To improve the prediction of crop yields at an aggregate scale, we developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimilation. The core of the framework is an Ensemble Kalman Filter (EnKF) used to control crop model runs, assimilate remote sensing (RS) data and update model state variables. We modified the Decision Support System for Agro-technology Transfer – Cropping System Model (DSSAT-CSM)-Maize model (Jones et al., 2003) to be able to stop and start simulations at any given time in the growing season, such that the EnKF can update model state variables as RS data become available. The data assimilation-crop modeling framework was evaluated against 2003–2009 maize yields in Story County, Iowa, USA, assimilating AMSR-E soil moisture and MODIS-LAI data independently and simultaneously. Assimilating LAI or soil moisture independently slightly improved the correlation of observed and simulated yields (R=0.51 and 0.50) compared to no data assimilation (open-loop; R=0.47) but prediction errors improved with reductions in MBE and RMSE by 0.5 and 0.5Mgha−1 respectively for LAI assimilation while these were reduced by 1.8 and 1.1Mgha−1 for soil moisture assimilation. Yield correlation improved more when both soil moisture and LAI were assimilated (R=0.65) suggesting a cause–effect interaction between soil moisture and LAI, prediction errors (MBE and RMSE) were also reduced by 1.7 and 1.8Mgha−1 with respect to open-loop simulations. Results suggest that assimilation of LAI independently might be preferable when conditions are extremely wet while assimilation of soil moisture+LAI might be more suitable when conditions are more nominal. AMSR-E soil moisture tends to be more biased under the presence of high vegetation (i.e., when crops are fully developed) and that updating rootzone soil moisture by near-surface soil moisture assimilation under very wet conditions could increase the modeled percolation causing excessive nitrogen (N) leaching hence reducing crop yields even with water stress reduced at a minimum due to soil moisture assimilation. However, applying the data assimilation-crop modeling framework strategically by considering a-priori information on climate condition expected during the growing season may improve yield prediction performance substantially, in our case with higher correlation (R=0.80) and more reductions in MBE and RMSE (2.5 and 3.3Mgha−1) compared to when there is no data assimilation. Scaling AMSR-E soil moisture to the climatology of the model did not improve our data assimilation results because the model is also biased. Better soil moisture products e.g., from Soil Moisture Active Passive (SMAP) mission, may solve the soil moisture data issue in the near future

Agriculture (arguably the backbone of India's economy) is highly dependent on the spatial and temporal distribution of monsoon rainfall. This paper presents an analysis of crop–climate relationships for India, using historic production statistics for major crops (rice, wheat, sorghum, groundnut and sugarcane) and for aggregate food grain, cereal, pulses and oilseed production. Correlation analysis provides an indication of the influence of monsoon rainfall and some of its potential predictors (Pacific and Indian Ocean sea‐surface temperatures, Darwin sea‐level pressure) on crop production. All‐India annual total production (except sorghum and sugarcane), and production in the monsoon (except sorghum) and post‐monsoon seasons (except rice and sorghum) were significantly correlated to all‐India summer monsoon rainfall. Monsoon season crops (except sorghum) were strongly associated with the three potential monsoon predictors. Results using state‐level crop production statistics and subdivisional monsoon rainfall were generally consistent with the all‐India results, but demonstrated some surprising spatial variations. Whereas the impact of subdivisional monsoon rainfall is strong in most of the country, the influence of concurrent predictors related to El Niño–southern oscillation and the Indian Ocean sea‐surface temperatures at a long lead time seem greatest in the western to central peninsula. Copyright © 2004 Royal Meteorological Society