提出了适于运行化农作物单产预测的方法。即以农作物单产区划为基础 ,通过搜集不同地区不同作物的单产预测模型 ,分析每个模型的空间适用范围 ,并从模型参数等角度筛选模型 ,然后利用这些模型进行气象站点的作物单产预测 ,并以NDVI分布图为参考数据将点上的单产数据空间外推到区域尺度。借助耕地分布估计区域水平的农作物单产。最后以 2 0 0 3年冬小麦为例 ,进行了全国 10个省的冬小麦平均单产估算 ,花费了较少的人力和时间 ,符合运行化遥感估产要求

In this paper, the authors develop an operational method to predict crop yield in China. Crop yield stratification is fundament, in which each stratum has own yield model for different crops. The level of crop yield (winter wheat, corn, rice, et al.) as well as physical factors of temperature, precipitation, soil type and sun radiation are considered. There are about 11 strata in China at the first level based on physical factors, 39 strata at the second level based on crop yield and 133 strata at the third level based on agro-meteorology stratification. Literature study goes review has been made through the journals and books since 1980s for collecting agro-meteorological models and relevant application area. There are 114 models for wheat, 25 models for maize, 70 models for rice and 36 models for soybean. For every model, the suitable area has been defined by considering the original application area and crop yield stratification, and the parameters are generated by regression method of historical crop yield data and meteorological data. The crop yield prediction is stratum by stratum. To one stratum, there are many meteorological stations and counties. It is impossible to do the model calibration for each station or each county due to the lack of data. It may have the yield data for each county, but it is difficult to have the meteorological data at the same period for this county. Only those counties with both yield and meteorological data are selected to calibrate the yield model. The yield predictions are done for those counties. Spatial interpolation is used to extrapolate the yield at a station to whole county or whole stratum. Each pixel has its own yield data. The non-arable land is masked with landuse map and the average yield at a county or a stratum is calculated. At the end of this paper, a case study is presented to predict the yield of winter wheat in 2003.