Wheat is cultivated on more than 2.7 million hectares in Afghanistan annually, yet the country is dependent on imports to meet domestic demand. The timely estimation of domestic wheat production is highly critical to address any potential food security issues and has been identified as a priority by the Ministry of Agriculture Irrigation and Livestock (MAIL). In this study, we developed a system for in-season mapping of wheat crop area based on both optical (Sentinel-2) and synthetic aperture radar (SAR, Sentinel-1) data to support estimation of wheat cultivated area for management and food security planning. Utilizing a 2010 Food and Agriculture Organization (FAO) cropland mask, wheat sown area for 2017 was mapped integrating decision trees and machine learning algorithms in the Google Earth Engine cloud platform. Information from provincial crop calendars in addition to training and validation data from field-based surveys, and high-resolution Digitalglobe and Airbus Pleiades images were used for classification and validation. The total irrigated and rainfed wheat area were estimated as 912,525 and 562,611 ha, respectively for 2017. Province-wise accuracy assessments show the maximum accuracy of irrigated (IR) and rainfed (RF) wheat across provinces was 98.76 and 99%, respectively, whereas the minimum accuracy was found to be 48% (IR) and 73% (RF). The lower accuracy is attributed to the unavailability of reference data, cloud cover in the satellite images and overlap of spectral reflectance of wheat with other crops, especially in the opium poppy growing provinces. While the method is designed to provide estimation at different stages of the growing season, the best accuracy is achieved at the end of harvest using time-series satellite data for the whole season. The approach followed in the study can be used to generate wheat area maps for other years to aid in food security planning and policy decisions.

中文翻译：

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谷歌地球引擎云环境下基于光学和SAR时间序列图像的阿富汗小麦面积制图

阿富汗每年种植小麦的面积超过 270 万公顷，但该国依赖进口来满足国内需求。及时估算国内小麦产量对于解决任何潜在的粮食安全问题至关重要，农业灌溉和畜牧部 (MAIL) 已将其确定为优先事项。在这项研究中，我们开发了一个基于光学 (Sentinel-2) 和合成孔径雷达 (SAR, Sentinel-1) 数据的小麦作物面积季节性测绘系统，以支持小麦种植面积的估算，以实现管理和粮食安全规划。利用 2010 年粮食及农业组织 (FAO) 农田掩码，在谷歌地球引擎云平台中集成决策树和机器学习算法绘制了 2017 年小麦播种面积图。除了来自实地调查的培训和验证数据之外，来自省级作物日历的信息以及高分辨率 Digitalglobe 和空中客车昴宿星团图像也用于分类和验证。2017 年灌溉和雨育小麦总面积估计分别为 912,525 公顷和 562,611 公顷。 各省精度评估显示，各省灌溉 (IR) 和雨育 (RF) 小麦的最大精度分别为 98.76% 和 99%，而发现最低准确度为 48% (IR) 和 73% (RF)。精度较低的原因是参考数据不可用、卫星图像中的云层覆盖以及小麦与其他作物的光谱反射重叠，特别是在罂粟种植省份。虽然该方法旨在提供生长季节不同阶段的估计，但使用整个季节的时间序列卫星数据在收获结束时实现最佳准确度。研究中采用的方法可用于生成其他年份的小麦面积图，以帮助制定粮食安全规划和政策决策。