Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing, storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic stresses. In this study, we used a suite of methods to assess yields at the village level (< 5 km²) using remote sensing technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques (SMTs) and crop management information collected from field surveys along with ground data. The locations of crop cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields (R² is greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models, crop yields at harvest could be successfully predicted.

作物产量估算对于通知物流管理（例如营养投入的处方，融资，储存和运输，销售）以及因非生物和生物胁迫导致的损失而进行的作物保险评估非常重要。在这项研究中，我们使用了一套方法来评估村级（<5 km ²）在印度的Telangana，安得拉邦和奥里萨邦使用遥感技术和作物建模。遥感产品是使用Sentinel-2和Landsat 8时间序列数据生成的，并使用从农民田间收集的数据进行了校准。我们得出的地图显示了作物范围，作物生长阶段和叶面积指数（LAI）的空间变化，这对产量评估至关重要。使用光谱匹配技术（SMT）对Sentinel-2时间序列数据进行农作物分类，并从田间调查中收集农作物管理信息以及地面数据。根据作物范围图确定了作物切割实验（CCE）的位置。使用Landsat 8次序列数据，基于SAVI（土壤调整后的植被指数）方程式得出LAI。我们使用基于遥感叶面积指数（LAI）的多次迭代，使用了作物模拟模型的重新参数化技术。数据同化方法有助于微调作物生长模型的初始参数，并借助遥感观测结果改善模拟效果。结果清楚地表明，观察到的和模拟的农作物产量之间具有良好的相关性（对于所有研究的农作物，R ²均大于0.7）。我们的研究表明，通过将遥感数据同化到作物模型中，可以成功地预测收获时的作物产量。