimation of crop yield using remote sensing satellite data [J]. International Journal of Remote Sensing, 2002, 23(1):49-56.
[66]Tao, F.L., Yokozawa, M., Zhang, Z., Xua, Y.I., Hayashi, Y., Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties [J]. Ecological Modelling.2005,183 ,385-396
[67]Tennakoon, S.B., Murty, W.N., Eiumnoh, A., Estimation of cropped area and grain-yield of rice using remote sensing data [J]. International Journal of Remote Sensing, 1992,13(3):427-439.
[68]Thuy, L. T., Ribbes, T., Wang, F., Floury, L., Ding, N., Kong, K., Fujita, J. A., Kurosu T., Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results [J]. IEEE Transactions on Geoscience and Remote Sensing, 1997,35: 41-56
[69] Willem, J.D., Van, L., Barron, J.O., Stuart, E.M., Stefanie, M.H., Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications [J]. Remote Sensing of Environment. 2006, 100: 67-81.
[70] Viovy, N., Arino, O., Belward, A., The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series [J]. International Journal of Remote Sensing, 1992, 13(8): 1585-1590.
[71] Wada, Y., Ohira, W., Reconstructing cloud free SPOT/VEGETATION using Harmonic analysis with local maximum fitting [J]. Proceedings of 25th Asian Conference on Remote Sensing, 2004, 1663-1667.
[72] Weiss, M., Troufleau, D., Baret, F., Chauki, L., Prevot, A., Olioso, N., Bruguier Brisson, N., Coupling canopy functioning and radiative transfer models fox remote sensing data assimilation [J]. Agricultural and Forest Meteorology, 2001,108:113-128
[73] Wim, G.M., Bastiaanssen, S.A., A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan [J]. Agriculture, Ecosystems and Environment. 2003,94 , 321-340
[74] Wu,B.F., Xu, W.B., Zhang, Y., Tian, Y.C., Huang, J.X., Evaluation of CBERS-2 CCD data for agricultural monitoring[J]. IEEE International Geoscience and Remote Sensing Symposium,2004,4025-4027.
[75] Xiao, X.M., He, L., Salas, W., Li, C., Moore, B., Zhao, R., Frolking, S., Boles, S., Quantitative relationships between field-measured leaf area index and vegetation index derived from vegetation images for paddy rice fields [J]. International Journal of Remote Sensing, 2002a, 23, 3595- 3604.
[76] Xiao, X.M., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., Zhao, R., Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using vegetaion sensor data [J]. International Journal of Remote Sensing, 2002b, 23, 3009- 3022.
[77] Xiao, X.M., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., Zhao, R., Landscape-scale characterization of cropland in China using vegetation and landsat TM images [J]. International Journal of Remote Sensing, 2002c, 23, 3579- 3594.
[78]Xiao, X.M., Boles, S., Liu, J. Y., Zhuang, D.F., Frolking, S., Li, C.S., Salas, W., Berrien, M., Mapping paddy rice agriculture in southern China using multi-temporal MODIS images [J].Remote Sensing of Environment, 2005, 95, 480-492.
[79] Xiao, X.M., Boles, S., Frolking, S., Li, C.S., Babu, J.Y., Salas, W., Berrien, M., Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images [J]. Remote Sensing of Environment, 2006,100, 95-113.
[80] Zhang, R.H., An improved model of crop yield estimate by remote sensing[J], KeXue TongBao, 1984,29(2):284-284.
[81] Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F. H., Gao, F., Reed B., Huete, R.A., 2003. Monitoring vegetation phenology using MODIS [J]. Remote Sensing of Environment, 84, 471 -475.
[82]Zhao, M.S., Running, S. W., Sensitivity of Moderate Resolution Imaging (MODIS) terrestrial primary production to the accuracy of meteorological re-analyses [J]. Journal of geophysical research. 2006, 111, 1-13.
[83]Zhao,M.S.,Heinscha,F.A.,Nemanib,R.R,Running,S.W.,Improvements of the MODIS terrestrial gross and net primary production global data set[J].Remote Sensing of Environment,2005,95,164-176
[84]Abou-ismail,O.,基于MODIS数据更新Rice-SRS模型的水稻估产研究(博士后出站论文),杭州.浙江大学.2005,12-78.
[85]曹阳,卞科,陈春刚,方丽.基于两维图论聚类分析的中国储粮区域划分[J].中国粮油学报,2005,20(4):122-128.
[86]陈拉.水稻遥感估产的不确定性研究(博士学位论文),杭州,浙江大学,2007,1-186.
[87]陈沈斌 孙九林.建立我国主要农作物卫星遥感估产运行系统的主要技术环节及解决途径[J].自然资源学报.1997,12(4):363-369
[88]程乾.MODIS数据提高水稻卫星遥感估产精度稳定性机理与方法的研究(博士学位论文),杭州,浙江大学,2004,1-113.
[89]程乾.基于MODIS产品水稻遥感估产模型研究[J].农业工程学报,2006,22(3):79-83.
[90]代立芹,吴炳方,李强子,李国春,徐新刚.作物单产预测方法研究进展[J].农业网络信息,2006,4:24-27
[91]方红亮.两种水稻种植面积遥感提取方案的分析[J].地理学报,1998,53(1):58-65.
[92]方红亮,刘海燕,黄进良,刘可群,江汉平原水稻遥感估产集成系统[J];遥感技术与应用;1996,2:48-56
[93]方红亮,吴炳方,刘海燕,黄绚.运用NOAAAVHRR和LandsatTM数据估算多年水稻种植面积[J].遥感技术与应用,1997,3:24-27.
[94]郭德友,吕耀昌,彭德福.朱振海,陶红等编译,农业遥感-农作物估产的理论与方法[M],北京:科学出版社,1986,
[95]吉书琴,陈鹏狮,张玉书.水稻遥感估产的一种方法[J].应用气象学报,1997,8(4):509-512.
[96]韩立建,潘耀忠,贾斌,朱秀芳,刘旭拢,王双,张锦水.基于多时相IRS-P6卫星AWiFS影像的水稻种植面积提取方法[J].农业工程学报,2007,23(5):137-143.
[97]韩颖娟,李剑萍,张学艺,亢艳莉.中巴资源卫星在宁夏水稻种植面积调查中的应用[J].农业网络信息,2006,11:13-17.
[98]何英彬.陈佑启,唐华俊.基于MODIS反演逐日LAI及SIMRIW模型的冷害对水稻单产的影响研究[J].农业工程学报,2007,23(11):188-194.
[99]何英彬.基于MODIS与TM的冷害影响水稻产量评估研究(博士学位论文),北京,中国农业科学院,2007,10-76.
[100]洪德林,潘恩飞,陈长青.杂交粳稻与纯系粳稻收获指数比较[J],南京农业大学学报.1998,21(4):12-18.
[101]胡冬炎.醴陵市杂交水稻推广中存在的问题与对策[J].杂交水稻.2005,20(1):40-41.
[102]黄敬峰.基于GIS的大面积水稻遥感估产方法研究-以浙江省为例(博士学位论文),杭州,浙江大学,1999,1-206.
[103]黄敬峰,杨忠恩,王人潮,许红卫,蒋亨显.基于GIS的水稻遥感估产模型研究[J].遥感技术与应用,2002,17(3):125-128.
[104]江东,王乃斌 杨小唤.我国粮食作物卫星遥感估产的研究[J].自然杂志,1999,21(6):351-355.
[105]江东,王乃斌,杨小唤,刘红辉.NDVI曲线与农作物长势的时序互动规律[J].生态学报.2002,22(2):247-253.
[106]江南,何隆华,王延颐.江苏水稻遥感估产研究[J].长江流域资源与环境,1996,5(2):160-166.
[107]焦险峰,杨邦杰,裴志远.基于分层抽样的中国水稻种植面积遥感调查方法研究[J],农业工程学报,2006,22(5):105-110.
[108]雷祖强,周天颖,郑丁元.运用QuickBird卫星影像于水稻田丘块萃取之研究[J].航测及遥测学刊,2006,11(3):297-310.
[109]李登科,张树誉.MODIS遥感数据的接收处理[J].陕西气象,2003a,1,34-36.
[110]李登科,张树誉.EOS/MODIS遥感数据与应用前景[J].陕西气象,2003b,2,37-40.
[111]李佛琳,李本逊,曹卫星.作物遥感估产的现状及其展望[J].云南农业大学学报,2005,20(5):680-684.
[112]廖圣东,廖其芳,李岩,何敬廉,SNB-SAR数据在大范围水稻种植面积调查中的应用--以广东省早稻调查为例[J].热带地理,2001,21(4):346-349.
[113]李岩,彭少麟,廖其芳,廖圣东.RADARSAT SNB SAR数据在大面积水稻估产中的应用研究[J].地球科学进展,2003,18(1):109-115.
[114]李卫国.基于TM遥感信息和产量形成过程的水稻估产模型[J].江苏农业科学,2007,4,12-14.
[115]李卫国,李秉柏,王志明,张娅香,黄晓军.作物长势遥感监测应用研究现状和展望[J].江苏农业科学.2006,3:12-15.
[116]李维钧,张微微,武伟,刘洪斌,基于中巴资源卫星数据的水稻种植面积监测--以梁平县为例[J].西南大学学报(自然科学版),2009,31(1):179-184.
[117]刘海启.欧盟MARS计划简介与我国农业遥感应用思路[J].中国农业资源与区划,1999,20(3):55-57.
[118]刘海启,金敏毓,龚维鹏.美国农业遥感技术应用状况概述[J].中国农业资源与区划,1999,20(2):56-60.
[119]刘海岩.牛振国,陈晓玲.EOS-MODIS数据在我国衣作物监测中的应用[J].遥感技术与应用,2005,20(5):531-536.
[120]刘浩,邵芸,王翠珍.多时相Radarsat数据在广东肇庆地区稻田分类中的应用[J].国土资源遥感.1997,4:1-6.
[121]刘可群,张晓阳,黄进良.江汉平原水稻长势遥感监测及估产模型[J].华中师范大学学报(自然科学版)[J].1997,31(4):482-487.
[122]刘可群,杨柏松,叶柏年,张晓阳,黄进良,李劲峰.江汉平原水稻实用数值估产模型[J].华中师范大学学报(自然科学版),1996.30(1):115-119.
[123]刘喜珍,李国靖,王立平,何国金,胡德水,应用卫星遥感技术对水稻面积变化实施动态监测[J].2003,2:5-7.
[124]马玉平,王石立,张黎,庄立伟.基于升尺度方法的华北冬小麦区域生长模型初步研究.潜在生产水平[J].作物学报,2005,31(6):697-705.
[125]蒙继华,吴炳方,李强子,张磊,全球农作物长势遥感监测系统的设计和实现[J].世界科技研究与发展,2006,28(3):41-44.
[126]彭代亮.黄峰,金辉明.基于MODIS-NDVI的浙江省耕地复种指数监测[J].中国农业科学,2006,39(7):1352-1357.
[127]潘晓华,邓强辉.作物收获指数的研究进展[J].江西农业大学,2007.29(1):1-5.
[128]千怀遂,李明霞.大面积农作物遥感估产区划的理论研究[J].河南大学学报(自然科学版),1997a,27(4):84-92.
[129]千怀遂.中国小麦遥感估产区划研究[J].自然资源学报,1997b,12(2):97-104.
[130]千怀遂,李明霞.中国玉米遥感估产区划研究[J].中国农业科学,1998a,31(4):32-39.
[131]千怀遂.农作物遥感估产最佳时相的选择研究-以中国主要粮食作物为例[J].生态学报,1998b,18(1):48-55.
[132]青先国,艾治勇.湖南水稻种植区域化布局研究[J],农业现代化研究,2007,28(6):704-708.
[133]任建强,陈仲新,唐华俊.基于MODIS-NDVI的区域冬小麦遥感估产[J].应用生态学报.2006,17(12):2371-2375.
[134]任建强,陈仲新,唐华俊,石瑞香.基于植物净初级生产力模型的区域冬小麦估产研究[J].农业工程学报.2006,22(5):111-117.
[135]石宇虹,朴瀛,张菁,李国春,应用NOAA/AVHRR资料监测水稻长势的研究[J].应用气象学报,1999,10(2):243-248.
[136]孙九林.中国农作物遥感动态监测与估产总论[M],北京:中国科学技术出版社.1996。
[137]孙华生.利用多时相MODIS数据提取中国水稻种植面积和长势信息(博士学位论文),杭州,浙江大学,2008,1-166.
[138]谭炳香,李增元,SAR数据在南方水稻分布图快速更新中的应用方法研究[J].国土资源遥感,2000,1:24-27.
[139]唐延林,黄敬峰,王人潮,王福民,水稻遥感估产模拟模式比较[J].农业工程学报,2004 20(1):166-171.
[140]田翠玲.水稻长势遥感监测与产量预报复合建模研究(硕士学位论文),南京,南京信息工程大学,2005,7-26.
[141]王长耀,林文鹏.基于MODIS EVI的冬小麦产量遥感预测研究[J],农业工程学报,2005 21(10):90-94.
[142]王福民.水稻参数高光谱反演方法研究及其系统开发和水稻面积遥感提取(博士学位论文).杭州.浙江大学,2007,1-221.
[143]王纪华.赵春江.黄文江,杨宝祝,王北洪,杨信廷.定量遥感参数与作物肥水模型链接初探[J].华北农学报.2001,16(4):52-58.
[144]王纪华,赵春江,黄文江.农业定量遥感基础与应用[M],北京:科学出版社.2008.259-289.
[145]王乃斌,中国小麦遥感动态监测与估产[M],北京:中国科学技术出版社,1996.
[146]王人潮,黄敬峰.水稻遥感估产[M],北京:中国农业出版社,2002,1-267.
[147]王人潮,王珂,沈掌泉,蒋亨显,朱德峰,蔡体常.水稻单产遥感估测建模研究[J].遥感学报.1998,2(2):119-124.
[148]王延颐.植被指数与水稻长势及产量结构要素关系的研究[J].国土资源遥感,1996,1:56-59.
[149]王延颐,高庆芳.稻田光谱与水稻长势及产量结构要素关系的研究[J].国土资源遥感.1996.1:51-55.
[150]王正兴,刘闯,Huete,R.A.,植被指数研究进展_从AVHRR_NDVI到MODIS_EVI[J].生态学报,2003,23(5):979-987.
[151]王正兴,刘闯,赵冰茄,刘爱军.利用MODIS增强型植被指数反演草地地上生物量[J].兰州大学学报(自然科学版),2005,41(2):10-16.
[152]吴炳方.全国农情监测与估产的运行化遥感方法[J].地理学报,2000,55(1):25-35.
[153]吴炳方.中国农情遥感监测研究[J].中国科学院院刊,2004,19(3):202-205.
[154]吴炳方,田亦陈,李强子.GVG农情采样系统及其应用[J].遥感学报,2004,8(6):570-580.
[155]吴炳方.中国农情遥感速报系统[J].遥感学报,2004,8(6):481-497.
[156]吴炳方,李强子.基于两个独立抽样框架的农作物种植面积遥感估算方法[J].遥感学报.2004,8(6):551-569.
[157]吴炳方,刘海燕.水稻种植面积估计的运行化遥感方法[J].遥感学报,1997,1(1):58-64.
[158]吴炳方,张峰,刘成林,张磊,罗治敏.农作物长势综合遥感监测方法[J].遥感学报,2004,8(6):498-513.
[159]薛利红,曹卫星,罗卫红.基于冠层反射光谱的水稻产量预测模型[J].遥感学报.2005,9(1):100-105.
[160]许红卫,王人潮.浙江省水稻遥感估产区划研究[J].浙江大学学报(农业与生命科学版),2000,26(4):417-422.
[161]阎静,王汶,李湘阁.利用神经网络方法提取水稻种植面积-以湖北省双季早稻为例[J].遥感学报.2001,5(3),227-230.
[162]闫岩,柳钦火,刘强,李静,陈良富.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究[J].遥感学报.2006.10(5):804-811.
[163]杨邦杰.裴志远,农作物长势的定义与遥感监测[J].农业工程学报,1999,15(3):214-218.
[164]杨晓华.基于神经网络和支持向量机的水稻遥感信息提取研究(博士学位论文),杭州,浙江大学,2007,1-166.
[165]杨星卫,王红,周红妹,陆贤.GIS支持下的上海水稻遥感估产分层研究[J].遥感技术与应用,1993,8(2):41-46.
[166]杨星卫,周红妹,李军,汪治澜,王长耀,牛铮.全球稻谷主产国遥感估产可行性研究[J].应用气象学报,1998,9(2):251-256.
[167]杨星卫,薛正平,陆贤.水稻遥感动力估产模拟初探[J].环境遥感,1994,9(4):280-286.
[168]宇振荣,Driessen,P.M.,基于遥感反演作物冠层温度的作物生长模拟和预报[J].中国农业大学学报,2003,8(增刊):71-75
[169]曾澜,张勇,张雪,吴炳方.基于遥感估产和农业统计的粮食供需平衡模型研究[J].遥感学报,2004,8(6):645-654.
[170]张峰,吴炳方,黄慧萍,李苗苗,泰国水稻种植区耕地信息提取研究[J].自然资源学报.2003,8(6):766-772.
[171]张峰,吴炳方,罗治敏.美国冬小麦产量遥感预测方法[J].遥感学报,2004,8(6):611-617.
[172]张宏名,王家圣.农作物遥感监测与估产[M],北京,北京农业大学出版社,1989.
[173]张海珍,马泽忠,周志跃,刘智华.基于MODIS数据的成都市水稻遥感估产研究[J].遥感信息,2008.5:63-67.
[174]张磊,吴炳方,李强子.中国农情采样网络的组织与实施.遥感学报[J].2004,8(6):593-601.
[175]张连义,王刚,宝路如,尔敦扎玛,镡建国,郭志忠.锡林郭勒盟草地MODIS-NDVI植被指数和估产牧草产量季节变化特征[J].草业科学,2008,25(3):6-11.
[176]张云柏,ASAR影像应用于水稻识别和面积测算研究--以江苏宝应县为例 (硕士学位论文)南京,南京农业大学,2004,22-50
[177]张霞,张兵,卫征,陈正超,郑兰芬.MODIS光谱指数监测小麦长势变化研究[J].中国图象图形学报,2005,10(4):420-424.
[178]郑长春,王秀珍,黄敬峰.基于特征波段的SPOT-5卫星影像水稻面积信息自动提取的方法研究[J].遥感技术与应用,2008,23(3):294-299.
[179]中华人民共和国国家统计局.中国统计年鉴[M].北京,中国统计出版社.2007.
[180]赵冰茹,刘闯,刘爱军,王正兴.利用MODIS-NDVI进行草地估产研究[J].草业科学,2004,21(8):12-15.
[181]赵锐,王延颐,戴锦芳.中国水稻遥感动态监测与估产[M].北京:中国科学技术出版社,1996.
[182]赵锐,汤君友,何隆华.江苏省水稻长势遥感监测与估产[J].国土资源遥感,2002,3:9-11.
[2] Badhwar,G.D., Crop emergence date determination from spectral data. Photogrammetrc Engineering and Remote Sensing [J], 1980, 46:369-377.
[3] Barrs, H.D., Prathapar, S.A., An inexpensive and effective basis for monitoring rice areas using GIS and remote sensing [J]. Australian Journal of Experimental Agriculture, 1994,34: 1079-1083
[4] Basnyat, P., McConkey, B., Lafond, G.R., Moulin, A., Pelcat, Y., Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies[J]. Canadian Journal of Plant Science, 2004, 28 (1):97-103.
[5] Boschetti, M., Confalonieri, R., Stroppiana, D., Bocchi, S., Brivio, P.A., Agro-ecological modelling for monitoring rice productions: contribution of field experiment and multi-temporal EO data. Proceedings of SPIE, 2005 , 5976: 597601-1
[6] Casanova, D., Epema, G.F., Goudriaan, J., Monitoring rice reflectance at field level for estimating biomass and LAI [J]. Field Crops Research , 1998, 55: 83-92 .
[7] Chang, K.W., Shen, Y., Lo, J.C., Predicting rice yield using canopy reflectance measured at booting stage [J]. Agronomy Journal,2005, 97 (3):872-878.
[8] Chapin, F., Stuart, III, Matson, P.A., Mooney, H.A., Principles of Terrestrial Ecosystem Ecology [M]. Birkhauser, 2002, 137-140.
[9] Chen, J., Jonsson, P., Tamura, M., Gu, Z.H., Matsushita, B., Eklundh, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter [J]. Remote Sensing of Environment, 2004, 91: 332-344.
[10] Chen, C., Mcnairn, H., A neural network integrated approach for rice crop monitoring [J]. International Journal of Remote Sensing. 2006, 27 (7): 1367-1393.
[11] Cihlar, J., Identification of contaminated pixels in AVHRR composite images for studies of land biosphere [J]. Remote Sensing of Environment, 1996, 56: 149-153.
[12]Clevers, JGPW, Van Leeuwen, HJC, Combined use of optical and microwave remote sensing data for crop growth monitoring [J]. Remote Sensing of Environment, 1996, 56:42-51
[13]Dadhwal, V.K., Sridhar, V.N., A non-linear regression form for vegetation index-crop yield relation incorporating acquisition date normalization [J]. International Journal of Remote Sensing.1997, 18(6):1403-1408.
[14]David, B., Lobell, A., Gregory, P., Asner, a, 1.J., Ivan Ortiz-M.B., Tracy L., Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties [J]. Agriculture, Ecosystems and Environment. 2003, 94 205-220.
[15]Denier, Van Der Gon H., Changes in CH_4emission from rice fields from 1960s to 1990s: 1. Impacts of modern rice technology [J]. Global Biogeochemical Cycles. 2000,1:61-72.
[16]Doraiswamy, P. C., Cook, P. W., Spring wheat yield assessment using NOAA AVHRR data [J]. Canadian Journal of Remote Sensing. 1995, 21: 43-51.
[17]Doraiswamy, PC., Hatfield, J.L., Jacksona,T.J., Akhmedov, B., Pruegerb, J. S. A., Crop condition and yield simulations using Landsat and MODIS [J]. Remote Sensing of Environment. 2004, 92: 548-559
[18]Fang, H.L., Wu, B.F., Liu, H.Y., Huang, X., Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year [J]. International Journal of Remote Sensing. 1998, 19(3): 521-525.
[19]Fang, H. L., Rice crop area estimation of an administrative division in China using remote sensing data [J]. International Journal of Remote Sensing. 1998, 17: 3411- 3419.
[20] Gao, B.C., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 1996, 58. 257-266
[21 ] Genovese, C. V., Negre. T, Passera, G., A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting: a case study on Spain[J]. Agronomie, 2001, 21: 91-111.
[22]Gillian, L. G., John, F.M., Jerry, M., Gendrin, A., Cerri, C.C., Cerri, C.E.P., Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil [J]. Remote Sensing of Environment, 2008, 112: 576-587.
[23]Hatfield,J.L., Remote sensing estimators of potential and actual crop yield[J]. Remote Sensing of Environment. 1983, 13(4): 301-311.
[24] Hayes, M. J., Decker, W. L., Using NOAA AVHRR data to estimate maize production in the US combelt [J]. International Journal of Remote Sensing. 1996, 17: 3189-3200.
[25]Hayes, M. J., Decker, W.L., Using satellite and real-time weather data to predict maize production [J].International Journal of Biometeorology. 1998,42 (1): 10-15
[26]Huete, A.R., A soil-adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment. 1988, 25: 295-309
[27] Huete, A. R., Liu, H. Q., Batchily, K., Van leeuwen W., A comparison of vegetation indices global set of TM images for EOS-MODIS [J]. Remote Sensing of Environment. 1997, 59: 440-451.
[28]Huete, A.R., Didan, K., Miura, T., Rodriguez, X., Gao, X., Ferreira, L.G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment. 2002, 83: 195-213.
[29]Idso, S.B., Jackson, R.D., Reginato, R.J., Remote sensing for agricultural water management and crop yield prediction [J]. Agricultural Water Management. 1977, 1(4):299-310.
[30]International Rice Research Institute, 1993-1995 IRRI Rice Almanac [M]. Manila, International Rice Research Institute. 1993.
[31]Ishitsuka, N., Saito, G., Ogawa, S., Area determination of rice paddy using satellite SAR data [J]. 9th International Space Conference of Pacific-Basin-Societies. 2002,223-231.
[32]Jonsson, P., Eklundh, L., Seasonality extraction by function fitting to time-series of satellite sensor data [J]. IEEE Transactions on Geoscience and Remote Sensing. 2002, 40(8): 1824-1832.
[33]Kaufman, Y. J., Tanre, D., Atmospherically resistant vegetation index (ARVI) for EOS-MODIS [J]. IEEE Transactions on Geoscience and Remote Sensing. 1992, 30: 261-270.
[34]Kurosu, T., Fujita, M. Chiba, K., The identification of rice fields using multitemporal ERS-1 C band SAR data [J]. International Journal of Remote Sensing.1997, 18: 2953-2965
[35] Lang, S.L., Fang, H.L., Hoogenboom, G., Teasdale, J., Cavigelli, M, Estimation of crop yield at the regional scale from MODIS observations [J]. IEEE International Geoscience and Remote Sensing Symposium, 2004,1625-1628.
[36] Le Toan, T., Ribbes, F., Wang, L., Floury, N., Ding, K., Kong, J., Fujita, M., Kurosu T., Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results [J]. IEEE Transactions on Geoscience and Remote Sensing. 1997, 1, 41-56,
[37]Li, C.S., Qiu, J.J., Frolking, S., Xiao, X.M., Salas, W., Moore, B., Boles, S., Huang, Y., Sass, R., Reduced methane emissions from large-scale changes in water management of China's rice paddies during 1980-2000[J]. Geophysical Research Letters, 2002, 29(20): 1972.
[38]Liu, H.Q., Huete, A.R., A feedback based modification of the NDV I to m inim ize canopy background and atmospheric noise [J]. IEEE Transactions on Geoscience and Remote Sensing 1995, 33: 457- 465.
[39] Liu, J., Liu, M., Zhuang, D., Zhang, Z., Deng, X., Study on spatial patterns of land use change in China during 1995-2000 [J]. Sciences in China : Series D Earth Sciences, 2003, 46, 373-384.
[40] Magd, I.A., Tanton, T., Remote sensing and GIS for estimation of irrigation crop water demand [J]. International Journal of Remote Sensing, 2005, 26 (11): 2359-2370.
[41] Maas, S. J., Use of remotely-sensed information in agricultural crop growth models [J]. Ecological Modeling, 1998, 41:274 - 268.
[42] Matthews, R., Wassmann, R., Modelling the impacts of climate change and methane emission reductions on rice production: a review [J]. European Journal of Agronomy.2003, 19, 573-598.
[43]McCloy, K. R., Smith, F. R., Robinson, M. R., Monitoring rice areas using Landsat MSS data [J]. International Journal of Remote Sensing, 1987 , 8, 741-749.
[44] Moulin, S., Fischer, A., Dedieu, G., Delecolle, R., Temporal variations in satellite reflectances at field and regional scales compared with values simulated by lining crop growth and SAIL models [J]. Remote Sensing of Environment, 1995, 54:261 - 272.
[45] Moulin, S., Bondeau, A., Delecolle, R., Combining agricultural crop models and satellite observations: From field to regional scales [J]. International Journal of Remote Sensing, 1996, 17 (5): 945 - 956.
[46] Murthy, C.S., Thiruvengadachari, S., Raji, P.V., Jonna, S., Improved ground sampling and crop yield estimation using satellite data [J]. International Journal of Remote Sensing, 1998, 19 (6): 1021 - 1036.
[47] Neue, H., Boonjawat, J., Methane emissions from rice fields. Asian change in the context of global climate change [M]. Cambridge: Cambridge University Press, 1998:187-209.
[48] Okamoto, K., Kawashima, H., Estimation of rice -planted area in the tropical zone using a combination of optical and microwave satellite sensor data [J]. International Journal of Remote Sensing, 1999, 20: 1045-1048.
[49] Panigrahy, S., Chakraborty, M., Sharma, S. A., Kundu ,N, Ghose, S. C., Pal, M., Early estimation of rice area using temporal ERS-1 synthetic aperture radar data-a case study for the Howrah and Hughly districts of West Bengal, India [J]. International Journal of Remote Sensing, 1997 , 18, 1827-1833.
[50] Patel, N.K., Singh T. P., Sahai, B., Spectral response of rice crop and its relation to yield and yield attribytes[J]. International Journal of Remote Sensing, 1985, 6(3): 657-664.
[51] Patel, N.K., Ravi, N., Navalgund, R.R., Dash, R.N., Das, K.C., Patnaik, S., Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa [J]. International Journal of Remote Sensing, 1991, 12: 2259-2266.
[52] Peng, D.L., Huang, J.F., Jin, H.M., Monitoring the Sequential Cropping Index of Arable Land in Zhejiang Province of China Using MODIS-NDV [J] Agricultural Sciences in China, 2007, 6 (2)208-213.
[53]Philipson, N.R., Operational Interpretation of AVHRR Vegetation Indices for World Crop Information [J]. Photogrammetric Engineering and Remote Sensing, 1998, 54(1): 55-59.
[54] Prasad, A.K., Chai, L., Singh, R.P., Kafatos, M., Crop yield estimation model for Iowa using remote sensing and surface parameters [J]. International Journal of Applied Earth Observation and Geoinformation. 2006, 8(1): 26-33.
[55]Quarmby, N., Milnes, M., Hindle, T. Silleos N., The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction [J]. International Journal of Remote Sensing, 1993,14: 199-210
[56]Rahman, A., Roytman, L., Krakauer, N.Y., Nizamuddin, M., Goldberg, M., Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh [J]. Sensors, 9(4):2968-2975.
[57]Reevers, M. C., Zhao, M.S., Running, S. W., Usefulness and limits of MODIS GPP for estimating wheat yield [J]. International Journal of Remote Sensing, 2005, 26, (7): 1403-1421
[58] Ren, J.Q., Li, S., Chen, Z.X., Zhou, Q.B., Tang, H.J., Regional yield prediction for winter wheat based on crop biomass estimation using multi-source data[J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2007,805-808.
[59] Ribbes, F., Toan, T. L., Rice field mapping and monitoring with RADARSAT data [J]. International Journal of Remote Sensing, 1999, 20: 745-765.
[60] Rouse, J., Haas, R., Schell, J., Deering, D., Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, 1973, 1,309-317.
[61] Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., A crop phenology detection method using time-series MODIS data [J]. Remote Sensing of Environment, 2005, 96: 366-374.
[62] Savitzky, A., Golay, M. J.E., Smoothing and differentiation of data by simplified least squares procedures [J]. Analytical Chemistry, 1964, 36(8): 1627-1639.
[63] Sellers, P.J., Tucker, C.J., Collatz, G.J., Los, S.O., Justice, CO., Dazlich, D.A., Randall, D.A., A global 1° by 1° NDVI data set for climate studies: Part II. The generation of global fields of terrestrial biophysical parameters from the NDVI [J]. International Journal of Remote Sensing, 1994, 15(17): 3519-3545.
[64] Shibayama, M., Akiyama, T., Seasonal visible- Near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass [J]. Remote Sensing of Environment, 1989, 27: 119-127 .
[65] Singh, R., Semwal, D.P., Rai, A., Chhikara, R.S., Small area estimation of crop yield using remote sensing satellite data [J]. International Journal of Remote Sensing, 2002, 23(1):49-56.
[66]Tao, F.L., Yokozawa, M., Zhang, Z., Xua, Y.I., Hayashi, Y., Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties [J]. Ecological Modelling.2005,183 ,385-396
[67]Tennakoon, S.B., Murty, W.N., Eiumnoh, A., Estimation of cropped area and grain-yield of rice using remote sensing data [J]. International Journal of Remote Sensing, 1992,13(3):427-439.
[68]Thuy, L. T., Ribbes, T., Wang, F., Floury, L., Ding, N., Kong, K., Fujita, J. A., Kurosu T., Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results [J]. IEEE Transactions on Geoscience and Remote Sensing, 1997,35: 41-56
[69] Willem, J.D., Van, L., Barron, J.O., Stuart, E.M., Stefanie, M.H., Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications [J]. Remote Sensing of Environment. 2006, 100: 67-81.
[70] Viovy, N., Arino, O., Belward, A., The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series [J]. International Journal of Remote Sensing, 1992, 13(8): 1585-1590.
[71] Wada, Y., Ohira, W., Reconstructing cloud free SPOT/VEGETATION using Harmonic analysis with local maximum fitting [J]. Proceedings of 25th Asian Conference on Remote Sensing, 2004, 1663-1667.
[72] Weiss, M., Troufleau, D., Baret, F., Chauki, L., Prevot, A., Olioso, N., Bruguier Brisson, N., Coupling canopy functioning and radiative transfer models fox remote sensing data assimilation [J]. Agricultural and Forest Meteorology, 2001,108:113-128
[73] Wim, G.M., Bastiaanssen, S.A., A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan [J]. Agriculture, Ecosystems and Environment. 2003,94 , 321-340
[74] Wu,B.F., Xu, W.B., Zhang, Y., Tian, Y.C., Huang, J.X., Evaluation of CBERS-2 CCD data for agricultural monitoring[J]. IEEE International Geoscience and Remote Sensing Symposium,2004,4025-4027.
[75] Xiao, X.M., He, L., Salas, W., Li, C., Moore, B., Zhao, R., Frolking, S., Boles, S., Quantitative relationships between field-measured leaf area index and vegetation index derived from vegetation images for paddy rice fields [J]. International Journal of Remote Sensing, 2002a, 23, 3595- 3604.
[76] Xiao, X.M., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., Zhao, R., Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using vegetaion sensor data [J]. International Journal of Remote Sensing, 2002b, 23, 3009- 3022.
[77] Xiao, X.M., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., Zhao, R., Landscape-scale characterization of cropland in China using vegetation and landsat TM images [J]. International Journal of Remote Sensing, 2002c, 23, 3579- 3594.
[78]Xiao, X.M., Boles, S., Liu, J. Y., Zhuang, D.F., Frolking, S., Li, C.S., Salas, W., Berrien, M., Mapping paddy rice agriculture in southern China using multi-temporal MODIS images [J].Remote Sensing of Environment, 2005, 95, 480-492.
[79] Xiao, X.M., Boles, S., Frolking, S., Li, C.S., Babu, J.Y., Salas, W., Berrien, M., Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images [J]. Remote Sensing of Environment, 2006,100, 95-113.
[80] Zhang, R.H., An improved model of crop yield estimate by remote sensing[J], KeXue TongBao, 1984,29(2):284-284.
[81] Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F. H., Gao, F., Reed B., Huete, R.A., 2003. Monitoring vegetation phenology using MODIS [J]. Remote Sensing of Environment, 84, 471 -475.
[82]Zhao, M.S., Running, S. W., Sensitivity of Moderate Resolution Imaging (MODIS) terrestrial primary production to the accuracy of meteorological re-analyses [J]. Journal of geophysical research. 2006, 111, 1-13.
[83]Zhao,M.S.,Heinscha,F.A.,Nemanib,R.R,Running,S.W.,Improvements of the MODIS terrestrial gross and net primary production global data set[J].Remote Sensing of Environment,2005,95,164-176
[84]Abou-ismail,O.,基于MODIS数据更新Rice-SRS模型的水稻估产研究(博士后出站论文),杭州.浙江大学.2005,12-78.
[85]曹阳,卞科,陈春刚,方丽.基于两维图论聚类分析的中国储粮区域划分[J].中国粮油学报,2005,20(4):122-128.
[86]陈拉.水稻遥感估产的不确定性研究(博士学位论文),杭州,浙江大学,2007,1-186.
[87]陈沈斌 孙九林.建立我国主要农作物卫星遥感估产运行系统的主要技术环节及解决途径[J].自然资源学报.1997,12(4):363-369
[88]程乾.MODIS数据提高水稻卫星遥感估产精度稳定性机理与方法的研究(博士学位论文),杭州,浙江大学,2004,1-113.
[89]程乾.基于MODIS产品水稻遥感估产模型研究[J].农业工程学报,2006,22(3):79-83.
[90]代立芹,吴炳方,李强子,李国春,徐新刚.作物单产预测方法研究进展[J].农业网络信息,2006,4:24-27
[91]方红亮.两种水稻种植面积遥感提取方案的分析[J].地理学报,1998,53(1):58-65.
[92]方红亮,刘海燕,黄进良,刘可群,江汉平原水稻遥感估产集成系统[J];遥感技术与应用;1996,2:48-56
[93]方红亮,吴炳方,刘海燕,黄绚.运用NOAAAVHRR和LandsatTM数据估算多年水稻种植面积[J].遥感技术与应用,1997,3:24-27.
[94]郭德友,吕耀昌,彭德福.朱振海,陶红等编译,农业遥感-农作物估产的理论与方法[M],北京:科学出版社,1986,
[95]吉书琴,陈鹏狮,张玉书.水稻遥感估产的一种方法[J].应用气象学报,1997,8(4):509-512.
[96]韩立建,潘耀忠,贾斌,朱秀芳,刘旭拢,王双,张锦水.基于多时相IRS-P6卫星AWiFS影像的水稻种植面积提取方法[J].农业工程学报,2007,23(5):137-143.
[97]韩颖娟,李剑萍,张学艺,亢艳莉.中巴资源卫星在宁夏水稻种植面积调查中的应用[J].农业网络信息,2006,11:13-17.
[98]何英彬.陈佑启,唐华俊.基于MODIS反演逐日LAI及SIMRIW模型的冷害对水稻单产的影响研究[J].农业工程学报,2007,23(11):188-194.
[99]何英彬.基于MODIS与TM的冷害影响水稻产量评估研究(博士学位论文),北京,中国农业科学院,2007,10-76.
[100]洪德林,潘恩飞,陈长青.杂交粳稻与纯系粳稻收获指数比较[J],南京农业大学学报.1998,21(4):12-18.
[101]胡冬炎.醴陵市杂交水稻推广中存在的问题与对策[J].杂交水稻.2005,20(1):40-41.
[102]黄敬峰.基于GIS的大面积水稻遥感估产方法研究-以浙江省为例(博士学位论文),杭州,浙江大学,1999,1-206.
[103]黄敬峰,杨忠恩,王人潮,许红卫,蒋亨显.基于GIS的水稻遥感估产模型研究[J].遥感技术与应用,2002,17(3):125-128.
[104]江东,王乃斌 杨小唤.我国粮食作物卫星遥感估产的研究[J].自然杂志,1999,21(6):351-355.
[105]江东,王乃斌,杨小唤,刘红辉.NDVI曲线与农作物长势的时序互动规律[J].生态学报.2002,22(2):247-253.
[106]江南,何隆华,王延颐.江苏水稻遥感估产研究[J].长江流域资源与环境,1996,5(2):160-166.
[107]焦险峰,杨邦杰,裴志远.基于分层抽样的中国水稻种植面积遥感调查方法研究[J],农业工程学报,2006,22(5):105-110.
[108]雷祖强,周天颖,郑丁元.运用QuickBird卫星影像于水稻田丘块萃取之研究[J].航测及遥测学刊,2006,11(3):297-310.
[109]李登科,张树誉.MODIS遥感数据的接收处理[J].陕西气象,2003a,1,34-36.
[110]李登科,张树誉.EOS/MODIS遥感数据与应用前景[J].陕西气象,2003b,2,37-40.
[111]李佛琳,李本逊,曹卫星.作物遥感估产的现状及其展望[J].云南农业大学学报,2005,20(5):680-684.
[112]廖圣东,廖其芳,李岩,何敬廉,SNB-SAR数据在大范围水稻种植面积调查中的应用--以广东省早稻调查为例[J].热带地理,2001,21(4):346-349.
[113]李岩,彭少麟,廖其芳,廖圣东.RADARSAT SNB SAR数据在大面积水稻估产中的应用研究[J].地球科学进展,2003,18(1):109-115.
[114]李卫国.基于TM遥感信息和产量形成过程的水稻估产模型[J].江苏农业科学,2007,4,12-14.
[115]李卫国,李秉柏,王志明,张娅香,黄晓军.作物长势遥感监测应用研究现状和展望[J].江苏农业科学.2006,3:12-15.
[116]李维钧,张微微,武伟,刘洪斌,基于中巴资源卫星数据的水稻种植面积监测--以梁平县为例[J].西南大学学报(自然科学版),2009,31(1):179-184.
[117]刘海启.欧盟MARS计划简介与我国农业遥感应用思路[J].中国农业资源与区划,1999,20(3):55-57.
[118]刘海启,金敏毓,龚维鹏.美国农业遥感技术应用状况概述[J].中国农业资源与区划,1999,20(2):56-60.
[119]刘海岩.牛振国,陈晓玲.EOS-MODIS数据在我国衣作物监测中的应用[J].遥感技术与应用,2005,20(5):531-536.
[120]刘浩,邵芸,王翠珍.多时相Radarsat数据在广东肇庆地区稻田分类中的应用[J].国土资源遥感.1997,4:1-6.
[121]刘可群,张晓阳,黄进良.江汉平原水稻长势遥感监测及估产模型[J].华中师范大学学报(自然科学版)[J].1997,31(4):482-487.
[122]刘可群,杨柏松,叶柏年,张晓阳,黄进良,李劲峰.江汉平原水稻实用数值估产模型[J].华中师范大学学报(自然科学版),1996.30(1):115-119.
[123]刘喜珍,李国靖,王立平,何国金,胡德水,应用卫星遥感技术对水稻面积变化实施动态监测[J].2003,2:5-7.
[124]马玉平,王石立,张黎,庄立伟.基于升尺度方法的华北冬小麦区域生长模型初步研究.潜在生产水平[J].作物学报,2005,31(6):697-705.
[125]蒙继华,吴炳方,李强子,张磊,全球农作物长势遥感监测系统的设计和实现[J].世界科技研究与发展,2006,28(3):41-44.
[126]彭代亮.黄峰,金辉明.基于MODIS-NDVI的浙江省耕地复种指数监测[J].中国农业科学,2006,39(7):1352-1357.
[127]潘晓华,邓强辉.作物收获指数的研究进展[J].江西农业大学,2007.29(1):1-5.
[128]千怀遂,李明霞.大面积农作物遥感估产区划的理论研究[J].河南大学学报(自然科学版),1997a,27(4):84-92.
[129]千怀遂.中国小麦遥感估产区划研究[J].自然资源学报,1997b,12(2):97-104.
[130]千怀遂,李明霞.中国玉米遥感估产区划研究[J].中国农业科学,1998a,31(4):32-39.
[131]千怀遂.农作物遥感估产最佳时相的选择研究-以中国主要粮食作物为例[J].生态学报,1998b,18(1):48-55.
[132]青先国,艾治勇.湖南水稻种植区域化布局研究[J],农业现代化研究,2007,28(6):704-708.
[133]任建强,陈仲新,唐华俊.基于MODIS-NDVI的区域冬小麦遥感估产[J].应用生态学报.2006,17(12):2371-2375.
[134]任建强,陈仲新,唐华俊,石瑞香.基于植物净初级生产力模型的区域冬小麦估产研究[J].农业工程学报.2006,22(5):111-117.
[135]石宇虹,朴瀛,张菁,李国春,应用NOAA/AVHRR资料监测水稻长势的研究[J].应用气象学报,1999,10(2):243-248.
[136]孙九林.中国农作物遥感动态监测与估产总论[M],北京:中国科学技术出版社.1996。
[137]孙华生.利用多时相MODIS数据提取中国水稻种植面积和长势信息(博士学位论文),杭州,浙江大学,2008,1-166.
[138]谭炳香,李增元,SAR数据在南方水稻分布图快速更新中的应用方法研究[J].国土资源遥感,2000,1:24-27.
[139]唐延林,黄敬峰,王人潮,王福民,水稻遥感估产模拟模式比较[J].农业工程学报,2004 20(1):166-171.
[140]田翠玲.水稻长势遥感监测与产量预报复合建模研究(硕士学位论文),南京,南京信息工程大学,2005,7-26.
[141]王长耀,林文鹏.基于MODIS EVI的冬小麦产量遥感预测研究[J],农业工程学报,2005 21(10):90-94.
[142]王福民.水稻参数高光谱反演方法研究及其系统开发和水稻面积遥感提取(博士学位论文).杭州.浙江大学,2007,1-221.
[143]王纪华.赵春江.黄文江,杨宝祝,王北洪,杨信廷.定量遥感参数与作物肥水模型链接初探[J].华北农学报.2001,16(4):52-58.
[144]王纪华,赵春江,黄文江.农业定量遥感基础与应用[M],北京:科学出版社.2008.259-289.
[145]王乃斌,中国小麦遥感动态监测与估产[M],北京:中国科学技术出版社,1996.
[146]王人潮,黄敬峰.水稻遥感估产[M],北京:中国农业出版社,2002,1-267.
[147]王人潮,王珂,沈掌泉,蒋亨显,朱德峰,蔡体常.水稻单产遥感估测建模研究[J].遥感学报.1998,2(2):119-124.
[148]王延颐.植被指数与水稻长势及产量结构要素关系的研究[J].国土资源遥感,1996,1:56-59.
[149]王延颐,高庆芳.稻田光谱与水稻长势及产量结构要素关系的研究[J].国土资源遥感.1996.1:51-55.
[150]王正兴,刘闯,Huete,R.A.,植被指数研究进展_从AVHRR_NDVI到MODIS_EVI[J].生态学报,2003,23(5):979-987.
[151]王正兴,刘闯,赵冰茄,刘爱军.利用MODIS增强型植被指数反演草地地上生物量[J].兰州大学学报(自然科学版),2005,41(2):10-16.
[152]吴炳方.全国农情监测与估产的运行化遥感方法[J].地理学报,2000,55(1):25-35.
[153]吴炳方.中国农情遥感监测研究[J].中国科学院院刊,2004,19(3):202-205.
[154]吴炳方,田亦陈,李强子.GVG农情采样系统及其应用[J].遥感学报,2004,8(6):570-580.
[155]吴炳方.中国农情遥感速报系统[J].遥感学报,2004,8(6):481-497.
[156]吴炳方,李强子.基于两个独立抽样框架的农作物种植面积遥感估算方法[J].遥感学报.2004,8(6):551-569.
[157]吴炳方,刘海燕.水稻种植面积估计的运行化遥感方法[J].遥感学报,1997,1(1):58-64.
[158]吴炳方,张峰,刘成林,张磊,罗治敏.农作物长势综合遥感监测方法[J].遥感学报,2004,8(6):498-513.
[159]薛利红,曹卫星,罗卫红.基于冠层反射光谱的水稻产量预测模型[J].遥感学报.2005,9(1):100-105.
[160]许红卫,王人潮.浙江省水稻遥感估产区划研究[J].浙江大学学报(农业与生命科学版),2000,26(4):417-422.
[161]阎静,王汶,李湘阁.利用神经网络方法提取水稻种植面积-以湖北省双季早稻为例[J].遥感学报.2001,5(3),227-230.
[162]闫岩,柳钦火,刘强,李静,陈良富.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究[J].遥感学报.2006.10(5):804-811.
[163]杨邦杰.裴志远,农作物长势的定义与遥感监测[J].农业工程学报,1999,15(3):214-218.
[164]杨晓华.基于神经网络和支持向量机的水稻遥感信息提取研究(博士学位论文),杭州,浙江大学,2007,1-166.
[165]杨星卫,王红,周红妹,陆贤.GIS支持下的上海水稻遥感估产分层研究[J].遥感技术与应用,1993,8(2):41-46.
[166]杨星卫,周红妹,李军,汪治澜,王长耀,牛铮.全球稻谷主产国遥感估产可行性研究[J].应用气象学报,1998,9(2):251-256.
[167]杨星卫,薛正平,陆贤.水稻遥感动力估产模拟初探[J].环境遥感,1994,9(4):280-286.
[168]宇振荣,Driessen,P.M.,基于遥感反演作物冠层温度的作物生长模拟和预报[J].中国农业大学学报,2003,8(增刊):71-75
[169]曾澜,张勇,张雪,吴炳方.基于遥感估产和农业统计的粮食供需平衡模型研究[J].遥感学报,2004,8(6):645-654.
[170]张峰,吴炳方,黄慧萍,李苗苗,泰国水稻种植区耕地信息提取研究[J].自然资源学报.2003,8(6):766-772.
[171]张峰,吴炳方,罗治敏.美国冬小麦产量遥感预测方法[J].遥感学报,2004,8(6):611-617.
[172]张宏名,王家圣.农作物遥感监测与估产[M],北京,北京农业大学出版社,1989.
[173]张海珍,马泽忠,周志跃,刘智华.基于MODIS数据的成都市水稻遥感估产研究[J].遥感信息,2008.5:63-67.
[174]张磊,吴炳方,李强子.中国农情采样网络的组织与实施.遥感学报[J].2004,8(6):593-601.
[175]张连义,王刚,宝路如,尔敦扎玛,镡建国,郭志忠.锡林郭勒盟草地MODIS-NDVI植被指数和估产牧草产量季节变化特征[J].草业科学,2008,25(3):6-11.
[176]张云柏,ASAR影像应用于水稻识别和面积测算研究--以江苏宝应县为例 (硕士学位论文)南京,南京农业大学,2004,22-50
[177]张霞,张兵,卫征,陈正超,郑兰芬.MODIS光谱指数监测小麦长势变化研究[J].中国图象图形学报,2005,10(4):420-424.
[178]郑长春,王秀珍,黄敬峰.基于特征波段的SPOT-5卫星影像水稻面积信息自动提取的方法研究[J].遥感技术与应用,2008,23(3):294-299.
[179]中华人民共和国国家统计局.中国统计年鉴[M].北京,中国统计出版社.2007.
[180]赵冰茹,刘闯,刘爱军,王正兴.利用MODIS-NDVI进行草地估产研究[J].草业科学,2004,21(8):12-15.
[181]赵锐,王延颐,戴锦芳.中国水稻遥感动态监测与估产[M].北京:中国科学技术出版社,1996.
[182]赵锐,汤君友,何隆华.江苏省水稻长势遥感监测与估产[J].国土资源遥感,2002,3:9-11.