# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 5 Conclusions  
In this study, the diffuse fraction-based two-leaf light-use efficiency (DTEC) model was used to calculate China's gross primary productivity (GPP) from 2001 to 2020, and the contributions of climate change (temperature, precipitation, solar radiation, and aerosol optical depth) and human activities to China's GPP were further investigated. The results showed that the DTEC model has good applicability in China compared with the 10 flux sites, and its overall accuracy coefficients were R\({}^{2}\) = 0.85, RMSE = 41.12 g C m\({}^{-2}\) month\({}^{-1}\), and bias = 5.98 g C m\({}^{-2}\) month\({}^{-1}\), which achieved great accuracy for different vegetation types. From 2001 to 2020, 67.29% of China's regions showed an increasing trend in GPP, with an average increasing trend of 9.77 g C m\({}^{-2}\) yr\({}^{-1}\) (\(p\) < 0.01), and within different ecological regions, the increasing trend in GPP ranged from 20.65 to 2.03 g C m\({}^{-2}\) yr\({}^{-1}\) (\(p\) < 0.01). Temperature has a greater impact on GPP than precipitation, solar radiation, and aerosol optical depth under climate change, especially on the Qinghai-Tibet Plateau. In addition, multiple regression residual methods show that there is great spatial heterogeneity in the impacts of climate change and human activities on China's GPP. The impacts on GPP growth were 1.39 and 5.67 g C m\({}^{-2}\) yr\({}^{-1}\), respectively, and the relative contributions were 28% and 72%, respectively. Our conclusions show that human activities, mainly ecological restoration projects and agricultural intensification technologies, have been the main driving force behind China's GPP growth over the past 20 years. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16081361/s1](https://www.mdpi.com/article/10.3390/rs16081361/s1)]([https://www.mdpi.com/article/10.3390/rs16081361/s1](https://www.mdpi.com/article/10.3390/rs16081361/s1)), Figure S1: Inter-annual changes in China's GPP from 2001 to 2020, where DTEC stands for the simulated value of the DTEC model, SIF stands for the simulated value of the GO-SIF model, and MOD17 stands for the simulated value of the MOD17 model. Figure S2: Spatial distribution of the annual mean values of GPP from 2001 to 2020: GO-SIF (a), MOD17 (b), and DTEC (c). Among them, the darker the blue color, the higher the value of GPP in the region. Figure S3: Spatial distribution of GPP passing the significance test for 2001-2020: GO-SIF (a), MOD17 (b), and DTEC (c). Among them, the darker the blue color, the higher the trend of GPP growth in the region. Figure S4: Spatial distribution of land use types in China from 2001 (a) to 2020 (b) and conversion between different land use types from 2001 to 2020 (c). The land use types in China were resampled into six: forest, shrubland, savanna, grassland, crop, and non-vegetated area (NOA). Table S1: Grading of the impact of GPP\({}_{\text{CC}}\) and GPP\({}_{\text{HA}}\). Table S2: Determination Criteria for Drivers of China's GPP Changes and Calculation Method of Contribution Rate. **Author Contributions:** Methodology, writing original draft, J.P.; conceptualization, review and editing, Y.X. ; methodology and resources, N.P., Y.Z. and H.L. ; review and editing, F.Z. All authors contributed to the editing of the manuscript. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was financially supported by the Key Research and Development Program of the Xinjiang Uygur Autonomous Region (Grant Number: 2022B01012), the National Nature Science Foundation of China (Grant Number: 42301127), the Ph.D. Start-up Fund in Xinjiang University (Grant Number: 620321046), and the Tianchi Doctor Program of the Xinjiang Uygur Autonomous Region (Grant Number: TCBS202140). **Data Availability Statement:** Data sharing is not applicable to this article.