## 6 Conclusions  
Based on an improved CASA model driven by the new \(k\)NDVI vegetation index, this paper estimated the NPP of China's terrestrial ecosystem vegetation from 2001 to 2020 and used the Theil-Sen median, Mann-Kendall trend, second-order partial correlation, multiple correlation analysis, differential equation method, and PLS-SEM method to analyze the temporal and spatial evolution of vegetation NPP in China's terrestrial ecosystems. The results show that \(k\)NDVI can effectively improve the saturation problem, cope with complex phenological changes, and improve the precision of NPP estimation. The NPP of Chinese vegetation presented a fluctuating upward trend on the time scale from 2001 to 2020. It increased by a mean of 0.7235 g C/m\({}^{2}\) each year, showing a low northwest to high southeast pattern in the spatial distribution, with the highest average multi-year NPP value by province in Taiwan Province and the lowest in Xinjiang Uygur Autonomous Region. The changes in NPP were dominated by an increasing trend (72.96%), and the areas showing significant and very significant increases were concentrated in the Northeast Plain, Loess Plateau, and Sichuan Basin. In Northeast and North China, vegetation NPP was mainly influenced by precipitation, while in Southwest and South China it was mainly influenced by temperature and in Central China by solar radiation. With the passage of time, the dominant factors influencing the changes in NPP change and have different impacts. The main influencing factors of the climate in 2001-2005 were evaporation and precipitation, and the main influencing factors of the climate in 2005-2010 were evaporation and temperature; moreover, the influences of both phases were positive. However, in 2010-2015 the impact \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Remote-Sensing Indicator Sources** & **Impact Factors** \\ \hline This study & NDVI (MOD13Q1) & temperature, precipitation, solar radiation, evaporation, GDP, population, pH, clay, silt, DEM, slope \\ \hline
[45] & NPP (MOD17A3 HGF) & - \\ \hline
[48] & - & temperature, precipitation \\ \hline
[44] & NDVI (AVHRR/MOD13A3) & precipitation, relative humidity, sunshine hours, temperature, wind speed \\ \hline
[43] & NPP (MOD17A3 HGF) & temperature, precipitation \\ \hline
[36] & NPP (MOD17A3 HGF) & temperature, precipitation \\ \hline
[49] & FAP (MCD43A1 and MCD43A2) & temperature, precipitation \\ \hline
[46] & ANPP (ChinaNPP\_1985\_2015) & temperature, precipitation \\ \hline
[47] & NPP(Aqua-MODIS) & temperature, Chlorophyll-a, photosynthetically available radiation, relative surface density, wind stress curl, salinity, nitrates, phosphates \\ \hline
[50] & NDVI (GIMMS) & CO\({}_{2}\) concentration, climate, land use change \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with other NPP research. of the climate was negative. Climate change and human activities have a major positive effect on NPP change. The areas where the NPP increased due to climate change were mainly located in Northeast and North China, and the areas where the NPP increased due to human activities were mainly located in Central and South China. The results of this research can be used in China and around the world to better prepare for and respond to climate change. S.Q. : Conceptualization, Methodology, Software, Formal analysis, Project administration, Funding acquisition, Investigation, Writing--original draft, Writing--review and editing. H.Z. : Investigation, review. M.Z. : Data curation, Investigation, Funding acquisition, Writing--original draft. All authors have read and agreed to the published version of the manuscript. This study was funded by the National Natural Science Foundation of China (41901385), the Natural Science Foundation of Hunan Province of China (2022)J40873), and in part by the Education Department of Hunan Province of China (21A0177). Not applicable. The authors declare that they have no conflict of interest related to this work.