Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer

The decision support system for agro-technology transfer (DSSAT) is a worldwide crop modeling platform used for crops growth, yield, leaf area index (LAI), and biomass estimation under varying climatic, soil and management conditions. This study integrates DSSAT with satellite remote sensing (RS) data to estimates canopy state variables like LAI and biomass. For LAI estimation, Moderate Resolution Imaging Spectroradiometer (MODIS) product (MCD15A3H for LAI and MOD17A2 / MOD17A3 products for biomass) are used. Field data for Sheikhupura district is provided by National Agriculture Research Council (NARC) and used for the calibration and validation of the model. The results indicate strong agreement between the DSSAT and RS derived estimates. Correlation coefficients (R²) for LAI varied from 0.82 to 0.90, while for biomass ranged from 0.92 to 0.99 over two farms and two growing seasons (2012-2014). The index of agreement (D-index) ranged from 0.79 to 0.96 across the two farms and two growing seasons (2012-2014) affirming the model's durability. However, the biomass estimated from RS data is underestimated due to saturation phenomenon in the optical RS. The performance metrics, comprising the coefficient of residual mass (CRM) and normalized root mean square error (nRMSE), further substantiate the approach utilized. This study will help decision and policymakers and researchers to apply geospatial techniques for the sustainable agriculture practices.