Remote Sensing Based Grassland Net Primary Productivity Analysis

Grassland resource monitoring is an important element of grassland ecological monitoring and management, with the aim of grasping the basic situation of grasslands and providing strong data support for the formulation of scientific grassland environmental protection and management measures in the future. This paper focuses on the grassland region of northern Shaanxi, China, aiming to study the long-term spatial and temporal patterns of net primary productivity (NPP) in the region, the response mechanism of NPP to climatic factors, and the future trends of NPP under the existing environmental drivers. Using remote sensing data such as Normalized Difference Vegetation Index (NDVI), meteorology and solar radiation, were used to derive the distribution of NPP in northern Shaanxi, China, during 2010-2020 using the improved CASA model, and the effects of meteorological factors on it were analyzed by correlation analysis. The coupled CASA and CA-Markov model were used to simulate the future trends of NPP and predict future changes in the study area at the pixel level. The results showed that the NPP of grassland in northern Shaanxi, China, generally showed an increasing trend during 2010-2020; NPP had a significant correlation with precipitation, mainly positive, with a correlation coefficient of 0.21; The Kappa coefficient achieved a value of 0.7263, indicating good applicability of the model and its potential use in studying the future change trend of NPP.