Crop Yield Assessment of Smallholder Farms Using Remote Sensing and Simulation Modelling

Abstract

Among the various constraints experienced by the small farms on their management and policy intervention, an estimate of crop yields is critical for decision-making relating to agro-advisories, crop insurance, resource use, profitability, supply chains, etc. Remotely sensed data could be effectively integrated into empirical models to assess yields. We present a case study to map the wheat yield of small farms over a region using the InfoCrop simulation model and remote sensing derived leaf area index (LAI). The study employed look-up-table (LUT) based inversion of the radiative transfer model approach for estimation of LAI from remote sensing images and used a simulation model to develop empirical biometric relation between simulated LAI and simulated crop yields. The simulation model-derived biometric relation was applied to the farms in a region using remote sensing-derived LAI to estimate work. Both LUT-based LAI estimation and biometric relation-based yield estimation is computationally efficient and showed an accuracy better than 10 percent for the monitored farms. The approach was extended to a large district of Ganganagar in India to capture yield variations in a good crop year versus a bad crop year. The approach presented has the potential for easy adoption for operational use by agencies.