Comparative Analysis of Satellite Based Models for the Assessment of Gross Primary Productivity in Rainfed Cotton Agroecosystem

The largest flux and a crucial player in the terrestrial carbon cycle is the gross primary productivity (GPP). This study aimed to evaluate the effectiveness of different satellite based models for estimating the GPP of cotton agroecosystems in the Nagpur district of Maharashtra, India. To assess the spatial distribution and the estimation of GPP of rainfed cotton crop of the study area, three year’s satellite data of Landsat-8/9 and Sentinel-2A/B along with eddy covariance flux data were used. Two light use efficiency (LUE) based algorithms, namely vegetation photosynthesis model (VPM) and moderate resolution imaging spectroradiometer (MODIS), as well as a vegetation index (VI) based greenness and radiation (GR) model, were employed for GPP estimation. The overall accuracy for year wise cotton crop mapping were 91.6, 95.7 and 96.8% during 2019-20, 2020-21 and 2021-22, respectively. Results showed that the highest accumulation of GPP of 1000 gC m^-2 occurred during the 2021-22 crop growth period. The average GPP of cotton was 400 gC m^-2 of the study area. The coefficient of determination (R²) between model and flux estimated GPP was 0.92, 0.87 & 0.83 for VPM, MODIS algorithm and GR models, correspondingly, during the growing season of 2019-20. Whereas, the R² was 0.94, 0.90 and 0.89 during 2020-21 and 0.96, 0.92 and 0.90 during 2021-22 for VPM, MODIS algorithm and GR models, respectively. So, the average R² value for VPM, MODIS and GR models were 0.94, 0.90 and 0.87, individually. The validation of model derived GPP with flux estimated GPP of cotton crop shows that the VPM model provides a better result as compared to the MODIS algorithm and GR models.