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Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing
Agricultural Systems  (IF5.37),  Pub Date : 2021-10-25, DOI: 10.1016/j.agsy.2021.103299
Rajkumar Dhakar, Vinay Kumar Sehgal, Debasish Chakraborty, Rabi Narayan Sahoo, Joydeep Mukherjee, Amor V.M. Ines, Soora Naresh Kumar, Paresh B. Shirsath, Somnath Baidya Roy


An accurate crop yield forecast with sufficient lead time is critical for various applications, such as crop management, resources mobilization, agri-commodity trading, crop insurance, etc. Accurate yield forecasting well ahead of harvest at field scale with minimal field input data remains a challenge.


This study aimed to develop a novel prototype wheat yield forecasting system by assimilating remote sensing (RS) derived crop parameters and weather forecast into InfoCrop-Wheat crop simulation model (CSM), using minimum field measurements.


The CSM was calibrated and validated at both research farm and farmers' fields. The crop LAI was retrieved through inversion of the PROSAIL radiative transfer model from Sentinel-2A and Landsat-8 images and validated using in-situ LAI measurements. The CSM was modified to test assimilation of RS derived LAI through “Ensemble Kalman Filtering” (EnKF) and “Forcing” strategies at multiple time-steps. The RS derived LAI was not only used to correct/replace model simulated LAI but other model state variables were also adjusted accordingly. A major challenge of adjusting crop phenology based on RS derived LAI was also attempted. The WRF weather forecast was bias-corrected and incorporated into the modified model-LAI assimilation framework. Generic crop management inputs were specified to the model. Finally, the study demonstrated a workable prototype of a field scale wheat growth and yield forecasting system under limited field data availability.


The inversion of PROSAIL showed an RMSE of 0.56 m2/m2 in LAI retrievals. Model validation with measured inputs showed normalized error (NE) of 6‐–8% in grain yield. The proposed framework showed only 2%, 5%, 3% and 1% higher NE in simulating days to anthesis, days to physiological maturity, dry matter and grain yield, respectively, than with measured inputs. The “EnkF” outperformed “Forcing” for predicting crop yield as well as phenology and growth of wheat using generic management inputs. The system showed acceptable accuracy in forecasting phenology, dry matter and yield of wheat at field scale when weighted adaptive bias-correction of weather forecast was incorporated with a 15 days lead time.


The prototype can be scaled-up for wheat and other crops for predicting real-time crop condition and yield losses at farmers' field for a range of applications, notably, crop-insurance, resources allocation, targeted agro-advisories and triggering contingency plans. It offers considerable potential for objective assessment of crops in the marginal and smallholder systems supporting the smart farming paradigm.