Advanced technologies can improve the operational implementation of the Indian national crop insurance scheme, the Pradhan Mantri Fasal Bima Yojana (PMFBY), particularly in terms of accuracy and timeliness of the crop yield estimates that are used to determine yield losses at the Gram Panchayat (GP) level. In this study, conducted as a pilot test for PMFBY during the kharif season of 2018, technologies based on the Terrestrial Observation and Prediction System (TOPS) were tested and implemented for estimating GP-level crop yields of bajra (pearl millet, Pennisetum glaucum) in Firozabad District of Uttar Pradesh and rice (Oryza sativa) in Kendujhar District of Odisha. A combination of Synthetic Aperture Radar and optical data was used to map crop extent. Daily 2-km grids of input weather conditions were generated using a machine learning algorithm that incorporated station observations, satellite data, and reanalysis model outputs. Required crop biophysical estimates of leaf area index (LAI) and the fraction of intercepted photosynthetically active radiation (FPAR) were derived using daily cloud-screened MODIS 250-m data from the Terra and Aqua satellites and a modified MOD15 LAI/FPAR backup algorithm. A light-use-efficiency (LUE) model adapted from the MODIS (Moderate Resolution Imaging Spectroradiometer) algorithm (MOD17-GPP/NPP) was then used to spatially estimate crop yields. Crop extent maps, daily climate and gap-filled FPAR and the LUE model were used to estimate above-ground biomass, which was accumulated over the growing season and converted to crop yields using a crop-specific harvest index. The estimated yields at 250 m were aggregated within each GP and compared with crop yield data from crop cutting experiments (CCEs) conducted in 142 GPs for rice and 42 GPs for bajra. Crop extent mapping was 96% accurate in rice and 80% in bajra when validated with field surveys. A comparison of modeled yields with CCE yields showed a promising performance by the model in both crops (rice: r = 0.80, root-mean-square error (RMSE) = 411 kg/ha, mean absolute error (MAE) = 359 kg/ha, percent error (PE) = 7, Observed mean = 1500 kg/ha; Bajra: r = 0.84, RMSE = 309 kg/ha, MAE = 262 kg/ha, PE = −12.8, Observed mean = 1859 kg/ha). Although the approach showed promising results for both crops, further progress is needed to ensure consistent and reliable results. Some of the needed improvements include incorporating a dynamic crop calendar, improved maximum LUE estimates, and harvest index values that represent crop varietals grown in India. Routinely conducted CCEs in different crops and seasons around the country could provide a valuable resource for improving these parameters and, ultimately, crop yield estimates.

先进技术可以改善印度国家作物保险计划Pradhan Mantri Fasal Bima Yojana（PMFBY）的运营实施，尤其是在用来确定Gram Panchayat（GP）产量损失的作物产量估算的准确性和及时性方面） 等级。在这项研究中，作为2018年哈里夫季PMFBY的试点测试进行了测试，并测试了基于陆地观测和预测系统（TOPS）的技术，以估算bajra的GP级农作物产量（珍珠粟，青草（Pennisetum glaucum））在北方邦的菲罗扎巴德区和水稻（稻）在奥里萨邦的Kendujhar区。合成孔径雷达和光学数据的组合用于绘制作物范围图。使用机器学习算法生成每日2公里的输入天气状况网格，该算法结合了站点观测，卫星数据和重新分析模型的输出。使用来自Terra和Aqua卫星的每日经云筛查的MODIS 250-m数据和经过修改的MOD15 LAI / FPAR备用算法，得出了所需的作物生物物理叶面积指数（LAI）和被截断的光合有效辐射（FPAR）的生物物理估计值。然后，根据MODIS（中等分辨率成像光谱仪）算法（MOD17-GPP / NPP）改编的光利用效率（LUE）模型用于空间估算作物产量。作物范围图，每天的气候和缺口填充的FPAR和LUE模型用于估算地上生物量，该生物量是在生长季节积累的，并使用特定于作物的收获指数转化为作物产量。在每个GP内汇总了250 m处的估计产量，并与在142 GP的水稻和Bajra的42 GP进行的作物切割实验（CCE）的作物产量数据进行比较。经田间调查确认，水稻的作图精度在水稻中准确率为96％，在大麦芽庄中为80％。将模型化的产量与CCE产量进行比较，结果表明该模型在两种作物中均表现出良好的前景（水稻：在每个GP内汇总了250 m处的估计产量，并与在142 GP的水稻和Bajra的42 GP进行的作物切割实验（CCE）的作物产量数据进行比较。经田间调查确认，水稻的作图精度在水稻中准确率为96％，在大麦芽庄中为80％。将模型化的产量与CCE产量进行比较，结果表明该模型在两种作物中均表现出良好的前景（水稻：在每个GP内汇总了250 m处的估计产量，并与在142 GP的水稻和Bajra的42 GP进行的作物切割实验（CCE）的作物产量数据进行比较。经田间调查确认，水稻的作图精度在水稻中准确率为96％，在大麦芽庄中为80％。将模型化的产量与CCE产量进行比较，结果表明该模型在两种作物中均表现出良好的前景（水稻：r  = 0.80，均方根误差（RMSE）= 411 kg / ha，平均绝对误差（MAE）= 359 kg / ha，百分比误差（PE）= 7，观察到的均值= 1500 kg / ha；巴杰拉（Bajra）：r  = 0.84，RMSE = 309千克/公顷，MAE = 262千克/公顷，PE = -12.8，实测平均值= 1859千克/公顷）。尽管该方法对两种作物均显示出令人鼓舞的结果，但仍需要进一步的进展以确保结果一致且可靠。一些需要改进的方面包括合并动态作物日历，改进最大LUE估计值以及代表印度种植的作物品种的收获指数值。在全国不同作物和不同季节常规进行的CCE可以提供宝贵的资源，以改善这些参数，并最终提高作物产量的估计值。