Improved CASA model based on satellite remote sensing data: Simulating net primary productivity of Qinghai Lake Basin alpine grassland

The Carnegie-Ames-Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scale. However, the CASA is still driven by multi-source data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data, can conveniently provide real-time surface information at the regional scale, thus replacing ground observation data to drive CASA model. We attempted to improve the CASA model in this study using DEM data derived from radar RS and RS products data generated from Moderate 15 Resolution Imaging Spectroradiometer satellite sensor. We applied it to simulate the NPP of alpine grasslands in Qinghai Lake Basin, which is located in the northeastern Qinghai-Tibetan Plateau, China. The accuracy of the RS data driven CASA, with mean absolute percent error (MAPE) of 23.32% and root mean square error(RMSE) of 26.26 g C•m •month, was higher than that of the multi-source data driven CASA, with MAPE of 49.08% and RMSE of 65.21 g C•m•month. The NPP simulated by RS data driven CASA in July 2020 shows an average value of 110.17±26.25 g C•m•month, which is 20 similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapidly simulating grassland, farmland, forest, and other vegetation NPP to satisfy the requirements of precision agriculture, precision livestock farming, accounting carbon stocks, and other applications.