An improved casa model for estimating crop carbon sinks from remote sensing images

Accurately estimating crop carbon sinks at large regional scales from the perspective of remote sensing is of great significance for carbon neutrality research, crop yield estimation, and scientific agriculture. In this study, an improved Carnegie-Ames-Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate Net primary productivity. The NEP is then further calculated by coupling the soil respiration model to represent the carbon sink at a regional scale. The main research contents: (1) The month-by-month net primary productivity of crops in Jiangsu Province in 2021. (2) The net ecosystem productivity of crops in Jiangsu Province month by month in 2021.