作物空间分布“一张图”是我国农情遥感监测业务的重要工作，监测结果可为农业生产定量化、科学化管理提供基础数据。

本研究在多年农作物种植面积遥感监测的基础上，根据河南省地形地貌特征和作物种植特点，优选不同区域的遥感数据和分类方法，采用Sentinel-2、Landsat 8-OLI、GF-6 WFV等中高分辨率遥感数据，制作了2019年河南省玉米、花生、水稻和大豆等秋季主要作物空间分布“一张图”，并基于地面调查数据进行了监测精度分析。

河南省秋季作物主要包括玉米、花生、水稻和大豆，玉米种植面积最大，花生次之，秋季作物种植结构比较复杂，主要包括玉米单作、玉米-花生-大豆混作、水稻单作等种植模式。“一张图”总体精度和Kapaa系数分别为86.13%和0.83，基本符合省级尺度作物种植面积监测业务工作要求。

本研究从遥感数据源优选方案、作物分类方法、地面调查和精度评价等方面进行了深入的探讨，将小尺度作物种植结构遥感监测从技术研究层面拓展到大尺度业务应用层面，为我国大尺度作物空间分布“一张图”业务化遥感制作提供技术支撑。

Mapping the spatial distribution of all crops, using remote sensing, is important for monitoring agricultural production in China. The classification results provide basic data for quantitative and scientific management.

The spectral characteristics of different autumn crops are known from years of experience using remote sensing to monitor crop planting areas. A combined map of crop spatial distribution in Henan Province in 2019 was made using multi-source remote sensing and different classification methods, based on topographic and geomorphic characteristics and crop planting structures. The classification crops include corn, peanuts, rice and soybeans, and the multi-source images include Sentinel-2, Landsat 8-OLI, GF6 WFV with different spatial resolutions. The accuracy of the classification results was verified using ground samples and survey data.

The planting structure of autumn crops is complicated, including single crops of corn and rice, as well as mixed crops of corn, peanuts and soybeans. In the study area, corn was planted over the largest area, followed by peanuts. The overall accuracy and Kappa coefficient of the combined map were 86.13% and 0.83, respectively, which met the basic requirements for monitoring crops at provincial scale.

The paper addresses the optimization of remote sensing data sources, classification methods, ground surveys and precision verification approaches. In this study, the technical ability to monitor crops using remote sensing of planting structures was tested in a large-scale business application. It offers technical support for using remote sensing to produce a combined map at large scale.