基于模型和3S的数字稻作技术及系统研究
|
摘要
农业生产系统是复杂的多因子动态系统,影响因子(包括环境、作物、耕作制度、管理技术措施、农产品社会需求等)具有较强的时空变异性、区域分散性、管理经验性。而作物生长模拟模型可基于环境、作物和管理技术措施等对作物生长发育及生产力的形成进行定量描述和动态模拟,作物管理知识模型可基于作物、环境和管理水平生成作物精确管理方案或定量栽培模式,基于遥感的作物生长监测模型可以快速反演、获取作物长势,地理信息系统(GIS)可对环境等因子进行空间分析和管理,因此,综合利用上述关键技术,构建基于模型和3S的数字稻作系统,对于促进信息农业的快速发展具有重要的现实意义。本研究以水稻作物为对象,基于GIS平台,探讨基础气象要素的空间插值与栅格化方法,分析我国主要稻作区水稻生产气候资源的时空分布特征,进一步结合水稻管理知识模型中的层次生产潜力模型,研究我国主要稻作区不同层次水稻生产潜力的时空分布特征及增产潜力,并在本实验室已有水稻生长模拟模型、管理知识模型和生长监测模型的基础上,耦合集成3S技术,构建基于模型和3S的数字稻作系统,实现稻作系统监测、预测和管理决策的数字化、精确化。
针对作物模型区域化应用的需求,采用距离反比权重法(IDW)、协克里格法(CK)和薄盘样条法(TPS)3种不同的空间插值方法,对我国1951—2005年气象数据完整的559个气象站点逐月第15日的平均基本气象要素(最高气温、最低气温、日照时数和降水量)进行了插值分析与评价。结果表明：3种插值方法中,TPS法对最高气温和最低气温插值的根均方差(RMSE)最小(1.02℃和1.12℃)、R2最大(0.9916和0.9913)；不同季节中,TPS法对秋季最高气温、夏季最低气温进行插值的RMSE均最小(0.83℃和0.86℃),R2均为秋季最高。对于日照时数和降水量而言,TPS法的RMSE最小(0.59h和1.01mm)、R2最大(0.9118和0.8135)；不同季节中,TPS法对冬季日照时数进行插值的RMSE最小(0.49h)、R2最大(0.9293),TPS法对冬季降水量进行插值的RMSE最小、(0.33mm),IDW法对夏季降水量进行插值的RMSE最小(2.01mm), CK法对春季降水量进行插值的R2最大(0.8781)。TPS法可作为我国大量逐日基本气象要素的最优空间插值方法。
基于中国主要稻作区333个气象台站1961年～1970年(1960s)和1996年～2005年(2000s)2个10年历史时期的逐日气象资料,使用ARCGIS软件和ANUSPLIN软件包,基于最佳空间插值方法,得到了2个时期的逐日气象要素表面值；进一步逐年计算并比较了2个历史时期水稻生育期内主要气候资源(总日照时数、总有效积温、平均气温日较差、总降水量、总降水天数和平均降水强度)的时空分布特征。结果表明,同1960s相比,2000s中国主要稻作区水稻生育期内的平均总日照时数减少了11.93%,东北、西南地区减少的幅度小于中部和南方；平均总有效积温增加了9.40%,东北和西南地区增加的幅度大于中部和南方,但是在中部和南方存在总有效积温减少的地区；平均气温日较差减少了4.86%,东北和西南地区减少的幅度大于中部和南方,但在中部和南方地区亦存在部分增加的区域；总降水量增加了1.59%,平均降水强度增加了3.22%,平均降水强度的变化率在空间分布上与总降水量基本一致,东北地区和宁夏总降水量和平均降水强度呈降低的趋势,而中部和南方大部分地区呈增加的趋势；平均总降水天数减少了1.60%,东北地区和中部地区的降低幅度要小于南方沿海地区。
进一步基于水稻管理知识模型中的层次生产潜力模块,逐年计算了中国主要稻作区1961年～1970年(1960s)和1996年～2005年(2000s)的水稻光合、光温和气候生产潜力,分析了2个时期水稻光合、光温和气候生产潜力的空间变化规律,并基于2000s的光温和气候生产潜力、以及中国主要稻作区的实际产量和高产目标,计算了中国主要稻作区不同层次水稻增产潜力。结果表明,同1960s相比,2000s中国主要稻作区水稻平均光合生产潜力减少了5.40%,东北、西南地区减少的幅度小于中部和南方地区；平均光温生产潜力减少了2.56%,但东北和西南地区处于增加的趋势；平均气候生产潜力减少了7.44%,但在中部和南部的局部地区有增加的趋势。水稻高产目标对2000s平均产量的增产潜力结果表明,单季稻种植区大部分地区的增产潜力在2-6×103kg/ha之间,双季稻种植区的增产潜力在6-12×103kg/ha之间。2000s水稻光温生产潜力对高产目标的增产潜力,在单季稻种植区内大部分地区小于10×103kg/ha,东北和西南的局部地区光温生产潜力小于高产目标；在双季稻的大部分地区,增产潜力在10～30×103kg/ha之间,广西、广东以及海南等地区在30～40×103kg/ha之间。2000s的光温生产潜力对气候生产潜力的差值显示,在单季稻种植区,灌溉对产量的贡献在5-20×103kg/ha之间,在双季稻种植区中的大部分地区,灌溉对产量的贡献在20～40×103kg/ha之间；研究结果为进一步分析中国不同区域水稻增产途径及国家粮食安全生产提供了决策支持。
在已有作物(小麦和水稻)生长监测模型的基础上,采用地理数据抽象库(Geospatial data abstraction library, GDAL)和图形设备接口(Graphics device interface plus, GDI+)信息处理方法,使用期望最大化(Expectation maximization, EM)算法对反演的作物长势参数进行聚类分析,在Microsoft.NET平台上构建了基于聚类分析和遥感影像的网络化作物生长监测系统。系统具有常见格式遥感影像读取、遥感信息提取、作物长势参数反演、聚类分析、专题图制作以及信息发布等功能；并以江苏省方强农场为案例区,对系统的部分功能进行了测试与检验。结果表明,该系统能够准确的读取遥感影像信息,反演作物生长参数,并可根据聚类分析结果自动制作专题图,通过互联网予以发布,从而初步突破了用户无法直接参与遥感影像分析过程的瓶颈,为区域尺度的作物生长监测和精确管理调控提供了决策支持。
以水稻生长模拟模型、管理知识模型和生长监测模型为核心,结合3S技术,运用软构件设计方法及面向对象编程技术,采用Browser/Server模式和多层架构设计,在Microsoft.NET平台上构建了基于模型和3S的网络化数字稻作系统。系统基于数据层、数据访问层、业务逻辑层和表现层4层结构,以品种、气候、土壤及管理措施为基本输入,能够为用户提供稻作生产规划、方案设计、模拟预测、策略分析、动态调控、生长监测、精确稻作、生产力评价、病虫草害管理、智能学习、系统管理和系统帮助等信息服务。研究结果拓展了模型的应用尺度,有助于促进水稻生产管理的信息化和现代化。
Agricultural production system is a complex dynamic system influenced by multi-factors including environments, crop varieties characteristics, farming systems, management technologies and social demand, etc. With the characteristics of strong spatial and temporal variation, regional dispersion and management empirical, those factors are difficult to be quantized and normalized. On the basis of crop-environment-technology, crop growth simulation models could describe quantitatively and simulate dynamically the crop growth and development and yield formation, and crop management knowledge models could generate the precise crop management plan or quantitative cultivation mode. Based on RS, crop growth monitoring models could quickly invert the crop growth status and nutrient conditions. GIS could analyze and manage spatial data. Therefore, constructing a model-and3S-based digital rice farming system using information technologies mentioned above is practically important to promote the rapid development of information agricultural.
In the study the investigation was made to evaluate methods in interpolating and rasterizing meteorological data based on the platform of GIS. Spatial and temporal distribution characteristics of agricultural climatic resources had been analyzed based on the rasterized daily climatic data during rice-growing periods in main rice growing regions of China. Furthermore, spatial and temporal distribution characteristics of the rice potential photosynthetic, photo-thermal and climatic productivities, yield increment potential were researched based on the gradually descending model, which is one module of the rice management knowledge model. A model-and3S-based digital rice farming system was built based on the models (the rice growth simulation model, management knowledge model and growth monitoring model, which were built by our laboratory) and integration of the3S technology. Model-and3S-based digital rice farming system realized the digital and precise monitoring, prediction and management in the rice farming system.
A comparative study was made to evaluate the methods of inverse distance weighting (IDW), co-Kriging (CK) and thin plate spline (TPS) in interpolating the average meteorological data (including the maximum air temperature, minimum air temperature, sunshine hours and precipitation) of the15th day per month from the1951to2005comprehensive observation data of559meteorological stations in China. The results showed that the RMSEs for the maximum and minimum air temperature in a year interpolated by TPS were the smaller (1.02℃and1.12℃, respectively), and the R2values between the observed and predicted values the highest (0.9916and0.9913, respectively), compared with those interpolated by IDW and CK. In four seasons, the smallest RMSEs for the maximum air temperature and minimum air temperature, interpolated by TPS were observed in autumn(0.83℃) and summer (0.86℃), respectively, and the R2between the observed and predicted values interpolated by TPS were higher than in autumn than in other seasons. The RMSEs for the sunshine hours and precipitation in a year interpolated by TPS were the smallest (0.59h,1.01mm, respectively), and the R2between the observed and predicted values were the highest (0.9118,0.8135, respectively) interpolated by TPS compared with those using CK and IDW. In four seasons, the RMSE for the sunshine hours interpolated by TPS was the smallest (0.49h) in autumn, and the R2between the observed and predicted sunshine hours was the smallest (0.9293) in winter. The RMSE for the precipitation in winter interpolated by TPS was the smallest (0.33mm), while the RMSE for the precipitation in summer interpolated by IDW was the smallest (2.01mm). The R2between the observed and predicted precipitation in winter interpolated by CK was the highest (0.8781). It was suggested that TPS could be the optimal spatial interpolation method in interpolating and raterizing the daily meteorological elements in China.
The climate change trend during rice-growing periods in main rice growing regions of China is highly uncertain because of its vast area and marked variation. The daily meteorological data during the years from1961to1970(1960s) and1996to2005(2000s) of333weather stations in main rice growing regions of China were processed to generate the daily meteorological surface data using ANUSPLIN and ARCGIS. The spatial and temporal distribution characteristics of the climate resources for the rice production, including total sunshine hours, total growing degree days above10℃, average difference of diurnal air temperature, total precipitation, total rainfall days and daily precipitation intensity during rice-growing periods between two decades (1960s and2000s) were calculated and analyzed. As compared with1960s, the total sunshine hours in rice-growing periods of2000s decreased by11.93%, and the decreasing rates of total sunshine hours in the Northeast and Southwest China were lower than those in the Middle and South China. From1960s to2000s, the total growing degree days above10℃increased by9.40%, and the increasing rates of total growing degree days above10℃in the Northeast and Southwest China were faster than those in the Middle and South China. However, in part areas of the Middle and South China, the total growing degree days above10℃during rice-growing periods decreased. The average differences of diurnal air temperature during rice-growing periods of2000s decreased by4.86%as compared with1960s, while increased in part areas of the Middle and South China. The decreasing rates of average difference of diurnal air temperature in the Northeast and Southwest China were higher than those in the Middle and South China. Compared with1960s, total precipitation during rice-growing periods in2000s increased by1.59%and daily precipitation intensity increased by3.22%. The spatial distribution of the change rate of daily precipitation intensity was similar to that of total precipitation, the total precipitation and daily precipitation intensity decreased in Ningxia Province and the Northeast China, while increased in the Middle and South China. Compared with1960s, the total rainfall days during rice-growing periods of2000s decreased by1.60%, and the decreasing rates of total rainfall days in the Northeast and Middle China were lower than that in the South China.
Further, the annual potential photosynthetic, photo-thermal and climatic productivities were calculated with the gradually descending model, which was one module of rice management knowledge model, and the spatial and temporal distribution characteristics of above three potential productivities between two decades (1960s and2000s) were analyzed. Further, the spatial distribution characteristics of yield increasing potential and percentage from high yield target to potential photo-thermal productivity, yield increasing potential from actual yield of2000s to high yield target were investigated. As compared with1960s, the potential photosynthetic productivity of2000s decreases by5.40%, and the decreasing rates in the Northeast and Southwest China were lower than those in the Middle and South China. From1960s to2000s, the potential photo-thermal productivity increased in the Northeast and Southwest China, while decreased in most part areas of the Middle and South China, with average decrement of2.56%in main rice growing regions of China. Meanwhile, the potential climatic productivity of2000s China decreased in most areas of China as compared with1960s, while increased in part of the Middle and South China, with average decrement of7.44%in main rice growing regions of China. The yield increasing potential from the actual yield (2000s) to the high yield target ranged from2×103kg/ha to 6×103kg/ha in the single-cropping rice growing region, from6×103kg/hato12×103kg/ha in the double-cropping rice growing region. The yield increasing potential from the high yield target to the potential photo-thermal productivity (2000s) was less than10×103kg/ha in most areas of the single-cropping rice growing region, ranged from10×103kg/ha to30×103kg/ha in most areas of the double-cropping rice growing region, and greater than20x103kg/ha in most areas of South China, such as Guangxi, Guangdong and Hainan Province. In addition, differences from the potential climatic to photo-thermal productivities in2000s illustrated that the increasing potential contributed by irrigation were between5×103kg/ha and20×10kg/ha in the single-cropping rice growing region, between20×103kg/ha and40×10kg/ha in the double-cropping rice growing region. Differences from potential climatic to photo-thermal productivities in2000s indicated that full use of rainfall and reasonable irrigation was favorable to explore the rice potential productivity and ensure the rice increasing and high yields. These results were helpful for further exploration of technical approaches to enhancing rice yields, and for quantitative decision support for guiding national rice production and food security in China.
Quick and real-time monitoring of crop growth status based on remote sensing can support the decision-making on precision crop management. Based on growth estimating models in wheat and rice established by the authors'group, a RS image-based monitoring system was developed based on the Microsoft.NET framework using GDAL and GDI+as information processing methods and EM algorithm for classifying crop growth indices. This system realized the multiple functions as accessing the RS images with common formats, extracting crop information, inverting growth indices, clustering analysis, generating the thematic map and issuing the information with remote sensing technology. Several functions of the system were tested using the RS images at Fangqiang Farm, Jiangsu Province. The results showed that the system could effectively read general remote sensing images, invert the crop growth indices, classify the crop growth information based on the cluster models, interact with users for generating the thematic map of crop growth status, and issue the RS image information rapidly via internet. The present system has overcome the previous weakness that the ordinary users could not directly participate in the process of RS images analysis, and can help to monitor the crop growth condition and provide decision support for precision crop management at regional scale.
Model-and3S-based digital rice farming system was developed based on plentitude combination of rice growth simulation model, rice management knowledge model, rice monitoring model and3S technologies (GPS, GIS, and RS). Guided by the methodology of software component design and the technology of object oriented program, the system was constructed using the Browser/Server mode and based on the4-tier (the data tier, data access tier, business logic tier and presentation tier) architecture under the Microsoft.NET framework. By inputting the parameters of varieties, climate, soil and production management condition, the system provided users the information services of rice farming zone, cultivation strategy design, growth prediction, strategy evaluation, dynamic regulation, growth monitoring, precision management, productivity evaluation, management of disease, insect and weed, intelligent learning, system management and system help. The study expanded the application scale of models and was helpful to promote the informationization and modernization of the rice production management.
针对作物模型区域化应用的需求,采用距离反比权重法(IDW)、协克里格法(CK)和薄盘样条法(TPS)3种不同的空间插值方法,对我国1951—2005年气象数据完整的559个气象站点逐月第15日的平均基本气象要素(最高气温、最低气温、日照时数和降水量)进行了插值分析与评价。结果表明：3种插值方法中,TPS法对最高气温和最低气温插值的根均方差(RMSE)最小(1.02℃和1.12℃)、R2最大(0.9916和0.9913)；不同季节中,TPS法对秋季最高气温、夏季最低气温进行插值的RMSE均最小(0.83℃和0.86℃),R2均为秋季最高。对于日照时数和降水量而言,TPS法的RMSE最小(0.59h和1.01mm)、R2最大(0.9118和0.8135)；不同季节中,TPS法对冬季日照时数进行插值的RMSE最小(0.49h)、R2最大(0.9293),TPS法对冬季降水量进行插值的RMSE最小、(0.33mm),IDW法对夏季降水量进行插值的RMSE最小(2.01mm), CK法对春季降水量进行插值的R2最大(0.8781)。TPS法可作为我国大量逐日基本气象要素的最优空间插值方法。
基于中国主要稻作区333个气象台站1961年～1970年(1960s)和1996年～2005年(2000s)2个10年历史时期的逐日气象资料,使用ARCGIS软件和ANUSPLIN软件包,基于最佳空间插值方法,得到了2个时期的逐日气象要素表面值；进一步逐年计算并比较了2个历史时期水稻生育期内主要气候资源(总日照时数、总有效积温、平均气温日较差、总降水量、总降水天数和平均降水强度)的时空分布特征。结果表明,同1960s相比,2000s中国主要稻作区水稻生育期内的平均总日照时数减少了11.93%,东北、西南地区减少的幅度小于中部和南方；平均总有效积温增加了9.40%,东北和西南地区增加的幅度大于中部和南方,但是在中部和南方存在总有效积温减少的地区；平均气温日较差减少了4.86%,东北和西南地区减少的幅度大于中部和南方,但在中部和南方地区亦存在部分增加的区域；总降水量增加了1.59%,平均降水强度增加了3.22%,平均降水强度的变化率在空间分布上与总降水量基本一致,东北地区和宁夏总降水量和平均降水强度呈降低的趋势,而中部和南方大部分地区呈增加的趋势；平均总降水天数减少了1.60%,东北地区和中部地区的降低幅度要小于南方沿海地区。
进一步基于水稻管理知识模型中的层次生产潜力模块,逐年计算了中国主要稻作区1961年～1970年(1960s)和1996年～2005年(2000s)的水稻光合、光温和气候生产潜力,分析了2个时期水稻光合、光温和气候生产潜力的空间变化规律,并基于2000s的光温和气候生产潜力、以及中国主要稻作区的实际产量和高产目标,计算了中国主要稻作区不同层次水稻增产潜力。结果表明,同1960s相比,2000s中国主要稻作区水稻平均光合生产潜力减少了5.40%,东北、西南地区减少的幅度小于中部和南方地区；平均光温生产潜力减少了2.56%,但东北和西南地区处于增加的趋势；平均气候生产潜力减少了7.44%,但在中部和南部的局部地区有增加的趋势。水稻高产目标对2000s平均产量的增产潜力结果表明,单季稻种植区大部分地区的增产潜力在2-6×103kg/ha之间,双季稻种植区的增产潜力在6-12×103kg/ha之间。2000s水稻光温生产潜力对高产目标的增产潜力,在单季稻种植区内大部分地区小于10×103kg/ha,东北和西南的局部地区光温生产潜力小于高产目标；在双季稻的大部分地区,增产潜力在10～30×103kg/ha之间,广西、广东以及海南等地区在30～40×103kg/ha之间。2000s的光温生产潜力对气候生产潜力的差值显示,在单季稻种植区,灌溉对产量的贡献在5-20×103kg/ha之间,在双季稻种植区中的大部分地区,灌溉对产量的贡献在20～40×103kg/ha之间；研究结果为进一步分析中国不同区域水稻增产途径及国家粮食安全生产提供了决策支持。
在已有作物(小麦和水稻)生长监测模型的基础上,采用地理数据抽象库(Geospatial data abstraction library, GDAL)和图形设备接口(Graphics device interface plus, GDI+)信息处理方法,使用期望最大化(Expectation maximization, EM)算法对反演的作物长势参数进行聚类分析,在Microsoft.NET平台上构建了基于聚类分析和遥感影像的网络化作物生长监测系统。系统具有常见格式遥感影像读取、遥感信息提取、作物长势参数反演、聚类分析、专题图制作以及信息发布等功能；并以江苏省方强农场为案例区,对系统的部分功能进行了测试与检验。结果表明,该系统能够准确的读取遥感影像信息,反演作物生长参数,并可根据聚类分析结果自动制作专题图,通过互联网予以发布,从而初步突破了用户无法直接参与遥感影像分析过程的瓶颈,为区域尺度的作物生长监测和精确管理调控提供了决策支持。
以水稻生长模拟模型、管理知识模型和生长监测模型为核心,结合3S技术,运用软构件设计方法及面向对象编程技术,采用Browser/Server模式和多层架构设计,在Microsoft.NET平台上构建了基于模型和3S的网络化数字稻作系统。系统基于数据层、数据访问层、业务逻辑层和表现层4层结构,以品种、气候、土壤及管理措施为基本输入,能够为用户提供稻作生产规划、方案设计、模拟预测、策略分析、动态调控、生长监测、精确稻作、生产力评价、病虫草害管理、智能学习、系统管理和系统帮助等信息服务。研究结果拓展了模型的应用尺度,有助于促进水稻生产管理的信息化和现代化。
Agricultural production system is a complex dynamic system influenced by multi-factors including environments, crop varieties characteristics, farming systems, management technologies and social demand, etc. With the characteristics of strong spatial and temporal variation, regional dispersion and management empirical, those factors are difficult to be quantized and normalized. On the basis of crop-environment-technology, crop growth simulation models could describe quantitatively and simulate dynamically the crop growth and development and yield formation, and crop management knowledge models could generate the precise crop management plan or quantitative cultivation mode. Based on RS, crop growth monitoring models could quickly invert the crop growth status and nutrient conditions. GIS could analyze and manage spatial data. Therefore, constructing a model-and3S-based digital rice farming system using information technologies mentioned above is practically important to promote the rapid development of information agricultural.
In the study the investigation was made to evaluate methods in interpolating and rasterizing meteorological data based on the platform of GIS. Spatial and temporal distribution characteristics of agricultural climatic resources had been analyzed based on the rasterized daily climatic data during rice-growing periods in main rice growing regions of China. Furthermore, spatial and temporal distribution characteristics of the rice potential photosynthetic, photo-thermal and climatic productivities, yield increment potential were researched based on the gradually descending model, which is one module of the rice management knowledge model. A model-and3S-based digital rice farming system was built based on the models (the rice growth simulation model, management knowledge model and growth monitoring model, which were built by our laboratory) and integration of the3S technology. Model-and3S-based digital rice farming system realized the digital and precise monitoring, prediction and management in the rice farming system.
A comparative study was made to evaluate the methods of inverse distance weighting (IDW), co-Kriging (CK) and thin plate spline (TPS) in interpolating the average meteorological data (including the maximum air temperature, minimum air temperature, sunshine hours and precipitation) of the15th day per month from the1951to2005comprehensive observation data of559meteorological stations in China. The results showed that the RMSEs for the maximum and minimum air temperature in a year interpolated by TPS were the smaller (1.02℃and1.12℃, respectively), and the R2values between the observed and predicted values the highest (0.9916and0.9913, respectively), compared with those interpolated by IDW and CK. In four seasons, the smallest RMSEs for the maximum air temperature and minimum air temperature, interpolated by TPS were observed in autumn(0.83℃) and summer (0.86℃), respectively, and the R2between the observed and predicted values interpolated by TPS were higher than in autumn than in other seasons. The RMSEs for the sunshine hours and precipitation in a year interpolated by TPS were the smallest (0.59h,1.01mm, respectively), and the R2between the observed and predicted values were the highest (0.9118,0.8135, respectively) interpolated by TPS compared with those using CK and IDW. In four seasons, the RMSE for the sunshine hours interpolated by TPS was the smallest (0.49h) in autumn, and the R2between the observed and predicted sunshine hours was the smallest (0.9293) in winter. The RMSE for the precipitation in winter interpolated by TPS was the smallest (0.33mm), while the RMSE for the precipitation in summer interpolated by IDW was the smallest (2.01mm). The R2between the observed and predicted precipitation in winter interpolated by CK was the highest (0.8781). It was suggested that TPS could be the optimal spatial interpolation method in interpolating and raterizing the daily meteorological elements in China.
The climate change trend during rice-growing periods in main rice growing regions of China is highly uncertain because of its vast area and marked variation. The daily meteorological data during the years from1961to1970(1960s) and1996to2005(2000s) of333weather stations in main rice growing regions of China were processed to generate the daily meteorological surface data using ANUSPLIN and ARCGIS. The spatial and temporal distribution characteristics of the climate resources for the rice production, including total sunshine hours, total growing degree days above10℃, average difference of diurnal air temperature, total precipitation, total rainfall days and daily precipitation intensity during rice-growing periods between two decades (1960s and2000s) were calculated and analyzed. As compared with1960s, the total sunshine hours in rice-growing periods of2000s decreased by11.93%, and the decreasing rates of total sunshine hours in the Northeast and Southwest China were lower than those in the Middle and South China. From1960s to2000s, the total growing degree days above10℃increased by9.40%, and the increasing rates of total growing degree days above10℃in the Northeast and Southwest China were faster than those in the Middle and South China. However, in part areas of the Middle and South China, the total growing degree days above10℃during rice-growing periods decreased. The average differences of diurnal air temperature during rice-growing periods of2000s decreased by4.86%as compared with1960s, while increased in part areas of the Middle and South China. The decreasing rates of average difference of diurnal air temperature in the Northeast and Southwest China were higher than those in the Middle and South China. Compared with1960s, total precipitation during rice-growing periods in2000s increased by1.59%and daily precipitation intensity increased by3.22%. The spatial distribution of the change rate of daily precipitation intensity was similar to that of total precipitation, the total precipitation and daily precipitation intensity decreased in Ningxia Province and the Northeast China, while increased in the Middle and South China. Compared with1960s, the total rainfall days during rice-growing periods of2000s decreased by1.60%, and the decreasing rates of total rainfall days in the Northeast and Middle China were lower than that in the South China.
Further, the annual potential photosynthetic, photo-thermal and climatic productivities were calculated with the gradually descending model, which was one module of rice management knowledge model, and the spatial and temporal distribution characteristics of above three potential productivities between two decades (1960s and2000s) were analyzed. Further, the spatial distribution characteristics of yield increasing potential and percentage from high yield target to potential photo-thermal productivity, yield increasing potential from actual yield of2000s to high yield target were investigated. As compared with1960s, the potential photosynthetic productivity of2000s decreases by5.40%, and the decreasing rates in the Northeast and Southwest China were lower than those in the Middle and South China. From1960s to2000s, the potential photo-thermal productivity increased in the Northeast and Southwest China, while decreased in most part areas of the Middle and South China, with average decrement of2.56%in main rice growing regions of China. Meanwhile, the potential climatic productivity of2000s China decreased in most areas of China as compared with1960s, while increased in part of the Middle and South China, with average decrement of7.44%in main rice growing regions of China. The yield increasing potential from the actual yield (2000s) to the high yield target ranged from2×103kg/ha to 6×103kg/ha in the single-cropping rice growing region, from6×103kg/hato12×103kg/ha in the double-cropping rice growing region. The yield increasing potential from the high yield target to the potential photo-thermal productivity (2000s) was less than10×103kg/ha in most areas of the single-cropping rice growing region, ranged from10×103kg/ha to30×103kg/ha in most areas of the double-cropping rice growing region, and greater than20x103kg/ha in most areas of South China, such as Guangxi, Guangdong and Hainan Province. In addition, differences from the potential climatic to photo-thermal productivities in2000s illustrated that the increasing potential contributed by irrigation were between5×103kg/ha and20×10kg/ha in the single-cropping rice growing region, between20×103kg/ha and40×10kg/ha in the double-cropping rice growing region. Differences from potential climatic to photo-thermal productivities in2000s indicated that full use of rainfall and reasonable irrigation was favorable to explore the rice potential productivity and ensure the rice increasing and high yields. These results were helpful for further exploration of technical approaches to enhancing rice yields, and for quantitative decision support for guiding national rice production and food security in China.
Quick and real-time monitoring of crop growth status based on remote sensing can support the decision-making on precision crop management. Based on growth estimating models in wheat and rice established by the authors'group, a RS image-based monitoring system was developed based on the Microsoft.NET framework using GDAL and GDI+as information processing methods and EM algorithm for classifying crop growth indices. This system realized the multiple functions as accessing the RS images with common formats, extracting crop information, inverting growth indices, clustering analysis, generating the thematic map and issuing the information with remote sensing technology. Several functions of the system were tested using the RS images at Fangqiang Farm, Jiangsu Province. The results showed that the system could effectively read general remote sensing images, invert the crop growth indices, classify the crop growth information based on the cluster models, interact with users for generating the thematic map of crop growth status, and issue the RS image information rapidly via internet. The present system has overcome the previous weakness that the ordinary users could not directly participate in the process of RS images analysis, and can help to monitor the crop growth condition and provide decision support for precision crop management at regional scale.
Model-and3S-based digital rice farming system was developed based on plentitude combination of rice growth simulation model, rice management knowledge model, rice monitoring model and3S technologies (GPS, GIS, and RS). Guided by the methodology of software component design and the technology of object oriented program, the system was constructed using the Browser/Server mode and based on the4-tier (the data tier, data access tier, business logic tier and presentation tier) architecture under the Microsoft.NET framework. By inputting the parameters of varieties, climate, soil and production management condition, the system provided users the information services of rice farming zone, cultivation strategy design, growth prediction, strategy evaluation, dynamic regulation, growth monitoring, precision management, productivity evaluation, management of disease, insect and weed, intelligent learning, system management and system help. The study expanded the application scale of models and was helpful to promote the informationization and modernization of the rice production management.
引文
Aggarwal PK, Kalra N, Chander S, et al. InfoCrop:A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description[J]. Agricultural Systems,2006,89:1-25.
Angell JK, Korshover J, Cotton GF. Variation in United States cloudiness and sunshine,1950-82[J]. Journal of Climate and Applied Meteorology,1984,23:752-761.
Ares JO, Valle HF, Olinuck JA. Exploring improved pesticide management in sub-tropical environments with GIS-supported fate modeling[J]. Agricultural Systems,2006,91(3):189-210..
Basso B, Bertocco M, Sartori L, et al. Analyzing the effects of climate variability on spatial pattern of yield in a maize-wheat-soybean rotation[J]. Europ. J. Agronomy,2007,26:82-91.
Basso B, Ritchie JT, Piece FJ, et al. Spatial validation of crop models for precision agriculture[J]. Agricultural System,2001,68(2):97-112.
Berg M, Driessen PM. Water uptake in crop growth models for land use systems analysis I:A review of approaches and their pedigrees[J]. Agriculture, Ecosystems and Environment,2002,92:21-36.
Boegh E, Thorsen M, Butts MB, et al. Incorporating remote sensing data in physically based distributed agro-hydrological modeling [J]. Journal of Hydrology,2004,287:279-299.
Bouman BA, Keulen H, Laar HH. The "School of de Wit" Crop Growth Simulation Models:A Pedigree and Historical Overview[J]. Agricultural Systems,1996,52(23):171-198.
Bouman BA. Linking physical remote sensing model with crop growth simulation models, applied for suger beet[J]. International Journal of Remote Sensing,1992a,13:2565-2581.
Burrough PA, McDonnell RA. Principles of Geographical Information System[M]. New York:Oxford University Press,1998.
Caldiza DO, Haverkortb AJ, Struik PC. Analysis of a complex crop production system in interdependent agro-ecological zones:a methodological approach for potatoes in Argentina[J]. Agricultural Systems,2002,73:297-311.
Cao WX, Moss DN. Modeling phasic development in wheat:an integration of physiological components [J]. Journal of Agriculture Science,1997,129:163-172.
Chavas DR, Izaurralde RC, Thomas AM, et al. Long-term climate change impacts on agricultural productivity in eastern China[J]. Agricultural and Forest Meteorology,2009,149:1118-1128.
DeWit CT. Photosynthesis of leaf canopies[M]. Wageningen, the Netherlands:Institute for Biological and Chemical Research on Field Crops and Herbage. Agriculture Research Report,1965,663-671.
Duncan WG. Loomis RS, Williams WA, et al. A model for simulating Photosynthesis in plant communities[J]. Hilgardia,1967,38:181-205.
Engel T, Hoogenboom G, Jones JW, et al. AEGIS/Win:a computer program for the application of crop simulation models across geographic areas[J]. Agronomy Journal,1997,89(6):919-928.
Evans LT, Fischer RA. Yield potential:Its definition, measurement and significance[J]. Crop Science, 1999,39:1544-1551.
FAO. Report on the Agro-ecological Zones Project[J]. Vol.1. Methodology and results for south and Central America. World Soil Resources Report,483. FAO, Rome,1981.
Gehring R, Baynes RE, Wang J, et al. A web-based decision support system to estimate extended withdrawal intervals[J]. Computers and Electronics in Agriculture,2004,44:145-151.
Goe WR, Kenney M. The political economy of the privatization of agricultural information:The case of the United States[J]. Agricultural Administration and Extension,1988,28(2):81-99.
Gotway CA. Comparison of Kriging and inverse distance methods for mapping soil parameters[J]. Soil Science,1996,60:1237-1247.
Gruerif M, Duke C. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation[J]. European Journal of Agronomy,1998,9:127-136.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF, Gessler PE. Splines-more than just a smooth interpolato[J]. Geoderma,1994, 62:45-67.
Hutchinson MF. Interpolation of rainfall data with thin plate smoothing splines[J]. Journal of Geographic Information and Decision Analysis,1998,2(2):139-167.
IPCC. Climate Change 2007:The Physical Science Basis [M]. Cambridge, UK:Cambridge University Press,2007.
Ittersum MK, Leffelaar PA, Keulen H, et al. On approaches and applications of the Wageningen crop models[J]. Europ. J. Agronomy,2003,18:201-234.
Jones D, Barnes EM. Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management[J]. Agricultural Systems,2000,65:137-158.
Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy,2003,18:235-265.
Keating BA, Carberry PS, Hammer GL, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy,2003,18:267-288.
Kersebaum KC, Lorenz K, Reuter HI. Operational use of agro-meteorological data and GIS to derive site specific nitrogen fertilizer recommendations based on the simulation of soil and crop growth processes[J]. Physics and Chemistry of the Earth,2005,30:59-67.
Kropff MJ, Laar HH, Ten BH. ORYZA1:A basic model for irrigated lowland rice production[J]. Manila:IRRI,1983.
Laslett GM. Comparision of several spatial prediction methods for soil pH[J]. Soil Science,1987, 38:325-341.
Li Jingtao, Zou Ping, Gu Lin, et al. A Computer System for Forecasting the Threshold Period for Crop Weed Control[J]. Agricultural Sciences in China 2008,7(11):1394-1402.
Li Xia, Yeh AG. Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS[J]. Landscape and Urban Planning,2004,69:335-354.
Loomis RS, Williams WA. Maximum crop productivity:An estimate[J]. Crop Science,1963,3(1):67-72.
Marietto V, Ventura F, Giovanna Fontana, et al. Wheat growth simulation and yield prediction with seasonal forecasts and a numerical model[J]. Agricultural and Forest Meteorology,2007, 147:71-79.
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J]. Agriculture, Ecosystems and Environment,2009,131:281-291.
Mathiyalagan V, Grunwald S, Reddy KR, et al. A WebGIS and geodatabase for Florida's wetlands[J]. Computers and Electronics in Agriculture,2005,47:69-75.
Morari F, Lugato E, Borin M. An integrated non-point source model-GIS system for selecting criteria of best management practices in the Po Valley, North Italy[J]. Agriculture, Ecosystems and Environment,2004,102:247-262.
Opio-Odongo JMA, Raza MR. The need for a specialised agricultural information system:A proposal[J]. Agricultural Administration,1981,8(2):137-145.
Sante Ⅰ, Crecente R, Miranda D. A GIS web-based tool for the management of the PGI potato of Galicia[J]. Computers and Electronics in Agriculture,2004,44:161-171.
Seelan SK, Laguett S, Casady GM, et al. Remote sensing applications for precision agriculture:A learning community approach[J]. Remote sensing of Environment,2003,88(1-2):157-169.
Sero'dio C, Cunha JB, Morais R, et al. A networked platform for agricultural management systems[J]. Computers and Electronics in Agriculture,2001,31:75-90.
Shackley S, Young P, Parkinson S, et al. Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling:Are GCMs the Best Tools[J] Climatic Change,1998,38(2):159-205.
Tan GX, Shibasaki R. Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration[J]. Ecological Modeling,2003,168:357-370.
Thorp KR, Dejonge KC, Kaleita AL, et al. Methodology for the use of DSSAT models for precision agriculture decision support [J]. Computers and Electronics in Agriculture,2008.
Timmermans BGH, Vos J, Stomph TJ. The development, validation and application of a crop growth model to assess the potential of Solanum sisymbriifolium as a trap crop for potato cyst nematodes in Europe[J]. Field Crops Research,2009,111:22-31.
Timsina J, Humphreys E. Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems:A review[J]. Agricultural Systems,2006,90:5-31.
Wahba G, Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation[J]. Monthly Weather Review,1980,108:1122-1145.
Wesseling JG, Feddes RA. Assessing crop water productivity from field to regional scale[J]. Agricultural water management,2006,86:30-39.
Wharton PS, Kirk WW, Baker KM, et al. A web-based interactive system for risk management of potato late blight in Michigan[J]. Computers and Electronics in Agriculture,2008,61:136-148.
Xiong W, Holman I, Conway D, et al. A crop model cross calibration for use in regional climate impacts studies[J]. Ecological Modelling,2008,213:365-380.
Xue LH, Cao WX, Luo WH, Dai TB, Zhu Y. Monitoring leafnitrogen status in rice with canopy spectral reflectance[J]. Agronomy Journal,2004,96(1):135-142.
Yun JI. Predicting regional rice production in South Korea using spatial data and crop-growth modeling [J]. Agricultural Systems,2003,77:23-38.
Zeng HH, Alarcon VJ, Kingery W, et al. A web-based simulation system for transport and retention of dissolved contaminants in soil[J]. Computers and Electronics in Agriculture,2002,33:105-120.
曹卫星,朱艳,田永超,等.数字农作技术研究的若干进展与发展方向[J].中国农业科学,2006,39(2)：281-288.
曹卫星,罗卫红.作物系统模拟及智能管理[M].高等教育出版社,2003,1：121-139.
曹卫星.农业信息学[M].北京：中国农业出版社,2005.
曹永华.农业决策支持系统研究综述[J].中国农业气象,1997,18(4)：46～50.
曾昭美,严中伟,章名立.近40年我国云、日照、温度及日较差的统计[J].科学通报,1993,38(5)：440-443.
常文渊,戴新刚,陈洪武.地质统计学在气象要素场插值的实例研究[J].地球物理学报,2004,47(6)：982-990.
陈国南.用迈阿密模型测算我国生物生产两的初步尝试[J].自然资源学报,1987,2(3)：270-278.
陈惠,王加义,林晶,等.基于GIS的福建省双季稻气候——土壤生产潜力[J].生态学杂志,2008,27(7)：1104-1108.
戴明宏,陶洪斌,廖树华,等.基于CERES-Maize模型的华北平原玉米生产潜力的估算与分析[J].农业工程学报,2008,24(4)：30-36.
戴新刚,陈洪武.地址统计学在气象要素场插值的实力研究[J].地球物理学报,2004,47(6)：983-990.
封志明.区域土地资源承载力研究模式雏议[J].自然资源学报,1990,5(3)：271-283.
冯利平,孙宁,刘荣花,等.我国华北冬小麦生产影响评估模型的研究[J].中国生态农业学报,2003,11(4)：73-76.
付修勇,张晶,王景平,等.基于栅格的中国一季稻生产潜力研究[J].中国人口,2008,18(4)： 99-103.
高亮之,金之庆,黄耀.水稻栽培计算机模拟优化决策系统(RCSODS)[M].北京：中国农业出版社,1992.
宫鹏,梅雪良,Biging GS,等.利用数字摄影测量探测橡树草原变化[J].遥感学报,1999,3(4)：285-289.
宫鹏,浦瑞良,郁彬.不同季相针叶树种高光谱数据识别分析[J].遥感学报,1998,8：211-217.
宫鹏.遥感科学与技术中的一些前沿问题[J].遥感学报,2009,10：13-23.
宫鹏.遥感生态测量学进展[J].自然资源学报,1999,14(4)：313-317.
辜智慧,史培军,陈晋.气象观测站点稀疏地区的降水插值方法探讨——以锡林郭勒盟为例[J].北京师范大学学报(自然科学版),2006,42(2)：204-208.
光继双,王唳华.3S技术在农田基础地图测绘与更新重的集成应用[J].农业工程学报,2003,19(3)：220-223.
侯光良,游橙才.用筑后模型估算我国植物气候生产力[J].自然资源学报,1990,5(1)：60-65.
候光良,刘允芳.我国气候生产潜力及其分区[J].自然资源,1985,19(3)：52-59.
候光良,游松才.用筑后模型估算我国植物气候生产力[J].自然资源学报,1990,5(1)：60-65.
侯军WebGIS技术在海洋监测信息集成中的应用研究[J].海洋技术,2005,24(4)：18-22.
胡惠萍.土壤属性的空间差异及其空间插值方法研究[J].湘潭师范学院学报(自然科学版),2001,11(1)：56-58.
黄秉维.中国农业生产潜力一光合潜力[C].地理集刊.北京：科学出版社,1985.
贾善刚.农业信息化与农业经济发展[J].农业经济问题,1999,2：48-51.
金之庆,葛道阔,高亮之,等.我国东部样带适应全球气候变化的若干粮食生产对策的模拟研究[J].中国农业科学,1998,31(4)：51-58.
金之庆,葛道阔,石春林,等.东北平原适应全球气候变化的若干粮食生产对策的模拟研究[J].作物学报,2002,28(1)：24-31.
金之庆,葛道阔,郑喜莲,等.评价全球气候变化对我国玉米生产的可能影响[J].作物学报,1996,22(5)：513-524.
金之庆,石春林.江淮平原小麦渍害预警系统(WWWS)[J].作物学报,2006,32(10)：1458-1465.
李存东,曹卫星,李旭,等.论作物信息技术及其发展战略[J].农业现代化研究,1998,19(1)：17-20.
李德仁.“三S”技术与农业发展[J].微电脑应用,1998,3：2～7.
李德仁.论RS, GPS与GIS集成的定义、理论与关键技术[J].遥感学报,1997,1(1)：63～68.
李建龙,蒋平,赵德华,等.3S技术在草地产量生态成因分析与农业资源估测中的应用研究[J].中国草地,2003,25(3)：15～23.
李剑萍,郑有飞,殷剑敏等.3S技术在晚稻长势监测中的综合应用研究[J].南京气象学院学报,2001,24(1)：106-112.
李培军,张维峰,郭洪涛,等.气象资料三维化技术中的插值问题[J].气象科学,2005,47(6)：617-625.
李三爱,居辉,池宝亮.作物生产潜力研究进展[J].中国农业气象,2005,26(2)：106-111.
李晓燕,张树文.吉林省德惠市土壤速效钾的空间变异及不同插值方法的比较[J].水土保持学报,2004,18(4)：97-100.
梁荣欣,张瑞雪.水稻的气候土壤生产潜力估算[J].自然资源,1984,(2)：68-73.
凌辉,武伟,王润,等.小尺度下土壤重金属铬含量的空间插值方法比较研究[J].西南大学学报(自然科学版),2007,29(11)：93-99.
刘建栋,王馥棠,于强,等.华北地区农业干旱预测模型及其应用研究[J].应用气象学报,2003,14(5)：593-604.
刘建栋,周秀骥,于强.FAO生产潜力模型中基本参数的修正[J].自然资源学报,2001,16(3)：240-247.
刘晓军,朱艳,姚霞.基于WebGIS的农业空间信息管理及辅助决策系统[J].农业工程学报,2006,22(5)：125-129.
刘雪琴,李宁,温玉婷,等.内蒙中西部土壤水分统计插值模型试验[J].气象科学,2009,29(12)：734-741.
刘宇,陈泮勤,张稳,等.一种地面气温的空间插值方法及其误差分析[J].大气科学,2006,30(1)：146-152
刘志红,Me Vicar TR, Li LT,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科技大学学报(自然科学版),2008,36(10)：227-234
刘志红,李领涛,McVicar TR,等.专用气候数据空间插值软件ANUSPLIN及其应用[J].气象,2008,34(2)：92-100
龙嘉,于慧梅.加拿大现代农业信息技术的应用与管理[J].世界农业,2003,3：29-32.
陆忠艳,袁子鹏,蔡福,等.基于GIS的气温和降水推算方法研究[J].气象科技,2008,36(4)：389－395.
罗群英,林而达.农业技术转移决策支持系统(DSSAT)新进展[J].气象,1996,22(12)：10-13.
骆世明,彭少麟.农业生态系统分析[M].广州：广东科技出版社,1996.
马帅,陈印军,郭淑敏.作物生产潜力模型研究进展[J].中国农学通报,2006,22(1)：349-352.
潘瑜春,王纪华,赵春江.基于GIS和RS的应用分析系统集成研究[J].计算机工程,2005,31(15)：44-46.
潘瑜春,赵春江.地理信息技术在精准农业中的应用[J].农业工程学报,2003,19(4)：1-5.
庞夙,李廷轩,王永东,等.县域农田土壤铜含量的协同克里格插值及采样数量优化[J].中国农业科学,2009,42(8)：2828-2836.
彭汉艮,姚霞,朱艳,等.种植制度知识模型系统的设计与实现[J].南京农业大学学报,2005,28(2)：125-128.
浦瑞良,宫鹏.森林生物化学与CASI高光谱分辨率遥感数据的相关分析[J].遥感学报,1995,1(2)：115-123.
戚昌瀚,殷新佑,刘桃菊,等.(RICAM 1.3双季稻高产管理决策支持系统》在小流域双季稻区的“水稻苗情预报”研究[J].江西农业大学学报,2000,22(4)：482-484.
任国玉,徐铭志,初子莹,等.近54年中国地面气温变化[J].气象学报,2005,63(6)：942-956.
沙宗尧,边馥菩.3S技术的农业应用与精细农业工程[J].测绘通报,2003,6：29-31.
帅细强,王石立,马玉平,等.基于ORYZA2000模型的湘赣双季稻气候生产潜力[J].中国农业气象,2009,30(4)：575－581.
苏希.农业信息技术的发展与应用前景[J].计算机与农业,2003,10：6-9.
孙希华.基于GIS和RS的章丘市农业自然资源质量综合评价[J].山东师范大学学报(自然科学版),2004,19(1)：48-59.
汤国安,赵牡丹.地理信息系统[M].北京：科学出版社,2002.
田永超,朱艳,曹卫星,等.利用冠层反射光谱和叶片SPAD值预测小麦籽粒蛋白质和淀粉的积累[J].中国农业科学,2004,37：808-813.
田永超,朱艳,曹卫星,等.小麦冠层反射光谱与植株水分状况的关系[J].应用生态学报,2004,15：2072-2076.
田永超,朱艳,曹卫星,等.利用冠层反射光谱预测小麦叶片的糖氮量及糖氮比[J].作物学报,2005,31：355-360.
汪懋华.信息技术革命与持续农业发展.http://www.agri.ac.cn.
王纯枝,宇振荣,辛景峰.基于遥感和作物生长模型的作物产量差估测[J].农业工程学报,2005, 21(7)：84-89.
王霓虹.基于Web与3S技术的森林防火智能决策支持系统的研究[J].林业科学,2002,38(3)：114－119.
王人潮,黄敬峰,史舟,等.论农业信息科学的形成与发展[J].浙江大学学报(农业与生命科学版),2003,29(4)：355-360.
王人潮,史舟.农业信息科学与农业信息技术[M].北京：中国农业出版社,2003.
王向东,张建平,马海莲,等.作物模拟模型的研究概况及展望[J].河北农业大学学报,2003,26增刊：20～23.
王晓云,郭文利,奚文,等.利用“3S”技术进行北京地区土壤水分监测[J].应用气象学报,2002,13(4)：422-429.
吴文玉,马晓群.基于GIS的安徽省气温数据栅格化方法研究[J].中国农学通报,2009,25(02)：263-267
吴秀芹,张洪岩,李瑞改,等.ArcGIS 9——地理信息系统的应用和实践[M].北京：清华出版社,2007.
谢云,王晓岚,林燕,等.近40年中国东部地区夏秋粮作物农业气候生产潜力时空变化[J].资源科学,2003,25(2)：7－13.
徐超,吴大千,张治国.山东省多年气象要素空间插值方法比较研究[J].山东大学学报(理学版),2008,43(3)：1-5.
徐新良,刘纪远,曹明奎,等.近期气候波动与LUCC过程对东北农田生产潜力的影响[J].地理科学,2007,27(3)：318-324.
薛利红,曹卫星,罗卫红,等.光谱植被指数与水稻叶面积指数相关性的研究[J].植物生态学报,2004,28(1)：47-52.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163-169.
杨京平,王兆骞.作物生长模拟模型及其应用[J].应用生态学报,1999,10(4)：501-505.
杨宁,廖桂平.作物生长模拟研究进展[J].作物研究,2002,5：255～257.
游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1)：140-144.
章云兰,郑江平,陈振宇.农业信息技术现状分析与我国的发展对策[J].浙江大学学报(农业与生命科学版),2001,27(2)：229-232.
赵哲远,吴妍,艾亮辉.论RS与GIS集成技术支持下的土地资源管理手段现代化[J].管理现代化,2002,2：32-35.
郑小波,罗宇翔,于飞,等.西南复杂山地农业气候要素空间插值方法比较[J].中国农业气象,2008,29(4)：458-462.
周兆德.海南岛水稻气候生产潜力和人口承载量的估算[J].自然资源学报,1989,4(1)：46-53.
周治国,曹卫星,朱艳.基于GIS的作物生产管理信息系统[J].农业工程学报,2005,21(1)：114-118.
周治国,孟亚利,曹卫星.基于知识模型和GIS的作物生产潜力评价[J].中国农业科学,2005,38(6)：1142-1147.
朱求安,张万昌,赵登忠.基于PRISM和泰森多边形的地形要素日降水量空间插值研究[J].地理科学,2005,25(2)：233-238.
朱艳,曹卫星,王绍华,等.小麦栽培管理知识模拟系统的设计与实现[J].南京农业大学学报,2002,25(3)：12～16.
朱艳,曹卫星,田永超,等.作物管理知识模型系统设计与开发框架研究[J].农业工程学报,2004,20(4)：138～142.
朱艳.基于知识模型的小麦管理决策支持系统的研究[D].南京：南京农业大学,2003.
庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5)：605-615.
邹源.不同地貌土壤属性空间变异性研究[D].扬州：扬州大学,2006.
Evjen B, Hanselman S, Muhanmmad F. Professional ASP.NET 2.0[M]. John Wiley and Sones,2006.
Nagel C, Evjen B, Glynn J. Professional C# 2005[M]. John Wiley and Sones,2006.
Graham J, Newman G, Jarnevich C, et al. A global organism detection and monitoring system for non-native species[J]. Ecological Informatics,2007,2(2):177-183.
Microsoft Corporation. Visual Studio 2005 MSDN Library[M]. Microsoft Corporation,2005.
Rao M, Fan GL, Thomas J, et al. A web-based GIS decision support system for managing and planning USDA's conservation reserve program (CRP)[J]. Environmental Modeling & Software,2007,22(9): 1270-1280.
Raup B, Kaab A, Kargel J S, et al. Remote sensing and GIS technology in the global land ice measurements from space (GLIMS) project[J]. Computers and Geosciences,2007,33(26):104-125.
Serranoa S, Jimenez-Hornero FJ, Raveb EG, et al. GIS design application for "Sierra Morena Honey" designation of origin[J]. Computers and Electronics in Agriculture,2008,64(2):307-317.
北京超图地理信息技术有限公司.理解SuperMap IS.NET[M].北京超图地理信息技术有限公司,2006.
曹静.精确农作管理模型与决策支持系统的研究[D].南京：南京农业大学,2008.
单英杰.基于GIS和模型的种植设计系统研究[D].南京：南京农业大学,2007.
董岩,耿杰,谭景信.基于OGR的雷达终端显示系统的设计与实现[J].计算机工程与设计,2009,30(19)：4560-4563
冯伟.基于高光谱遥感的小麦氮素营养及生长指标监测研究[D].南京：南京农业大学,2007.
花登峰.基于构件化生长模型的作物管理决策支持系统[D].南京：南京农业大学,2007.
蒋红军.基于模型和3S的数字麦作支持系统研究[D].南京：南京农业大学,2007.
康金春.中间件技术在空间数据共享中的应用[J].地理空间信息,2009,7(5)：35-37
李芳,邬群勇,汪小钦.基于GeoRaster的多源遥感数据存储研究[J].测绘科学,2009,34(3)：150－151
李卫国.水稻生长模拟与决策支持系统的研究[D].南京：南京农业大学,2005.
李映雪.基于冠层反射光谱的小麦氮素营养与籽粒品质监测[D].南京：南京农业大学,2005.
刘小军.基于WebGIS和模型的农业空间信息管理系统研究[D].南京：南京农业大学,2005.
潘洁.小麦生长模拟与决策支持系统的研究[D].南京：南京农业大学,2005.
汤泉,牛铮.基于IDL与ENVI二次开发的遥感系统开发方法[J].计算机应用,2008,28(6)：270-276
田永超.基于冠层反射光谱的水稻水分及稻麦生长监测[D].南京：南京农业大学,2003.
严定春.水稻管理知识模型与决策支持系统的研究[[)].南京：南京农业大学,2004.
叶宏宝.稻麦生产的水肥动态模拟及管理决策支持系统研究[D].南京：南京农业大学,2005.
叶宏宝.基于模型与GIS的水稻生产力预测预警技术研究[D].南京：南京农业大学,2008.
周冬琴.基于冠层反射光谱的水稻氮素营养与籽粒品质监测[D].南京：南京农业大学,2007.
朱艳.基于知识模型的小麦管理决策支持系统的研究[D].南京：南京农业大学,2003.
朱洪芬.基于遥感的作物生长监测与诊断系统研究[D].南京：南京农业大学,2008.
朱元励.基于遥感的作物生长监测与诊断系统研究[D].南京：南京农业大学,2009.
Angell JK, Korshover J, Cotton GF. Variation in United States cloudiness and sunshine,1950-82[J]. Journal of Climate and Applied Meteorology,1984,23:752-761.
Burrough PA, McDonnell RA. Principles of Geographical Information System[M]. New York:Oxford University Press,1998.
Collins FC, Bolstad PV. A comparison of spatial interpolation techniques in temperature estimation[EB/OL]. (2004-12-31) [2009-09-08]. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/collins_fred/collins.html.
Consultative Group on International Agricultural Research. The CGIAR Consortium for Spatial Information[EB/OL]. (2008-08-19) [2009-12-09].http://srtm.csi.cgiar.org/selection/inputCoord.asp.
Hartkamp D, Beurs KD, Stein A, et al. Interpolation Techniques for Climate Variables[M]. Mexico: International Maize and Wheat Improvement Center,1999.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF, Gessler PE. Splines-more than just a smooth interpolator[J]. Geoderma,1994, 62:45-67.
Hutchinson MF. ANUSPLIN version 4.3 User Guide[M]. Canberra:Center for Resource and Environment Studies, Australia National University,2004.
Hutchinson MF. Interpolation of rainfall data with thin plate smoothing splines[J]. Journal of Geographic Information and Decision Analysis,1998,2(2):139-167.
Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy,2003,18:235-265.
Kaiser DP. Two Long-term Instrumental Climatic Databases of the People's Republic of China[R]. Tennessee:Oak Ridge National Laboratory,1991.
Keating BA, Carberry PS, Hammer GL, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy,2003,18:267-288.
Krige DG. A statistical approach to some mine valuations problems at the Witwatersrand[J]. Journal of the Chemical, Metallurgical and Mining Society of South Africa,1951,52:119-138.
Lam NS. Spatial interpolation method:a review[J]. The American Cartographer,1983,10(2):129-149.
Wahba G, Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation[J]. Monthly Weather Review,1980,108:1122-1145.
曹宏鑫,金之庆,石春林,等.中国作物模型系列的研究与应用[J].农业网络信息,2006(5)：45-48.
曾昭美,严中伟,章名立.近40年我国云、日照、温度及日较差的统计[J].科学通报,1993,38(5)：440-443.
戴新刚,陈洪武.地质统计学在气象要素场插值的实例研究[J].地球物理学报,2004,47(6)：983-990.
辜智慧,史培军,陈晋.气象观测站点稀疏地区的降水插值方法探讨——以锡林郭勒盟为例[J].北京师范大学学报(自然科学版),2006,42(2)：204-208.
何红艳,郭志华,肖国发.降水空间插值技术的研究进展[J].生态学杂志,2005,24(10)：1187-1191.
刘宇,陈泮勤,张稳,等.一种地面气温的空间插值方法及其误差分析[J].大气科学,2006,30(1)：146－152.
刘志红.Mc Vicar TR,李领涛,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科 技大学学报(自然科学版),2008,36(10)：227-234.
刘志红,李领涛,Mc Vicar TR,等.专用气候数据空间插值软件ANUSPLIN及其应用[J].气象,2008,34(2)：92－100.
陆忠艳,袁子鹏,蔡福,等.基于GIS的气温和降水推算方法研究[J].气象科技,2008,36(4)：389-395.
汤国安,赵牡丹.地理信息系统[M].北京：科学出版社,2002.
吴文玉,马晓群.基于GIS的安徽省气温数据栅格化方法研究[J].中国农学通报,2009,25(02)：263-267.
吴秀芹,张洪岩,李瑞改,等.地理信息系统应用与实践——ArcGIS 9[M].北京：清华出版社,2007.
徐超,吴大千,张治国.山东省多年气象要素空间插值方法比较研究[J].山东大学学报(理学版),2008,43(3)：1-5.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163－169.
游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1)：140-144.
郑小波,罗宇翔,于飞,等.西南复杂山地农业气候要素空间插值方法比较[J].中国农业气象,2008,29(4)：458-462.
朱求安,张万昌,赵登忠.基于PRISM和泰森多边形的地形要素日降水量空间插值研究[J].地理科学,2005,25(2)：233-238.
庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5)：605-615.
Consultative Group on International Agricultural Research. The CGIAR Consortium for Spatial Information[EB/OL]. (2008-08-19) [2009-12-09].http://srtm.csi.cgiar.org/selection/inputCoord.asp.
He YQ, Wilfred HT, Zhang ZL, et al. Asynchronous Holocene climatic change across China:a review[J]. Quaternary Research,2004,61(1):52-63.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF. ANUSPLIN Version 4.3 User Guide[C]. Canberra:Center for Resource and Environment Studies, Australia National University,2004.
IPCC. Climate Change 2007:The Physical Science Basis [M]. Cambridge, UK:Cambridge University Press,2007.
Mohammed AR, Tarpley L. High nighttime temperatures affect rice productivity through altered pollen germination and spikelet fertility[J]. Agricultural and Forest Meteorology,2009,149:999-1008.
Peng SB, Huang JL, Sheehy JE, et al. Rice yields decline with higher night temperature from global warming[J]. Proceedings of the National Academy of Sciences of the USA,2004,101(27): 9971-9975.
Ryan MG Effects of climate change on plant respiration[J]. Ecological Application,1991,1(2):157-167.
Tao FL, Yokozawa M, Xu YL, et al. Climate changes and trends in phenology and yields of field crops in China,1981-2000[J]. Agricultural and Forest Meteorology,2006,138:82-92.
Yin XY, Kropff MJ, Goudriaan J. Differential effects of day and night temperature on development to flowering in rice[J]. Annals of Botany,1996,77(3):203-213.
Yin XY, Kropff MJ, Goudriaan J. The effects of temperature on leaf appearance in rice[J]. Annals of Botany,1996,77(3):215-221.
陈隆勋,邵永宁,张清芳等.近四十年我国气候变化的初步分析[J].应用气象学报,1991,2(2)：164-174.
陈隆勋,周秀骥,李维亮等.中国近80年来气候变化特征及其形成机制[J].气象学报,2004,62(5)：634-646.
陈隆勋,朱文琴,王文等.中国近45年来气候变化的研究[J].气象学报,1998,56(3)：257-271.
陈玉民,郭国兴,王光兴等.中国主要作物需水量与灌溉[M].北京：水利电力出版社,1995,300-350.
刁操铨等.作物栽培学各论(南方本)[M].北京：中国农业出版社,1994,78-79.
黄丕生.早稻品种的生长发育与热量条件关系的分析[J].南京农业大学学报,1994,2：22-31.
刘志红,McVicar TR,李领涛,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科技大学学报(自然科学版),2008,36(10)：227-234.
卢爱刚,庞德谦,康世明等.全球变暖背景下中国气温时空差异响应[J].干旱区资源与环境,2009,23(8)：61－65.
梅方权,吴宪章,姚长溪,等.中国水稻种植区划[J].中国水稻科学,1988,2(3)：97-110.
任国玉,徐铭志,初子莹,等.近50年来中国地面气候变化基本特征[J].气象学报,2005,63(6)：942-956.
唐国力,任国玉.近百年中国地表气温变化趋势的再分析[J].气候与环境研究,2005,10(4)： 791-798.
谢立勇,林而达.二氧化碳浓度增高对稻、麦品质影响研究进展[J].应用生态学报,2007,18(3)：659-664.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163-169.
严定春,朱艳,曹卫星,等.水稻群体生长指标动态的知识模型研究[J].中国农业科学,2005,38(1)：38-44.
张淑梅,张建明,李丁鲁,等.高抗性淀粉粳稻新品系稻米淀粉链长分布与主要品质特性差异[J].中国农业科学,2009,42(6)：2237-2243.
中国主要农作物需水量等值线图协作组.中国主要农作物需水量等值线图研究[M].北京：中国农业科技出版社,1993,15-17.
中华人民共和国农业部.中国水稻高产栽培模式图[EB/OL]. (2009-04-19) [2009-12-09]. http://www.agri.gov.cn/ztzl/ql/jsms/P020090410579290158332.doc.
左洪超,吕世华,胡隐樵.中国近50年气温及降水量的变化趋势分析[J].高原气象,2004,23(2)：238-244.
Chavas DR, Izaurralde RC, Thomas AM, et al. Long-term climate change impacts on agricultural productivity in eastern China[J]. Agricultural and Forest Meteorology,2009,149:1118-1128.
Consultative Group on International Agricultural Research. The CGIAR Consortium for Spatial Information[EB/OL]. (2008-08-19) [2009-12-09].http://srtm.csi.cgiar.org/selection/inputCoord.asp.
Dirceu TC, Robert FD. An energy-crop growth variable and temperature function for predicting corn growth and development:planting to silking[J]. Agronomy Journal,1980,72:503-516.
He YQ, Wilfred HT, Zhang ZL, et al. Asynchronous Holocene climatic change across China:a review[J]. Quaternary Research,2004,61(1):52-63.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF. ANUSPLIN version 4.3 user guide[M]. Canberra:Center for Resource and Environment Studies, Australia National University,2004.
Loomis RS, Williams WA. Maximum crop productivity:an estimate[J]. Crop Science,1963,3(1):67-72.
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J]. Agriculture, Ecosystems and Environment,2009,131:281-291.
Richard WK. Techniques for estimating uncertainty in climate change scenarios and impact studies[J]. Climate Research,2002,20:167-185
Shackley S, Young P, Parkinson S, et al. Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling:Are GCMs the Best Tools[J]. Climatic Change,1998,38(2):159-205
Tao FL, Yokozawa M, Xu YL, et al. Climate changes and trends in phenology and yields of field crops in China,1981-2000[J]. Agricultural and Forest Meteorology,2006,138:82-92.
Thomas A. Climate change and potential agricultural productivity in China[J]. Erdkunde,2006, 60:157-172
曹卫星,罗卫红.作物系统模拟及智能管理[M].北京：华文出版社,2000：111－112.
曹卫星,朱艳,汤亮,等.数字农作系统[M].北京：科学出版社,2008：101-103.
曹卫星,朱艳.作物管理知识模型[M].北京：中国农业出版社,2004：49-51.
陈惠,王加义,林晶,等.基于GIS的福建省双季稻气候——土壤生产潜力[J].生态学杂志,2008,27(7)：1104－1108.
陈隆勋,邵永宁,张清芳,等.近四十年我国气候变化的初步分析[J].应用气象学报,1991,2(2)：164-174.
陈隆勋,周秀骥,李维亮等.中国近80年来气候变化特征及其形成机制[J].气象学报,2004,62(5)：634-646.
陈隆勋,朱文琴,王文等.中国近45年来气候变化的研究[J].气象学报,1998,56(3)：257-271.
陈玉民,郭国兴,王光兴等.中国主要作物需水量与灌溉[M].北京：水利电力出版社,1995, 300-350.
戴明宏,陶洪斌,廖树华,等.基于CERES-Maize模型的华北平原玉米生产潜力的估算与分析[J].农业工程学报,2008,24(4)：30-36.
丁一汇,林而达,何建坤,等.中国气候变化——科学、影响、适应及对策研究[M].北京：中国环境科学出版社,2009：30-31.
付修勇,张晶,王景平,等.基于栅格的中国一季稻生产潜力研究[J].中国人口,2008,18(4)：99-103.
候光良,刘允芳.我国气候生产潜力及其分区[J].自然资源,1985,19(3)：52-59.
黄秉维.自然条件与作物生产：光合潜力[M].自然地理综合工作六十年——黄秉维文集.北京：科学出版社,1993：183－196.
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.
金之庆,葛道阔,高亮之,等.我国东部样带适应全球气候变化的若干粮食生产对策的模拟研究[J].中国农业科学,1998,31(4)：51-58.
金之庆,葛道阔,石春林,等.东北平原适应全球气候变化的若干粮食生产对策的模拟研究[J].作物学报,2002,28(1)：24-31.
金之庆,葛道阔,郑喜莲,等.评价全球气候变化对我国玉米生产的可能影响[J].作物学报,1996,22(5)：513-524.
金之庆,石春林.江淮平原小麦渍害预警系统(WWWS)[J].作物学报,2006,32(10)：1458－1465.
刘志红,McVicar TR,李领涛,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科技大学学报(自然科学版),2008,36(10)：227-234.
龙斯玉.江苏省农业气候资源生产潜力及区划的研究[J].地理科学,1985,5(3)：218-226.
梅方权,吴宪章,姚长溪,等.中国水稻种植区划[J].中国水稻科学,1988,2(3)：97-110.
任国玉,徐铭志,初子莹,等.近50年来中国地面气候变化基本特征[J].气象学报,2005,63(6)：942-956.
孙华生,黄敬峰,李波,等.中国水稻遥感信息获取区划研究[J].中国农业科学,2008,41(12)：4039-4047.
谢立勇,林而达.二氧化碳浓度增高对稻、麦品质影响研究进展[J].应用生态学报,2007,18(3)：659-664.
谢云,王晓岚,林燕,等.近40年中国东部地区夏秋粮作物农业气候生产潜力时空变化[J].资源科学,2003,25(2)：7－13.
严定春,朱艳,曹卫星,等.水稻群体生长指标动态的知识模型研究[J].中国农业科学,2005,38(1)：38-44.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2): 163-169.
张晋科,张凤荣,张琳,等.中国耕地的粮食生产能力与粮食产量对比研究[J].中国农业科学,2006,39(11)：2278-2285
张淑梅,张建明,李丁鲁,等.高抗性淀粉粳稻新品系稻米淀粉链长分布与主要品质特性差异[J].中国农业科学,2009,42(6)：2237-2243.
中国主要农作物需水量等值线图协作组.中国主要农作物需水量等值线图研究[M].北京：中国农业科技出版社,1993,3-40.
中华人民共和国农业部.中国水稻高产栽培模式图[EB/OL]. (2009-04-19) [2009-12-09]. http://www.agri.gov.cn/ztzl/ql/jsms/P020090410579290158332.doc.
周兆德.海南岛水稻气候生产潜力和人口承载量的估算[J].自然资源学报,1989,4(1)：46-53.
周治国,孟亚利,曹卫星.基于GIS的作物生产管理信息系统[J].中国农业科学,2005,38(6)：1142-1147.
朱艳,曹卫星,田永超,等.作物管理知识模型系统设计与开发框架研究[J].农业工程学报,2004,20(4)：138～142.
左洪超,吕世华,胡隐樵.中国近50年气温及降水量的变化趋势分析[J].高原气象,2004,23(2)：238-244.
Bandyopadhyay S, Maulik U. Genetic clustering for automatic evolution of clusters and application to image classification[J]. IEEE Pattern Recognition,2002,35(6):1197-1208.
Cheng Hengda, Shi Xianjun, Min Rui, et al. Approaches for automated detection and classification of masses in mammograms[J]. Pattern Recognition,2006,39(4):646-668.
Chu Yianshu, Tseng Shianshyong, Tsai Yujie, et al. An intelligent questionnaire analysis expert system[J]. Expert Systems with Applications,2009,36(2):2699—2710.
GDAL. Geospatial data abstraction library, http://www.gdal.org/,2008-11-04/2009-08-21.
Graham J, Newman G, Jarnevich C, et al. A global organism detection and monitoring system for non-native species[J]. Ecological Informatics,2007,2(2):177-183.
Guo Ping, Jia Yunde, Lyu M R. A study of regularized gaussian classifier in high-dimension small sample set case based on MDL Principle with application to spectrum recognition[J]. Pattern Recognition,2008,41(9):2842-2854.
Hu Tianming, Sung S Y. A hybrid EM approach to spatial clustering[J]. Computational Statistics & Data Analysis,2006,50(5):1188-1025.
Li Ying, Vodacek A, Zhu Yushan. An automatic statistical segmentation algorithm for extraction of fire and smoke regions[J]. Remote Sensing of Environment,2007,108(2):171 - 178.
Melomed E, Gorbach I, Berger A, et al. Microsoft SQL Server 2005 Analysis Services[M]. Indiana: Sams Publishing,2007.
Microsoft. Microsoft Clustering Algorithm Technical Reference[EB/OL]. http://msdn.microsoft.com/ zh-cn/library/cc280445.aspx,2009-7-30/2009-08-21.
Microsoft. Programming AMO Data Mining Objects[EB/OL]. http://technet.microsoft.com/en-us/ library/ms345087.aspx,2009-7-30/2009-08-21.
Potdar M B, Geoge H R, Petter M J. Sorghum yield modeling based on crop growth parameters determined from visible and near-IR channel NOAA AVHRR data[J]. International Journal of Remote Sensing,1993,14(5):895-905.
Rao M, Fan Guoliang, Thomas J, et al. A web-based GIS decision support system for managing and planning USDA's conservation reserve program (CRP)[J]. Environmental Modeling & Software, 2007,22(9):1270-1280.
Raup B, Kaab A, Kargel J S, et al. Remote sensing and GIS technology in the global land ice measurements from space (GLIMS) project[J]. Computers and Geosciences,2007,33(26):104-125.
Raup B, Racoviteanu A, Khalsa S J S, et al. The GLIMS geospatial glacier database:A new tool for studying glacier change[J]. Global and Planetary Change,2007,56:101-110.
Serranoa S, Jim6nez-Hornero F J, Gutierrez de Rav6b E, et al. GIS design application for "Sierra Morena Honey" designation of origin[J]. Computers and Electronics in Agriculture,2008,64(2): 307-317.
Tang Zhaohui, MacLennan J. Data mining with SQL Server 2005[M]. New York:John Wiley and Sons Inc,2005.
安琼,杨邦杰,焦险峰.作物遥感识别中的数据挖掘[J].农业工程学报,2007,23(8)：181－186.
居红云,张俊本,李朝峰,等.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究,2007,24(11)：318－320.
鞠昌华.利用地-空高光谱遥感监测小麦氮素状况与生长特征[D].南京农业大学农学院,2008.
李映雪,朱艳,曹卫星.不同施氮条件下小麦冠层的高光谱和多光谱反射特征[J].麦类作物学报,2006,26(2)：103－108.
刘扬,周清波,刘佳,等.基于遥感和WebGIS的冬小麦估计支持系统[J].中国农业科学,2008,41(10)：3371－3375.
骆剑承,周成虎,梁怡,等.有限混合密度模型及遥感影像EM聚类算法[J].中国图象图形学报,2002,7A(4)：336－340.
蒙继华,吴炳方,李强子,等.运行化的农作物长势遥感监测系统[J].高技术通讯,2007,17(1)：94--99.
潘建刚,赵文吉,宫辉力.遥感图像分类方法的研究[J].首都师范大学学报：自然科学版,2004,25(3)：86－91.
徐新刚,吴炳方,蒙继华,等.农作物单产预测系统的设计和实现[J].计算机工程,2008,34(9)：283-285.
徐怡,李龙澍,李学俊.基于粗糙熵和K-均值聚类算法的图像分割[J].华东理工大学学报：自然科学版,2007,33(2)：255－258.
薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学,2003,36(7)：807－812.
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.
薛利红,朱艳,张宪,等.利用冠层反射光谱预测小麦籽粒品质指标的研究[J].作物学报,2004,30(10)：1036－1041.
周小成,汪小钦.遥感影像数据挖掘研究进展[J].遥感信息,2005,3：58－62.
Delecolle R, Mass SJ, Guerif M. Remote Sensing And Crop Production Models[J]. Journal Of Photogrammetry And Remote Sensing,1992,47:145-161.
Engel T, Hoogenboom G, Jones JW, et al.1997. AEGIS/WIN:A computer program for the application of crop simulation models across geographic areas[J]. Agronomy J.1997,89:919-928.
Er-Raki S, Chehbouni A, Guemouria N, et al. Combinging FAO-56 Model and Ground-Based Remote Sensing to Estimate Water Consumptions of Wheat Crop in A Semi-Arid Region[J]. Agricultural Water Management,2007,87(1):41-54.
Microsoft. "Visual Studio 2005 MSDN Library[M]. Microsoft Corporation,2005.
Raymond EE. Jongschaap. Run-time Calibration of Simulation Models by Integrating Remote Sensing Estimates of Leaf Area Index and Canopy Nitrogen[J]. European Journal of Agronomy.2006, 24(4):316-324.
StSckle CO, Donatelli M, Nelson R. CropSyst, a cropping systems simulation model[J]. European Journal of Agronomy,2000,18:289-307.
Timsina J, Humphreys E. Performance of CERES-Rice and CERES-Wheat Models in Rice-Wheat Systems:A Review [J]. Agricultural Systems,2006,90:5-31.
北京超图地理信息技术有限公司.理解SuperMap IS.NET[M].北京超图地理信息技术有限公司,2006.
曹卫星,朱艳,田永超,等.数字农作技术研究的若干进展与发展方向[J].中国农业科学,2006,39(2)：281-288.
程明华,陈建平.3S技术在农业中的应用[J].山西农业科学,2006,34(2)：15-17.
郭银巧,赵传德,刘小军,等.基于模型和GIS技术的数字棉作系统的设计与实现[J].农业工程学报,2008,24(11)：139-144.
蒋红军,刘小军,汤亮,等.基于模型和3S技术的数字麦作系统的设计与实现[J].麦类作物学报,2007,27(5)：908-913.
姜晓剑,刘小军,田永超,等.基于遥感影像的作物生长监测系统的设计与实现[J].农业工程学报,2010,26(3)：156－163.
李卫国,李秉柏,石春林.基于模型和遥感的水稻长势监测研究进展[J].中国农学通报,2006,22(9)：457-461.
林忠辉,莫兴国,项月琴.作物生长模型研究综述[J].作物学报,2003,29(5)：750-758.
刘小军,朱艳,姚霞,等.基于WebGIS的农田生产环境质量评价系统研究[J].中国农业科学,2005,38(3)：551-557.
刘小军,朱艳,姚霞,等.基于WebGIS的农业空间信息管理及辅助决策系统[J].农业工程学报,2006,22(5)：125-129.
马玉平,王石立,张黎,等.基于遥感信息的华北冬小麦区域生长模型及模拟研究[J].气象学报,2005,63(2)：204-215.
潘瑜春,王纪华,赵春江,等.基于网络GIS的作物品质监测与调优栽培系统[J].农业工程学报,2004,20(6)：120-123.
沙宗尧,边馥苓.“3S"技术的农业应用与精细农业工程[J].测绘通报,2003,(6)：29-31.
王纯枝,宇振荣,辛景峰,等.基于遥感和作物生长模型的作物产量差估测[J].农业工程学报,2005,21(7)：84-89.
维埃拉R. SQL Server 2005高级程序设计[M].董明,译.北京：人民邮电出版社,2008.
沃特S,波尔顿C. SQL Server 2005'性能调优[M].齐宁,董泽惠,译.北京：清华大学出版社,2009.
谢云,James R Kiniry.国外作物生长模型发展综述[J].作物学报,2002,28(2)：190-195.
张宏森,朱征宇.四层B/S结构及解决方案[J].计算机应用研究,2002,(9)：20-22.
赵艳霞,秦军,周秀骥.遥感信息与棉花模型相结合反演模型初始值和参数的方法研究[J].棉花学报,2005,17(5)：280-284.
周留根,周治国,曹卫星,等.基于知识模型和GIS的棉花生产潜力评价系统[J].棉花学报,2005,17(2)：112－121.
周治国,孟亚利,曹卫星.基于知识模型和GIS的作物生产潜力评价[J].中国农业科学,2005,38(6)：1142-1147.
朱艳,曹卫星,王其猛,等.基于知识模型和生长模型的小麦管理决策支持系统[J].中国农业科学,2004,37(6)：814-820.
Burrough PA, McDonnell RA. Principles of Geographical Information System[M]. New York:Oxford University Press,1998.
Chavas DR, Izaurralde RC, Thomas AM, et al. Long-term climate change impacts on agricultural productivity in eastern China[J]. Agricultural and Forest Meteorology,2009,149:1118-1128.
Collins FC, Bolstad PV. A comparison of spatial interpolation techniques in temperature estimation [EB/OL]. (2004-12-31) [2009-09-08]. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/ sf_papers/collins_fred/collins.html.
Engel T, Hoogenboom G, Jones JW, et al.1997. AEGIS/WIN:A computer program for the application of crop simulation models across geographic areas[J]. Agronomy J.1997,89:919-928.
Guo Ping, Jia Yunde, Lyu M R. A study of regularized gaussian classifier in high-dimension small sample set case based on MDL Principle with application to spectrum recognition[J]. Pattern Recognition,2008,41(9):2842-2854.
Hartkamp D, Beurs KD, Stein A, et al. Interpolation Techniques for Climate Variables[M]. Mexico: International Maize and Wheat Improvement Center,1999.
He YQ, Wilfred HT, Zhang ZL, et al. Asynchronous Holocene climatic change across China:a review[J]. Quaternary Research,2004,61(1):52-63.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF, Gessler PE. Splines-more than just a smooth interpolator[J]. Geoderma,1994, 62:45-67.
Hutchinson MF. Interpolation of rainfall data with thin plate smoothing splines[J]. Journal of Geographic Information and Decision Analysis,1998,2(2):139-167.
Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy,2003,18:235-265.
Keating BA, Carberry PS, Hammer GL, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy,2003,18:267-288.
Krige DG. A statistical approach to some mine valuations problems at the Witwatersrand[J]. Journal of the Chemical, Metallurgical and Mining Society of South Africa,1951,52:119-138.
LamNS. Spatial interpolation method:a review[J]. The American Cartographer,1983,10(2):129-149.
Li Ying, Vodacek A, Zhu Yushan. An automatic statistical segmentation algorithm for extraction of fire and smoke regions[J]. Remote Sensing of Environment,2007,108(2):171 - 178.
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J]. Agriculture, Ecosystems and Environment,2009,131:281-291.
Richard WK. Techniques for estimating uncertainty in climate change scenarios and impact studies[J]. Climate Research,2002,20:167-185.
Shackley S, Young P, Parkinson S, et al. Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling:Are GCMs the Best Tools?[J]. Climatic Change,1998,38(2):159-205.
Stockle CO, Donatelli M, Nelson R. CropSyst, a cropping systems simulation model[J]. European Journal of Agronomy,2000,18:289-307.
Tao FL, Hayashi Y, Zhang Z, et al. Global warming, rice production, and water use in China:Developing a probabilistic assessment[J]. Agricultural and Forest Meteorology,2008,148:94-110.
Thomas A. Climate change and potential agricultural productivity in China[J]. Erdkunde,2006, 60:157-172
Wahba G, Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation[J]. Monthly Weather Review,1980,108:1122-1145.
安琼,杨邦杰,焦险峰.作物遥感识别中的数据挖掘[J].农业工程学报,2007,23(8)：181－186.
陈惠,王加义,林晶,等.基于GIS的福建省双季稻气候——土壤生产潜力[J].生态学杂志,2008,27(7)：1104-1108.
郭银巧,赵传德,刘小军,等.基于模型和GIS技术的数字棉作系统的设计与实现[J].农业工程学报,2008,24(11)：139-144.
居红云,张俊本,李朝峰,等.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究,2007,24(11)：318－320.
李映雪,朱艳,曹卫星.不同施氮条件下小麦冠层的高光谱和多光谱反射特征[J].麦类作物学报,2006,26(2)：103－108.
刘志红,李领涛,Mc Vicar TR,等.专用气候数据空间插值软件ANUSPLIN及其应用[J].气象,2008,34(2)：92-100.
卢爱刚,庞德谦,康世明,等.全球变暖背景下中国气温时空差异响应[J].干旱区资源与环境,2009,23(8)：61-65.
蒙继华,吴炳方,李强子,等.运行化的农作物长势遥感监测系统[J].高技术通讯,2007,17(1)：94－99.
潘瑜春,王纪华,赵春江,等.基于网络GIS的作物品质监测与调优栽培系统[J].农业工程学报,2004,20(6)：120－123.
任国玉,徐铭志,初子莹,等.近50年来中国地面气候变化基本特征[J].气象学报,2005,63(6)：942-956.
唐国力,任国玉.近百年中国地表气温变化趋势的再分析[J].气候与环境研究,2005,10(4)：791-798.
谢立勇,林而达.二氧化碳浓度增高对稻、麦品质影响研究进展[J].应用生态学报,2007,18(3)：659-664.
谢云,王晓岚,林燕,等.近40年中国东部地区夏秋粮作物农业气候生产潜力时空变化[J].资源科学,2003,25(2)：7-13.
徐新刚,吴炳方,蒙继华,等.农作物单产预测系统的设计和实现[J].计算机工程,2008,34(9)：283-285.
徐怡,李龙澍,李学俊.基于粗燥熵和K-均值聚类算法的图像分割[J].华东理工大学学报：自然科学版,2007,33(2)：255-258.
薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学,2003,36(7)：807－812.
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.
薛利红,朱艳,张宪,等.利用冠层反射光谱预测小麦籽粒品质指标的研究[J].作物学报,2004,30(10)：1036－1041.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163-169.
张晋科,张凤荣,张琳,等.中国耕地的粮食生产能力与粮食产量对比研究[J].中国农业科学,2006,39(11)：2278-2285.
张淑梅,张建明,李丁鲁,等.高抗性淀粉粳稻新品系稻米淀粉链长分布与主要品质特性差异[J].中国农业科学,2009,42(6)：2237-2243.
周小成,汪小钦.遥感影像数据挖掘研究进展[J].遥感信息,2005,3：58－62.
周治国,孟亚利,曹卫星.基于GIS的作物生产管理信息系统[J].中国农业科学,2005,38(6)：1142-1147.
庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5)：605-615.
Angell JK, Korshover J, Cotton GF. Variation in United States cloudiness and sunshine,1950-82[J]. Journal of Climate and Applied Meteorology,1984,23:752-761.
Ares JO, Valle HF, Olinuck JA. Exploring improved pesticide management in sub-tropical environments with GIS-supported fate modeling[J]. Agricultural Systems,2006,91(3):189-210..
Basso B, Bertocco M, Sartori L, et al. Analyzing the effects of climate variability on spatial pattern of yield in a maize-wheat-soybean rotation[J]. Europ. J. Agronomy,2007,26:82-91.
Basso B, Ritchie JT, Piece FJ, et al. Spatial validation of crop models for precision agriculture[J]. Agricultural System,2001,68(2):97-112.
Berg M, Driessen PM. Water uptake in crop growth models for land use systems analysis I:A review of approaches and their pedigrees[J]. Agriculture, Ecosystems and Environment,2002,92:21-36.
Boegh E, Thorsen M, Butts MB, et al. Incorporating remote sensing data in physically based distributed agro-hydrological modeling [J]. Journal of Hydrology,2004,287:279-299.
Bouman BA, Keulen H, Laar HH. The "School of de Wit" Crop Growth Simulation Models:A Pedigree and Historical Overview[J]. Agricultural Systems,1996,52(23):171-198.
Bouman BA. Linking physical remote sensing model with crop growth simulation models, applied for suger beet[J]. International Journal of Remote Sensing,1992a,13:2565-2581.
Burrough PA, McDonnell RA. Principles of Geographical Information System[M]. New York:Oxford University Press,1998.
Caldiza DO, Haverkortb AJ, Struik PC. Analysis of a complex crop production system in interdependent agro-ecological zones:a methodological approach for potatoes in Argentina[J]. Agricultural Systems,2002,73:297-311.
Cao WX, Moss DN. Modeling phasic development in wheat:an integration of physiological components [J]. Journal of Agriculture Science,1997,129:163-172.
Chavas DR, Izaurralde RC, Thomas AM, et al. Long-term climate change impacts on agricultural productivity in eastern China[J]. Agricultural and Forest Meteorology,2009,149:1118-1128.
DeWit CT. Photosynthesis of leaf canopies[M]. Wageningen, the Netherlands:Institute for Biological and Chemical Research on Field Crops and Herbage. Agriculture Research Report,1965,663-671.
Duncan WG. Loomis RS, Williams WA, et al. A model for simulating Photosynthesis in plant communities[J]. Hilgardia,1967,38:181-205.
Engel T, Hoogenboom G, Jones JW, et al. AEGIS/Win:a computer program for the application of crop simulation models across geographic areas[J]. Agronomy Journal,1997,89(6):919-928.
Evans LT, Fischer RA. Yield potential:Its definition, measurement and significance[J]. Crop Science, 1999,39:1544-1551.
FAO. Report on the Agro-ecological Zones Project[J]. Vol.1. Methodology and results for south and Central America. World Soil Resources Report,483. FAO, Rome,1981.
Gehring R, Baynes RE, Wang J, et al. A web-based decision support system to estimate extended withdrawal intervals[J]. Computers and Electronics in Agriculture,2004,44:145-151.
Goe WR, Kenney M. The political economy of the privatization of agricultural information:The case of the United States[J]. Agricultural Administration and Extension,1988,28(2):81-99.
Gotway CA. Comparison of Kriging and inverse distance methods for mapping soil parameters[J]. Soil Science,1996,60:1237-1247.
Gruerif M, Duke C. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation[J]. European Journal of Agronomy,1998,9:127-136.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF, Gessler PE. Splines-more than just a smooth interpolato[J]. Geoderma,1994, 62:45-67.
Hutchinson MF. Interpolation of rainfall data with thin plate smoothing splines[J]. Journal of Geographic Information and Decision Analysis,1998,2(2):139-167.
IPCC. Climate Change 2007:The Physical Science Basis [M]. Cambridge, UK:Cambridge University Press,2007.
Ittersum MK, Leffelaar PA, Keulen H, et al. On approaches and applications of the Wageningen crop models[J]. Europ. J. Agronomy,2003,18:201-234.
Jones D, Barnes EM. Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management[J]. Agricultural Systems,2000,65:137-158.
Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy,2003,18:235-265.
Keating BA, Carberry PS, Hammer GL, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy,2003,18:267-288.
Kersebaum KC, Lorenz K, Reuter HI. Operational use of agro-meteorological data and GIS to derive site specific nitrogen fertilizer recommendations based on the simulation of soil and crop growth processes[J]. Physics and Chemistry of the Earth,2005,30:59-67.
Kropff MJ, Laar HH, Ten BH. ORYZA1:A basic model for irrigated lowland rice production[J]. Manila:IRRI,1983.
Laslett GM. Comparision of several spatial prediction methods for soil pH[J]. Soil Science,1987, 38:325-341.
Li Jingtao, Zou Ping, Gu Lin, et al. A Computer System for Forecasting the Threshold Period for Crop Weed Control[J]. Agricultural Sciences in China 2008,7(11):1394-1402.
Li Xia, Yeh AG. Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS[J]. Landscape and Urban Planning,2004,69:335-354.
Loomis RS, Williams WA. Maximum crop productivity:An estimate[J]. Crop Science,1963,3(1):67-72.
Marietto V, Ventura F, Giovanna Fontana, et al. Wheat growth simulation and yield prediction with seasonal forecasts and a numerical model[J]. Agricultural and Forest Meteorology,2007, 147:71-79.
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J]. Agriculture, Ecosystems and Environment,2009,131:281-291.
Mathiyalagan V, Grunwald S, Reddy KR, et al. A WebGIS and geodatabase for Florida's wetlands[J]. Computers and Electronics in Agriculture,2005,47:69-75.
Morari F, Lugato E, Borin M. An integrated non-point source model-GIS system for selecting criteria of best management practices in the Po Valley, North Italy[J]. Agriculture, Ecosystems and Environment,2004,102:247-262.
Opio-Odongo JMA, Raza MR. The need for a specialised agricultural information system:A proposal[J]. Agricultural Administration,1981,8(2):137-145.
Sante Ⅰ, Crecente R, Miranda D. A GIS web-based tool for the management of the PGI potato of Galicia[J]. Computers and Electronics in Agriculture,2004,44:161-171.
Seelan SK, Laguett S, Casady GM, et al. Remote sensing applications for precision agriculture:A learning community approach[J]. Remote sensing of Environment,2003,88(1-2):157-169.
Sero'dio C, Cunha JB, Morais R, et al. A networked platform for agricultural management systems[J]. Computers and Electronics in Agriculture,2001,31:75-90.
Shackley S, Young P, Parkinson S, et al. Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling:Are GCMs the Best Tools[J] Climatic Change,1998,38(2):159-205.
Tan GX, Shibasaki R. Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration[J]. Ecological Modeling,2003,168:357-370.
Thorp KR, Dejonge KC, Kaleita AL, et al. Methodology for the use of DSSAT models for precision agriculture decision support [J]. Computers and Electronics in Agriculture,2008.
Timmermans BGH, Vos J, Stomph TJ. The development, validation and application of a crop growth model to assess the potential of Solanum sisymbriifolium as a trap crop for potato cyst nematodes in Europe[J]. Field Crops Research,2009,111:22-31.
Timsina J, Humphreys E. Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems:A review[J]. Agricultural Systems,2006,90:5-31.
Wahba G, Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation[J]. Monthly Weather Review,1980,108:1122-1145.
Wesseling JG, Feddes RA. Assessing crop water productivity from field to regional scale[J]. Agricultural water management,2006,86:30-39.
Wharton PS, Kirk WW, Baker KM, et al. A web-based interactive system for risk management of potato late blight in Michigan[J]. Computers and Electronics in Agriculture,2008,61:136-148.
Xiong W, Holman I, Conway D, et al. A crop model cross calibration for use in regional climate impacts studies[J]. Ecological Modelling,2008,213:365-380.
Xue LH, Cao WX, Luo WH, Dai TB, Zhu Y. Monitoring leafnitrogen status in rice with canopy spectral reflectance[J]. Agronomy Journal,2004,96(1):135-142.
Yun JI. Predicting regional rice production in South Korea using spatial data and crop-growth modeling [J]. Agricultural Systems,2003,77:23-38.
Zeng HH, Alarcon VJ, Kingery W, et al. A web-based simulation system for transport and retention of dissolved contaminants in soil[J]. Computers and Electronics in Agriculture,2002,33:105-120.
曹卫星,朱艳,田永超,等.数字农作技术研究的若干进展与发展方向[J].中国农业科学,2006,39(2)：281-288.
曹卫星,罗卫红.作物系统模拟及智能管理[M].高等教育出版社,2003,1：121-139.
曹卫星.农业信息学[M].北京：中国农业出版社,2005.
曹永华.农业决策支持系统研究综述[J].中国农业气象,1997,18(4)：46～50.
曾昭美,严中伟,章名立.近40年我国云、日照、温度及日较差的统计[J].科学通报,1993,38(5)：440-443.
常文渊,戴新刚,陈洪武.地质统计学在气象要素场插值的实例研究[J].地球物理学报,2004,47(6)：982-990.
陈国南.用迈阿密模型测算我国生物生产两的初步尝试[J].自然资源学报,1987,2(3)：270-278.
陈惠,王加义,林晶,等.基于GIS的福建省双季稻气候——土壤生产潜力[J].生态学杂志,2008,27(7)：1104-1108.
戴明宏,陶洪斌,廖树华,等.基于CERES-Maize模型的华北平原玉米生产潜力的估算与分析[J].农业工程学报,2008,24(4)：30-36.
戴新刚,陈洪武.地址统计学在气象要素场插值的实力研究[J].地球物理学报,2004,47(6)：983-990.
封志明.区域土地资源承载力研究模式雏议[J].自然资源学报,1990,5(3)：271-283.
冯利平,孙宁,刘荣花,等.我国华北冬小麦生产影响评估模型的研究[J].中国生态农业学报,2003,11(4)：73-76.
付修勇,张晶,王景平,等.基于栅格的中国一季稻生产潜力研究[J].中国人口,2008,18(4)： 99-103.
高亮之,金之庆,黄耀.水稻栽培计算机模拟优化决策系统(RCSODS)[M].北京：中国农业出版社,1992.
宫鹏,梅雪良,Biging GS,等.利用数字摄影测量探测橡树草原变化[J].遥感学报,1999,3(4)：285-289.
宫鹏,浦瑞良,郁彬.不同季相针叶树种高光谱数据识别分析[J].遥感学报,1998,8：211-217.
宫鹏.遥感科学与技术中的一些前沿问题[J].遥感学报,2009,10：13-23.
宫鹏.遥感生态测量学进展[J].自然资源学报,1999,14(4)：313-317.
辜智慧,史培军,陈晋.气象观测站点稀疏地区的降水插值方法探讨——以锡林郭勒盟为例[J].北京师范大学学报(自然科学版),2006,42(2)：204-208.
光继双,王唳华.3S技术在农田基础地图测绘与更新重的集成应用[J].农业工程学报,2003,19(3)：220-223.
侯光良,游橙才.用筑后模型估算我国植物气候生产力[J].自然资源学报,1990,5(1)：60-65.
候光良,刘允芳.我国气候生产潜力及其分区[J].自然资源,1985,19(3)：52-59.
候光良,游松才.用筑后模型估算我国植物气候生产力[J].自然资源学报,1990,5(1)：60-65.
侯军WebGIS技术在海洋监测信息集成中的应用研究[J].海洋技术,2005,24(4)：18-22.
胡惠萍.土壤属性的空间差异及其空间插值方法研究[J].湘潭师范学院学报(自然科学版),2001,11(1)：56-58.
黄秉维.中国农业生产潜力一光合潜力[C].地理集刊.北京：科学出版社,1985.
贾善刚.农业信息化与农业经济发展[J].农业经济问题,1999,2：48-51.
金之庆,葛道阔,高亮之,等.我国东部样带适应全球气候变化的若干粮食生产对策的模拟研究[J].中国农业科学,1998,31(4)：51-58.
金之庆,葛道阔,石春林,等.东北平原适应全球气候变化的若干粮食生产对策的模拟研究[J].作物学报,2002,28(1)：24-31.
金之庆,葛道阔,郑喜莲,等.评价全球气候变化对我国玉米生产的可能影响[J].作物学报,1996,22(5)：513-524.
金之庆,石春林.江淮平原小麦渍害预警系统(WWWS)[J].作物学报,2006,32(10)：1458-1465.
李存东,曹卫星,李旭,等.论作物信息技术及其发展战略[J].农业现代化研究,1998,19(1)：17-20.
李德仁.“三S”技术与农业发展[J].微电脑应用,1998,3：2～7.
李德仁.论RS, GPS与GIS集成的定义、理论与关键技术[J].遥感学报,1997,1(1)：63～68.
李建龙,蒋平,赵德华,等.3S技术在草地产量生态成因分析与农业资源估测中的应用研究[J].中国草地,2003,25(3)：15～23.
李剑萍,郑有飞,殷剑敏等.3S技术在晚稻长势监测中的综合应用研究[J].南京气象学院学报,2001,24(1)：106-112.
李培军,张维峰,郭洪涛,等.气象资料三维化技术中的插值问题[J].气象科学,2005,47(6)：617-625.
李三爱,居辉,池宝亮.作物生产潜力研究进展[J].中国农业气象,2005,26(2)：106-111.
李晓燕,张树文.吉林省德惠市土壤速效钾的空间变异及不同插值方法的比较[J].水土保持学报,2004,18(4)：97-100.
梁荣欣,张瑞雪.水稻的气候土壤生产潜力估算[J].自然资源,1984,(2)：68-73.
凌辉,武伟,王润,等.小尺度下土壤重金属铬含量的空间插值方法比较研究[J].西南大学学报(自然科学版),2007,29(11)：93-99.
刘建栋,王馥棠,于强,等.华北地区农业干旱预测模型及其应用研究[J].应用气象学报,2003,14(5)：593-604.
刘建栋,周秀骥,于强.FAO生产潜力模型中基本参数的修正[J].自然资源学报,2001,16(3)：240-247.
刘晓军,朱艳,姚霞.基于WebGIS的农业空间信息管理及辅助决策系统[J].农业工程学报,2006,22(5)：125-129.
刘雪琴,李宁,温玉婷,等.内蒙中西部土壤水分统计插值模型试验[J].气象科学,2009,29(12)：734-741.
刘宇,陈泮勤,张稳,等.一种地面气温的空间插值方法及其误差分析[J].大气科学,2006,30(1)：146-152
刘志红,Me Vicar TR, Li LT,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科技大学学报(自然科学版),2008,36(10)：227-234
刘志红,李领涛,McVicar TR,等.专用气候数据空间插值软件ANUSPLIN及其应用[J].气象,2008,34(2)：92-100
龙嘉,于慧梅.加拿大现代农业信息技术的应用与管理[J].世界农业,2003,3：29-32.
陆忠艳,袁子鹏,蔡福,等.基于GIS的气温和降水推算方法研究[J].气象科技,2008,36(4)：389－395.
罗群英,林而达.农业技术转移决策支持系统(DSSAT)新进展[J].气象,1996,22(12)：10-13.
骆世明,彭少麟.农业生态系统分析[M].广州：广东科技出版社,1996.
马帅,陈印军,郭淑敏.作物生产潜力模型研究进展[J].中国农学通报,2006,22(1)：349-352.
潘瑜春,王纪华,赵春江.基于GIS和RS的应用分析系统集成研究[J].计算机工程,2005,31(15)：44-46.
潘瑜春,赵春江.地理信息技术在精准农业中的应用[J].农业工程学报,2003,19(4)：1-5.
庞夙,李廷轩,王永东,等.县域农田土壤铜含量的协同克里格插值及采样数量优化[J].中国农业科学,2009,42(8)：2828-2836.
彭汉艮,姚霞,朱艳,等.种植制度知识模型系统的设计与实现[J].南京农业大学学报,2005,28(2)：125-128.
浦瑞良,宫鹏.森林生物化学与CASI高光谱分辨率遥感数据的相关分析[J].遥感学报,1995,1(2)：115-123.
戚昌瀚,殷新佑,刘桃菊,等.(RICAM 1.3双季稻高产管理决策支持系统》在小流域双季稻区的“水稻苗情预报”研究[J].江西农业大学学报,2000,22(4)：482-484.
任国玉,徐铭志,初子莹,等.近54年中国地面气温变化[J].气象学报,2005,63(6)：942-956.
沙宗尧,边馥菩.3S技术的农业应用与精细农业工程[J].测绘通报,2003,6：29-31.
帅细强,王石立,马玉平,等.基于ORYZA2000模型的湘赣双季稻气候生产潜力[J].中国农业气象,2009,30(4)：575－581.
苏希.农业信息技术的发展与应用前景[J].计算机与农业,2003,10：6-9.
孙希华.基于GIS和RS的章丘市农业自然资源质量综合评价[J].山东师范大学学报(自然科学版),2004,19(1)：48-59.
汤国安,赵牡丹.地理信息系统[M].北京：科学出版社,2002.
田永超,朱艳,曹卫星,等.利用冠层反射光谱和叶片SPAD值预测小麦籽粒蛋白质和淀粉的积累[J].中国农业科学,2004,37：808-813.
田永超,朱艳,曹卫星,等.小麦冠层反射光谱与植株水分状况的关系[J].应用生态学报,2004,15：2072-2076.
田永超,朱艳,曹卫星,等.利用冠层反射光谱预测小麦叶片的糖氮量及糖氮比[J].作物学报,2005,31：355-360.
汪懋华.信息技术革命与持续农业发展.http://www.agri.ac.cn.
王纯枝,宇振荣,辛景峰.基于遥感和作物生长模型的作物产量差估测[J].农业工程学报,2005, 21(7)：84-89.
王霓虹.基于Web与3S技术的森林防火智能决策支持系统的研究[J].林业科学,2002,38(3)：114－119.
王人潮,黄敬峰,史舟,等.论农业信息科学的形成与发展[J].浙江大学学报(农业与生命科学版),2003,29(4)：355-360.
王人潮,史舟.农业信息科学与农业信息技术[M].北京：中国农业出版社,2003.
王向东,张建平,马海莲,等.作物模拟模型的研究概况及展望[J].河北农业大学学报,2003,26增刊：20～23.
王晓云,郭文利,奚文,等.利用“3S”技术进行北京地区土壤水分监测[J].应用气象学报,2002,13(4)：422-429.
吴文玉,马晓群.基于GIS的安徽省气温数据栅格化方法研究[J].中国农学通报,2009,25(02)：263-267
吴秀芹,张洪岩,李瑞改,等.ArcGIS 9——地理信息系统的应用和实践[M].北京：清华出版社,2007.
谢云,王晓岚,林燕,等.近40年中国东部地区夏秋粮作物农业气候生产潜力时空变化[J].资源科学,2003,25(2)：7－13.
徐超,吴大千,张治国.山东省多年气象要素空间插值方法比较研究[J].山东大学学报(理学版),2008,43(3)：1-5.
徐新良,刘纪远,曹明奎,等.近期气候波动与LUCC过程对东北农田生产潜力的影响[J].地理科学,2007,27(3)：318-324.
薛利红,曹卫星,罗卫红,等.光谱植被指数与水稻叶面积指数相关性的研究[J].植物生态学报,2004,28(1)：47-52.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163-169.
杨京平,王兆骞.作物生长模拟模型及其应用[J].应用生态学报,1999,10(4)：501-505.
杨宁,廖桂平.作物生长模拟研究进展[J].作物研究,2002,5：255～257.
游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1)：140-144.
章云兰,郑江平,陈振宇.农业信息技术现状分析与我国的发展对策[J].浙江大学学报(农业与生命科学版),2001,27(2)：229-232.
赵哲远,吴妍,艾亮辉.论RS与GIS集成技术支持下的土地资源管理手段现代化[J].管理现代化,2002,2：32-35.
郑小波,罗宇翔,于飞,等.西南复杂山地农业气候要素空间插值方法比较[J].中国农业气象,2008,29(4)：458-462.
周兆德.海南岛水稻气候生产潜力和人口承载量的估算[J].自然资源学报,1989,4(1)：46-53.
周治国,曹卫星,朱艳.基于GIS的作物生产管理信息系统[J].农业工程学报,2005,21(1)：114-118.
周治国,孟亚利,曹卫星.基于知识模型和GIS的作物生产潜力评价[J].中国农业科学,2005,38(6)：1142-1147.
朱求安,张万昌,赵登忠.基于PRISM和泰森多边形的地形要素日降水量空间插值研究[J].地理科学,2005,25(2)：233-238.
朱艳,曹卫星,王绍华,等.小麦栽培管理知识模拟系统的设计与实现[J].南京农业大学学报,2002,25(3)：12～16.
朱艳,曹卫星,田永超,等.作物管理知识模型系统设计与开发框架研究[J].农业工程学报,2004,20(4)：138～142.
朱艳.基于知识模型的小麦管理决策支持系统的研究[D].南京：南京农业大学,2003.
庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5)：605-615.
邹源.不同地貌土壤属性空间变异性研究[D].扬州：扬州大学,2006.
Evjen B, Hanselman S, Muhanmmad F. Professional ASP.NET 2.0[M]. John Wiley and Sones,2006.
Nagel C, Evjen B, Glynn J. Professional C# 2005[M]. John Wiley and Sones,2006.
Graham J, Newman G, Jarnevich C, et al. A global organism detection and monitoring system for non-native species[J]. Ecological Informatics,2007,2(2):177-183.
Microsoft Corporation. Visual Studio 2005 MSDN Library[M]. Microsoft Corporation,2005.
Rao M, Fan GL, Thomas J, et al. A web-based GIS decision support system for managing and planning USDA's conservation reserve program (CRP)[J]. Environmental Modeling & Software,2007,22(9): 1270-1280.
Raup B, Kaab A, Kargel J S, et al. Remote sensing and GIS technology in the global land ice measurements from space (GLIMS) project[J]. Computers and Geosciences,2007,33(26):104-125.
Serranoa S, Jimenez-Hornero FJ, Raveb EG, et al. GIS design application for "Sierra Morena Honey" designation of origin[J]. Computers and Electronics in Agriculture,2008,64(2):307-317.
北京超图地理信息技术有限公司.理解SuperMap IS.NET[M].北京超图地理信息技术有限公司,2006.
曹静.精确农作管理模型与决策支持系统的研究[D].南京：南京农业大学,2008.
单英杰.基于GIS和模型的种植设计系统研究[D].南京：南京农业大学,2007.
董岩,耿杰,谭景信.基于OGR的雷达终端显示系统的设计与实现[J].计算机工程与设计,2009,30(19)：4560-4563
冯伟.基于高光谱遥感的小麦氮素营养及生长指标监测研究[D].南京：南京农业大学,2007.
花登峰.基于构件化生长模型的作物管理决策支持系统[D].南京：南京农业大学,2007.
蒋红军.基于模型和3S的数字麦作支持系统研究[D].南京：南京农业大学,2007.
康金春.中间件技术在空间数据共享中的应用[J].地理空间信息,2009,7(5)：35-37
李芳,邬群勇,汪小钦.基于GeoRaster的多源遥感数据存储研究[J].测绘科学,2009,34(3)：150－151
李卫国.水稻生长模拟与决策支持系统的研究[D].南京：南京农业大学,2005.
李映雪.基于冠层反射光谱的小麦氮素营养与籽粒品质监测[D].南京：南京农业大学,2005.
刘小军.基于WebGIS和模型的农业空间信息管理系统研究[D].南京：南京农业大学,2005.
潘洁.小麦生长模拟与决策支持系统的研究[D].南京：南京农业大学,2005.
汤泉,牛铮.基于IDL与ENVI二次开发的遥感系统开发方法[J].计算机应用,2008,28(6)：270-276
田永超.基于冠层反射光谱的水稻水分及稻麦生长监测[D].南京：南京农业大学,2003.
严定春.水稻管理知识模型与决策支持系统的研究[[)].南京：南京农业大学,2004.
叶宏宝.稻麦生产的水肥动态模拟及管理决策支持系统研究[D].南京：南京农业大学,2005.
叶宏宝.基于模型与GIS的水稻生产力预测预警技术研究[D].南京：南京农业大学,2008.
周冬琴.基于冠层反射光谱的水稻氮素营养与籽粒品质监测[D].南京：南京农业大学,2007.
朱艳.基于知识模型的小麦管理决策支持系统的研究[D].南京：南京农业大学,2003.
朱洪芬.基于遥感的作物生长监测与诊断系统研究[D].南京：南京农业大学,2008.
朱元励.基于遥感的作物生长监测与诊断系统研究[D].南京：南京农业大学,2009.
Angell JK, Korshover J, Cotton GF. Variation in United States cloudiness and sunshine,1950-82[J]. Journal of Climate and Applied Meteorology,1984,23:752-761.
Burrough PA, McDonnell RA. Principles of Geographical Information System[M]. New York:Oxford University Press,1998.
Collins FC, Bolstad PV. A comparison of spatial interpolation techniques in temperature estimation[EB/OL]. (2004-12-31) [2009-09-08]. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/collins_fred/collins.html.
Consultative Group on International Agricultural Research. The CGIAR Consortium for Spatial Information[EB/OL]. (2008-08-19) [2009-12-09].http://srtm.csi.cgiar.org/selection/inputCoord.asp.
Hartkamp D, Beurs KD, Stein A, et al. Interpolation Techniques for Climate Variables[M]. Mexico: International Maize and Wheat Improvement Center,1999.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF, Gessler PE. Splines-more than just a smooth interpolator[J]. Geoderma,1994, 62:45-67.
Hutchinson MF. ANUSPLIN version 4.3 User Guide[M]. Canberra:Center for Resource and Environment Studies, Australia National University,2004.
Hutchinson MF. Interpolation of rainfall data with thin plate smoothing splines[J]. Journal of Geographic Information and Decision Analysis,1998,2(2):139-167.
Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy,2003,18:235-265.
Kaiser DP. Two Long-term Instrumental Climatic Databases of the People's Republic of China[R]. Tennessee:Oak Ridge National Laboratory,1991.
Keating BA, Carberry PS, Hammer GL, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy,2003,18:267-288.
Krige DG. A statistical approach to some mine valuations problems at the Witwatersrand[J]. Journal of the Chemical, Metallurgical and Mining Society of South Africa,1951,52:119-138.
Lam NS. Spatial interpolation method:a review[J]. The American Cartographer,1983,10(2):129-149.
Wahba G, Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation[J]. Monthly Weather Review,1980,108:1122-1145.
曹宏鑫,金之庆,石春林,等.中国作物模型系列的研究与应用[J].农业网络信息,2006(5)：45-48.
曾昭美,严中伟,章名立.近40年我国云、日照、温度及日较差的统计[J].科学通报,1993,38(5)：440-443.
戴新刚,陈洪武.地质统计学在气象要素场插值的实例研究[J].地球物理学报,2004,47(6)：983-990.
辜智慧,史培军,陈晋.气象观测站点稀疏地区的降水插值方法探讨——以锡林郭勒盟为例[J].北京师范大学学报(自然科学版),2006,42(2)：204-208.
何红艳,郭志华,肖国发.降水空间插值技术的研究进展[J].生态学杂志,2005,24(10)：1187-1191.
刘宇,陈泮勤,张稳,等.一种地面气温的空间插值方法及其误差分析[J].大气科学,2006,30(1)：146－152.
刘志红.Mc Vicar TR,李领涛,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科 技大学学报(自然科学版),2008,36(10)：227-234.
刘志红,李领涛,Mc Vicar TR,等.专用气候数据空间插值软件ANUSPLIN及其应用[J].气象,2008,34(2)：92－100.
陆忠艳,袁子鹏,蔡福,等.基于GIS的气温和降水推算方法研究[J].气象科技,2008,36(4)：389-395.
汤国安,赵牡丹.地理信息系统[M].北京：科学出版社,2002.
吴文玉,马晓群.基于GIS的安徽省气温数据栅格化方法研究[J].中国农学通报,2009,25(02)：263-267.
吴秀芹,张洪岩,李瑞改,等.地理信息系统应用与实践——ArcGIS 9[M].北京：清华出版社,2007.
徐超,吴大千,张治国.山东省多年气象要素空间插值方法比较研究[J].山东大学学报(理学版),2008,43(3)：1-5.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163－169.
游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1)：140-144.
郑小波,罗宇翔,于飞,等.西南复杂山地农业气候要素空间插值方法比较[J].中国农业气象,2008,29(4)：458-462.
朱求安,张万昌,赵登忠.基于PRISM和泰森多边形的地形要素日降水量空间插值研究[J].地理科学,2005,25(2)：233-238.
庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5)：605-615.
Consultative Group on International Agricultural Research. The CGIAR Consortium for Spatial Information[EB/OL]. (2008-08-19) [2009-12-09].http://srtm.csi.cgiar.org/selection/inputCoord.asp.
He YQ, Wilfred HT, Zhang ZL, et al. Asynchronous Holocene climatic change across China:a review[J]. Quaternary Research,2004,61(1):52-63.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF. ANUSPLIN Version 4.3 User Guide[C]. Canberra:Center for Resource and Environment Studies, Australia National University,2004.
IPCC. Climate Change 2007:The Physical Science Basis [M]. Cambridge, UK:Cambridge University Press,2007.
Mohammed AR, Tarpley L. High nighttime temperatures affect rice productivity through altered pollen germination and spikelet fertility[J]. Agricultural and Forest Meteorology,2009,149:999-1008.
Peng SB, Huang JL, Sheehy JE, et al. Rice yields decline with higher night temperature from global warming[J]. Proceedings of the National Academy of Sciences of the USA,2004,101(27): 9971-9975.
Ryan MG Effects of climate change on plant respiration[J]. Ecological Application,1991,1(2):157-167.
Tao FL, Yokozawa M, Xu YL, et al. Climate changes and trends in phenology and yields of field crops in China,1981-2000[J]. Agricultural and Forest Meteorology,2006,138:82-92.
Yin XY, Kropff MJ, Goudriaan J. Differential effects of day and night temperature on development to flowering in rice[J]. Annals of Botany,1996,77(3):203-213.
Yin XY, Kropff MJ, Goudriaan J. The effects of temperature on leaf appearance in rice[J]. Annals of Botany,1996,77(3):215-221.
陈隆勋,邵永宁,张清芳等.近四十年我国气候变化的初步分析[J].应用气象学报,1991,2(2)：164-174.
陈隆勋,周秀骥,李维亮等.中国近80年来气候变化特征及其形成机制[J].气象学报,2004,62(5)：634-646.
陈隆勋,朱文琴,王文等.中国近45年来气候变化的研究[J].气象学报,1998,56(3)：257-271.
陈玉民,郭国兴,王光兴等.中国主要作物需水量与灌溉[M].北京：水利电力出版社,1995,300-350.
刁操铨等.作物栽培学各论(南方本)[M].北京：中国农业出版社,1994,78-79.
黄丕生.早稻品种的生长发育与热量条件关系的分析[J].南京农业大学学报,1994,2：22-31.
刘志红,McVicar TR,李领涛,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科技大学学报(自然科学版),2008,36(10)：227-234.
卢爱刚,庞德谦,康世明等.全球变暖背景下中国气温时空差异响应[J].干旱区资源与环境,2009,23(8)：61－65.
梅方权,吴宪章,姚长溪,等.中国水稻种植区划[J].中国水稻科学,1988,2(3)：97-110.
任国玉,徐铭志,初子莹,等.近50年来中国地面气候变化基本特征[J].气象学报,2005,63(6)：942-956.
唐国力,任国玉.近百年中国地表气温变化趋势的再分析[J].气候与环境研究,2005,10(4)： 791-798.
谢立勇,林而达.二氧化碳浓度增高对稻、麦品质影响研究进展[J].应用生态学报,2007,18(3)：659-664.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163-169.
严定春,朱艳,曹卫星,等.水稻群体生长指标动态的知识模型研究[J].中国农业科学,2005,38(1)：38-44.
张淑梅,张建明,李丁鲁,等.高抗性淀粉粳稻新品系稻米淀粉链长分布与主要品质特性差异[J].中国农业科学,2009,42(6)：2237-2243.
中国主要农作物需水量等值线图协作组.中国主要农作物需水量等值线图研究[M].北京：中国农业科技出版社,1993,15-17.
中华人民共和国农业部.中国水稻高产栽培模式图[EB/OL]. (2009-04-19) [2009-12-09]. http://www.agri.gov.cn/ztzl/ql/jsms/P020090410579290158332.doc.
左洪超,吕世华,胡隐樵.中国近50年气温及降水量的变化趋势分析[J].高原气象,2004,23(2)：238-244.
Chavas DR, Izaurralde RC, Thomas AM, et al. Long-term climate change impacts on agricultural productivity in eastern China[J]. Agricultural and Forest Meteorology,2009,149:1118-1128.
Consultative Group on International Agricultural Research. The CGIAR Consortium for Spatial Information[EB/OL]. (2008-08-19) [2009-12-09].http://srtm.csi.cgiar.org/selection/inputCoord.asp.
Dirceu TC, Robert FD. An energy-crop growth variable and temperature function for predicting corn growth and development:planting to silking[J]. Agronomy Journal,1980,72:503-516.
He YQ, Wilfred HT, Zhang ZL, et al. Asynchronous Holocene climatic change across China:a review[J]. Quaternary Research,2004,61(1):52-63.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF. ANUSPLIN version 4.3 user guide[M]. Canberra:Center for Resource and Environment Studies, Australia National University,2004.
Loomis RS, Williams WA. Maximum crop productivity:an estimate[J]. Crop Science,1963,3(1):67-72.
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J]. Agriculture, Ecosystems and Environment,2009,131:281-291.
Richard WK. Techniques for estimating uncertainty in climate change scenarios and impact studies[J]. Climate Research,2002,20:167-185
Shackley S, Young P, Parkinson S, et al. Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling:Are GCMs the Best Tools[J]. Climatic Change,1998,38(2):159-205
Tao FL, Yokozawa M, Xu YL, et al. Climate changes and trends in phenology and yields of field crops in China,1981-2000[J]. Agricultural and Forest Meteorology,2006,138:82-92.
Thomas A. Climate change and potential agricultural productivity in China[J]. Erdkunde,2006, 60:157-172
曹卫星,罗卫红.作物系统模拟及智能管理[M].北京：华文出版社,2000：111－112.
曹卫星,朱艳,汤亮,等.数字农作系统[M].北京：科学出版社,2008：101-103.
曹卫星,朱艳.作物管理知识模型[M].北京：中国农业出版社,2004：49-51.
陈惠,王加义,林晶,等.基于GIS的福建省双季稻气候——土壤生产潜力[J].生态学杂志,2008,27(7)：1104－1108.
陈隆勋,邵永宁,张清芳,等.近四十年我国气候变化的初步分析[J].应用气象学报,1991,2(2)：164-174.
陈隆勋,周秀骥,李维亮等.中国近80年来气候变化特征及其形成机制[J].气象学报,2004,62(5)：634-646.
陈隆勋,朱文琴,王文等.中国近45年来气候变化的研究[J].气象学报,1998,56(3)：257-271.
陈玉民,郭国兴,王光兴等.中国主要作物需水量与灌溉[M].北京：水利电力出版社,1995, 300-350.
戴明宏,陶洪斌,廖树华,等.基于CERES-Maize模型的华北平原玉米生产潜力的估算与分析[J].农业工程学报,2008,24(4)：30-36.
丁一汇,林而达,何建坤,等.中国气候变化——科学、影响、适应及对策研究[M].北京：中国环境科学出版社,2009：30-31.
付修勇,张晶,王景平,等.基于栅格的中国一季稻生产潜力研究[J].中国人口,2008,18(4)：99-103.
候光良,刘允芳.我国气候生产潜力及其分区[J].自然资源,1985,19(3)：52-59.
黄秉维.自然条件与作物生产：光合潜力[M].自然地理综合工作六十年——黄秉维文集.北京：科学出版社,1993：183－196.
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.
金之庆,葛道阔,高亮之,等.我国东部样带适应全球气候变化的若干粮食生产对策的模拟研究[J].中国农业科学,1998,31(4)：51-58.
金之庆,葛道阔,石春林,等.东北平原适应全球气候变化的若干粮食生产对策的模拟研究[J].作物学报,2002,28(1)：24-31.
金之庆,葛道阔,郑喜莲,等.评价全球气候变化对我国玉米生产的可能影响[J].作物学报,1996,22(5)：513-524.
金之庆,石春林.江淮平原小麦渍害预警系统(WWWS)[J].作物学报,2006,32(10)：1458－1465.
刘志红,McVicar TR,李领涛,等.基于ANUSPLIN的时间序列气象要素空间插值[J].西北农林科技大学学报(自然科学版),2008,36(10)：227-234.
龙斯玉.江苏省农业气候资源生产潜力及区划的研究[J].地理科学,1985,5(3)：218-226.
梅方权,吴宪章,姚长溪,等.中国水稻种植区划[J].中国水稻科学,1988,2(3)：97-110.
任国玉,徐铭志,初子莹,等.近50年来中国地面气候变化基本特征[J].气象学报,2005,63(6)：942-956.
孙华生,黄敬峰,李波,等.中国水稻遥感信息获取区划研究[J].中国农业科学,2008,41(12)：4039-4047.
谢立勇,林而达.二氧化碳浓度增高对稻、麦品质影响研究进展[J].应用生态学报,2007,18(3)：659-664.
谢云,王晓岚,林燕,等.近40年中国东部地区夏秋粮作物农业气候生产潜力时空变化[J].资源科学,2003,25(2)：7－13.
严定春,朱艳,曹卫星,等.水稻群体生长指标动态的知识模型研究[J].中国农业科学,2005,38(1)：38-44.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2): 163-169.
张晋科,张凤荣,张琳,等.中国耕地的粮食生产能力与粮食产量对比研究[J].中国农业科学,2006,39(11)：2278-2285
张淑梅,张建明,李丁鲁,等.高抗性淀粉粳稻新品系稻米淀粉链长分布与主要品质特性差异[J].中国农业科学,2009,42(6)：2237-2243.
中国主要农作物需水量等值线图协作组.中国主要农作物需水量等值线图研究[M].北京：中国农业科技出版社,1993,3-40.
中华人民共和国农业部.中国水稻高产栽培模式图[EB/OL]. (2009-04-19) [2009-12-09]. http://www.agri.gov.cn/ztzl/ql/jsms/P020090410579290158332.doc.
周兆德.海南岛水稻气候生产潜力和人口承载量的估算[J].自然资源学报,1989,4(1)：46-53.
周治国,孟亚利,曹卫星.基于GIS的作物生产管理信息系统[J].中国农业科学,2005,38(6)：1142-1147.
朱艳,曹卫星,田永超,等.作物管理知识模型系统设计与开发框架研究[J].农业工程学报,2004,20(4)：138～142.
左洪超,吕世华,胡隐樵.中国近50年气温及降水量的变化趋势分析[J].高原气象,2004,23(2)：238-244.
Bandyopadhyay S, Maulik U. Genetic clustering for automatic evolution of clusters and application to image classification[J]. IEEE Pattern Recognition,2002,35(6):1197-1208.
Cheng Hengda, Shi Xianjun, Min Rui, et al. Approaches for automated detection and classification of masses in mammograms[J]. Pattern Recognition,2006,39(4):646-668.
Chu Yianshu, Tseng Shianshyong, Tsai Yujie, et al. An intelligent questionnaire analysis expert system[J]. Expert Systems with Applications,2009,36(2):2699—2710.
GDAL. Geospatial data abstraction library, http://www.gdal.org/,2008-11-04/2009-08-21.
Graham J, Newman G, Jarnevich C, et al. A global organism detection and monitoring system for non-native species[J]. Ecological Informatics,2007,2(2):177-183.
Guo Ping, Jia Yunde, Lyu M R. A study of regularized gaussian classifier in high-dimension small sample set case based on MDL Principle with application to spectrum recognition[J]. Pattern Recognition,2008,41(9):2842-2854.
Hu Tianming, Sung S Y. A hybrid EM approach to spatial clustering[J]. Computational Statistics & Data Analysis,2006,50(5):1188-1025.
Li Ying, Vodacek A, Zhu Yushan. An automatic statistical segmentation algorithm for extraction of fire and smoke regions[J]. Remote Sensing of Environment,2007,108(2):171 - 178.
Melomed E, Gorbach I, Berger A, et al. Microsoft SQL Server 2005 Analysis Services[M]. Indiana: Sams Publishing,2007.
Microsoft. Microsoft Clustering Algorithm Technical Reference[EB/OL]. http://msdn.microsoft.com/ zh-cn/library/cc280445.aspx,2009-7-30/2009-08-21.
Microsoft. Programming AMO Data Mining Objects[EB/OL]. http://technet.microsoft.com/en-us/ library/ms345087.aspx,2009-7-30/2009-08-21.
Potdar M B, Geoge H R, Petter M J. Sorghum yield modeling based on crop growth parameters determined from visible and near-IR channel NOAA AVHRR data[J]. International Journal of Remote Sensing,1993,14(5):895-905.
Rao M, Fan Guoliang, Thomas J, et al. A web-based GIS decision support system for managing and planning USDA's conservation reserve program (CRP)[J]. Environmental Modeling & Software, 2007,22(9):1270-1280.
Raup B, Kaab A, Kargel J S, et al. Remote sensing and GIS technology in the global land ice measurements from space (GLIMS) project[J]. Computers and Geosciences,2007,33(26):104-125.
Raup B, Racoviteanu A, Khalsa S J S, et al. The GLIMS geospatial glacier database:A new tool for studying glacier change[J]. Global and Planetary Change,2007,56:101-110.
Serranoa S, Jim6nez-Hornero F J, Gutierrez de Rav6b E, et al. GIS design application for "Sierra Morena Honey" designation of origin[J]. Computers and Electronics in Agriculture,2008,64(2): 307-317.
Tang Zhaohui, MacLennan J. Data mining with SQL Server 2005[M]. New York:John Wiley and Sons Inc,2005.
安琼,杨邦杰,焦险峰.作物遥感识别中的数据挖掘[J].农业工程学报,2007,23(8)：181－186.
居红云,张俊本,李朝峰,等.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究,2007,24(11)：318－320.
鞠昌华.利用地-空高光谱遥感监测小麦氮素状况与生长特征[D].南京农业大学农学院,2008.
李映雪,朱艳,曹卫星.不同施氮条件下小麦冠层的高光谱和多光谱反射特征[J].麦类作物学报,2006,26(2)：103－108.
刘扬,周清波,刘佳,等.基于遥感和WebGIS的冬小麦估计支持系统[J].中国农业科学,2008,41(10)：3371－3375.
骆剑承,周成虎,梁怡,等.有限混合密度模型及遥感影像EM聚类算法[J].中国图象图形学报,2002,7A(4)：336－340.
蒙继华,吴炳方,李强子,等.运行化的农作物长势遥感监测系统[J].高技术通讯,2007,17(1)：94--99.
潘建刚,赵文吉,宫辉力.遥感图像分类方法的研究[J].首都师范大学学报：自然科学版,2004,25(3)：86－91.
徐新刚,吴炳方,蒙继华,等.农作物单产预测系统的设计和实现[J].计算机工程,2008,34(9)：283-285.
徐怡,李龙澍,李学俊.基于粗糙熵和K-均值聚类算法的图像分割[J].华东理工大学学报：自然科学版,2007,33(2)：255－258.
薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学,2003,36(7)：807－812.
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.
薛利红,朱艳,张宪,等.利用冠层反射光谱预测小麦籽粒品质指标的研究[J].作物学报,2004,30(10)：1036－1041.
周小成,汪小钦.遥感影像数据挖掘研究进展[J].遥感信息,2005,3：58－62.
Delecolle R, Mass SJ, Guerif M. Remote Sensing And Crop Production Models[J]. Journal Of Photogrammetry And Remote Sensing,1992,47:145-161.
Engel T, Hoogenboom G, Jones JW, et al.1997. AEGIS/WIN:A computer program for the application of crop simulation models across geographic areas[J]. Agronomy J.1997,89:919-928.
Er-Raki S, Chehbouni A, Guemouria N, et al. Combinging FAO-56 Model and Ground-Based Remote Sensing to Estimate Water Consumptions of Wheat Crop in A Semi-Arid Region[J]. Agricultural Water Management,2007,87(1):41-54.
Microsoft. "Visual Studio 2005 MSDN Library[M]. Microsoft Corporation,2005.
Raymond EE. Jongschaap. Run-time Calibration of Simulation Models by Integrating Remote Sensing Estimates of Leaf Area Index and Canopy Nitrogen[J]. European Journal of Agronomy.2006, 24(4):316-324.
StSckle CO, Donatelli M, Nelson R. CropSyst, a cropping systems simulation model[J]. European Journal of Agronomy,2000,18:289-307.
Timsina J, Humphreys E. Performance of CERES-Rice and CERES-Wheat Models in Rice-Wheat Systems:A Review [J]. Agricultural Systems,2006,90:5-31.
北京超图地理信息技术有限公司.理解SuperMap IS.NET[M].北京超图地理信息技术有限公司,2006.
曹卫星,朱艳,田永超,等.数字农作技术研究的若干进展与发展方向[J].中国农业科学,2006,39(2)：281-288.
程明华,陈建平.3S技术在农业中的应用[J].山西农业科学,2006,34(2)：15-17.
郭银巧,赵传德,刘小军,等.基于模型和GIS技术的数字棉作系统的设计与实现[J].农业工程学报,2008,24(11)：139-144.
蒋红军,刘小军,汤亮,等.基于模型和3S技术的数字麦作系统的设计与实现[J].麦类作物学报,2007,27(5)：908-913.
姜晓剑,刘小军,田永超,等.基于遥感影像的作物生长监测系统的设计与实现[J].农业工程学报,2010,26(3)：156－163.
李卫国,李秉柏,石春林.基于模型和遥感的水稻长势监测研究进展[J].中国农学通报,2006,22(9)：457-461.
林忠辉,莫兴国,项月琴.作物生长模型研究综述[J].作物学报,2003,29(5)：750-758.
刘小军,朱艳,姚霞,等.基于WebGIS的农田生产环境质量评价系统研究[J].中国农业科学,2005,38(3)：551-557.
刘小军,朱艳,姚霞,等.基于WebGIS的农业空间信息管理及辅助决策系统[J].农业工程学报,2006,22(5)：125-129.
马玉平,王石立,张黎,等.基于遥感信息的华北冬小麦区域生长模型及模拟研究[J].气象学报,2005,63(2)：204-215.
潘瑜春,王纪华,赵春江,等.基于网络GIS的作物品质监测与调优栽培系统[J].农业工程学报,2004,20(6)：120-123.
沙宗尧,边馥苓.“3S"技术的农业应用与精细农业工程[J].测绘通报,2003,(6)：29-31.
王纯枝,宇振荣,辛景峰,等.基于遥感和作物生长模型的作物产量差估测[J].农业工程学报,2005,21(7)：84-89.
维埃拉R. SQL Server 2005高级程序设计[M].董明,译.北京：人民邮电出版社,2008.
沃特S,波尔顿C. SQL Server 2005'性能调优[M].齐宁,董泽惠,译.北京：清华大学出版社,2009.
谢云,James R Kiniry.国外作物生长模型发展综述[J].作物学报,2002,28(2)：190-195.
张宏森,朱征宇.四层B/S结构及解决方案[J].计算机应用研究,2002,(9)：20-22.
赵艳霞,秦军,周秀骥.遥感信息与棉花模型相结合反演模型初始值和参数的方法研究[J].棉花学报,2005,17(5)：280-284.
周留根,周治国,曹卫星,等.基于知识模型和GIS的棉花生产潜力评价系统[J].棉花学报,2005,17(2)：112－121.
周治国,孟亚利,曹卫星.基于知识模型和GIS的作物生产潜力评价[J].中国农业科学,2005,38(6)：1142-1147.
朱艳,曹卫星,王其猛,等.基于知识模型和生长模型的小麦管理决策支持系统[J].中国农业科学,2004,37(6)：814-820.
Burrough PA, McDonnell RA. Principles of Geographical Information System[M]. New York:Oxford University Press,1998.
Chavas DR, Izaurralde RC, Thomas AM, et al. Long-term climate change impacts on agricultural productivity in eastern China[J]. Agricultural and Forest Meteorology,2009,149:1118-1128.
Collins FC, Bolstad PV. A comparison of spatial interpolation techniques in temperature estimation [EB/OL]. (2004-12-31) [2009-09-08]. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/ sf_papers/collins_fred/collins.html.
Engel T, Hoogenboom G, Jones JW, et al.1997. AEGIS/WIN:A computer program for the application of crop simulation models across geographic areas[J]. Agronomy J.1997,89:919-928.
Guo Ping, Jia Yunde, Lyu M R. A study of regularized gaussian classifier in high-dimension small sample set case based on MDL Principle with application to spectrum recognition[J]. Pattern Recognition,2008,41(9):2842-2854.
Hartkamp D, Beurs KD, Stein A, et al. Interpolation Techniques for Climate Variables[M]. Mexico: International Maize and Wheat Improvement Center,1999.
He YQ, Wilfred HT, Zhang ZL, et al. Asynchronous Holocene climatic change across China:a review[J]. Quaternary Research,2004,61(1):52-63.
Hujmans RJ, Cameron SE, Parra JL, et al. Very high resolution interpolated climate surfaces for global land areas[J]. International Journal of Climatology,2005,25:1965-1978.
Hutchinson MF, Gessler PE. Splines-more than just a smooth interpolator[J]. Geoderma,1994, 62:45-67.
Hutchinson MF. Interpolation of rainfall data with thin plate smoothing splines[J]. Journal of Geographic Information and Decision Analysis,1998,2(2):139-167.
Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy,2003,18:235-265.
Keating BA, Carberry PS, Hammer GL, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy,2003,18:267-288.
Krige DG. A statistical approach to some mine valuations problems at the Witwatersrand[J]. Journal of the Chemical, Metallurgical and Mining Society of South Africa,1951,52:119-138.
LamNS. Spatial interpolation method:a review[J]. The American Cartographer,1983,10(2):129-149.
Li Ying, Vodacek A, Zhu Yushan. An automatic statistical segmentation algorithm for extraction of fire and smoke regions[J]. Remote Sensing of Environment,2007,108(2):171 - 178.
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models[J]. Agriculture, Ecosystems and Environment,2009,131:281-291.
Richard WK. Techniques for estimating uncertainty in climate change scenarios and impact studies[J]. Climate Research,2002,20:167-185.
Shackley S, Young P, Parkinson S, et al. Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling:Are GCMs the Best Tools?[J]. Climatic Change,1998,38(2):159-205.
Stockle CO, Donatelli M, Nelson R. CropSyst, a cropping systems simulation model[J]. European Journal of Agronomy,2000,18:289-307.
Tao FL, Hayashi Y, Zhang Z, et al. Global warming, rice production, and water use in China:Developing a probabilistic assessment[J]. Agricultural and Forest Meteorology,2008,148:94-110.
Thomas A. Climate change and potential agricultural productivity in China[J]. Erdkunde,2006, 60:157-172
Wahba G, Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation[J]. Monthly Weather Review,1980,108:1122-1145.
安琼,杨邦杰,焦险峰.作物遥感识别中的数据挖掘[J].农业工程学报,2007,23(8)：181－186.
陈惠,王加义,林晶,等.基于GIS的福建省双季稻气候——土壤生产潜力[J].生态学杂志,2008,27(7)：1104-1108.
郭银巧,赵传德,刘小军,等.基于模型和GIS技术的数字棉作系统的设计与实现[J].农业工程学报,2008,24(11)：139-144.
居红云,张俊本,李朝峰,等.基于K-means与SVM结合的遥感图像全自动分类方法[J].计算机应用研究,2007,24(11)：318－320.
李映雪,朱艳,曹卫星.不同施氮条件下小麦冠层的高光谱和多光谱反射特征[J].麦类作物学报,2006,26(2)：103－108.
刘志红,李领涛,Mc Vicar TR,等.专用气候数据空间插值软件ANUSPLIN及其应用[J].气象,2008,34(2)：92-100.
卢爱刚,庞德谦,康世明,等.全球变暖背景下中国气温时空差异响应[J].干旱区资源与环境,2009,23(8)：61-65.
蒙继华,吴炳方,李强子,等.运行化的农作物长势遥感监测系统[J].高技术通讯,2007,17(1)：94－99.
潘瑜春,王纪华,赵春江,等.基于网络GIS的作物品质监测与调优栽培系统[J].农业工程学报,2004,20(6)：120－123.
任国玉,徐铭志,初子莹,等.近50年来中国地面气候变化基本特征[J].气象学报,2005,63(6)：942-956.
唐国力,任国玉.近百年中国地表气温变化趋势的再分析[J].气候与环境研究,2005,10(4)：791-798.
谢立勇,林而达.二氧化碳浓度增高对稻、麦品质影响研究进展[J].应用生态学报,2007,18(3)：659-664.
谢云,王晓岚,林燕,等.近40年中国东部地区夏秋粮作物农业气候生产潜力时空变化[J].资源科学,2003,25(2)：7-13.
徐新刚,吴炳方,蒙继华,等.农作物单产预测系统的设计和实现[J].计算机工程,2008,34(9)：283-285.
徐怡,李龙澍,李学俊.基于粗燥熵和K-均值聚类算法的图像分割[J].华东理工大学学报：自然科学版,2007,33(2)：255-258.
薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学,2003,36(7)：807－812.
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.
薛利红,朱艳,张宪,等.利用冠层反射光谱预测小麦籽粒品质指标的研究[J].作物学报,2004,30(10)：1036－1041.
阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2)：163-169.
张晋科,张凤荣,张琳,等.中国耕地的粮食生产能力与粮食产量对比研究[J].中国农业科学,2006,39(11)：2278-2285.
张淑梅,张建明,李丁鲁,等.高抗性淀粉粳稻新品系稻米淀粉链长分布与主要品质特性差异[J].中国农业科学,2009,42(6)：2237-2243.
周小成,汪小钦.遥感影像数据挖掘研究进展[J].遥感信息,2005,3：58－62.
周治国,孟亚利,曹卫星.基于GIS的作物生产管理信息系统[J].中国农业科学,2005,38(6)：1142-1147.
庄立伟,王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5)：605-615.