Improved Regional Yield Prediction by Crop Growth Monitoring System Using Remote Sensing Derived Crop Phenology

Dynamic process-based crop simulation models are useful tool in predicting crop growth and yield in response to environmental and cultural factors but are constrained by lack of availability of the required large number of inputs when applied for regional studies. In this study we report (a) development of a prototype Crop Growth Monitoring System (CGMS) for wheat using WTGROWS simulation model on a 5’X5’ grid in GIS environment for generating daily crop growth maps and predicting district-wise grain yield, (b) demonstration of a technique for estimating date of sowing (DOS) using RS-derived spectral-temporal crop growth profiles and CGMS simulation capability and (c) evaluation of the capability of CGMS for spatial yield mapping and district level yield prediction for Haryana State during 2000-01 crop season. The technique for estimating district-wise DOS matched the RS-derived date of peak NDVI (from multi-date WiFS sensor aboard IRS-1D satellite) to date of peak LAI simulated in CGMS for a range of plausible dates of sowing. The peak date of NDVI was computed by fitting Badhwar model to the multi-date NDVI values. The CGMS performance was evaluated by incorporating RS-derived date of sowing in predicting district level wheat yields with and without use of district-wise N fertilizer application rate computed from district-wise fertilizer consumption statistics. The correlation between district yield simulated by CGMS and official State Department of Agriculture (SDA) estimates was only 0.163 when constant median/mean inputs of DOS, N fertilizer and irrigation application were specified for all the districts. The correlation increased to 0.52 when RS-CGMS-derived district-wise DOS was used as input and further increased to 0.74 when information from consumption statistics of N fertilizer use was additionally specified. Thus, the study demonstrated the derivation of crop sowing dates from RS inputs using crop simulation based CGMS and its incorporation into CGMS framework along with spatial variation in other crop management inputs for improving yield prediction. * Corresponding Author