## 5 Conclusions  
This study used a parameter-optimized MLR model to invert GPP. It also combined the station data of PAR, VPD, Tair, Tsoil, and SWC under different ecosystems and weather conditions with satellite data, ultimately revealing the spatiotemporal characteristics and influencing factors of GPP in arid regions. Specifically, the satellite products of PAR, VPD, Tair, Tsoil, and SWC could capture 65-98% of the environmental factor changes in different ecosystems. MODIS GPP could capture 65-82% of the GPP changes. The GPP inverted by the MLR model could capture over 80% of the GPP changes in three ecosystems. These products and inversion results had good applicability at the spatial scale. In addition, in different ecosystems, GPP fluctuated the most during the growing season (from March to September) and remained stable during other periods. The GPP had relatively high attribute values during periods of strong sun radiation and in areas with high vegetation coverage. Moreover, in grassland areas, PAR was the main influencing factor of GPP under all weather conditions. In desert vegetation areas, GPP was mainly affected by PAR on sunny days, by VPD on overcast days, and by Tair on rainy days. In farmland areas, PAR, VPD, Tair, and Tsoil jointly influenced GPP under different weather conditions. At the spatial scale, the spatial correlation between PAR, VPD, Tair, Tsoil, and GPP was more significant in areaswith high vegetation coverage but low sun radiation, while the spatial correlation between SWC and GPP was more significant in areas affected by artificial irrigation. The distribution characteristics of low-value spatial correlations between environmental factors and GPP were the opposite. Furthermore, in farmland areas, the comprehensive impact of environmental factors on GPP could be represented by a linear model. However, in other ecosystems, these impacts could be expressed based on linear (for VPD) and nonlinear patterns. This study might have been affected by the extremely minor errors remaining after the interpolation and quality control of the station data, as well as the differences in the resolution of remote sensing products. In the future, longer time series station data can be used, and the spatio-temporal scale of satellite products can be further extended through machine learning methods. This will help to uncover the spatiotemporal characteristics of GPP and its influencing factors over a longer time span and at a higher spatial resolution. Conceptualization: W.L., Y.W. and Y.L. ; Data curation: W.L., J.G. and A.A.; Investigation: W.L. ; Methodology: W.L. and Y.L. ; Resources: W.L. and Y.W. ; Project administration: [PERSON]; Software: [PERSON] and [PERSON]; Visualization: [PERSON], [PERSON] and C.W. ; Writing--original draft: [PERSON]; Writing--review and editing: [PERSON], [PERSON] and [PERSON]; Supervision: [PERSON], [PERSON] and [PERSON]; Funding acquisition: [PERSON]; Formal analysis: F.Y., C.Z., W.H., C.J. and H.S. All authors have read and agreed to the published version of the manuscript. This study was supported by the \"Transhan Talent\" Training Program-Science and Technology Innovation Team (Tianshan Innovation Team) Project (Grant No. 2022 TSYCTD0007), the National Natural Science Foundation of China (Grant No. 42305132, 42375084), the Youth Innovation Team of the China Meteorological Administration (Grant No. CMA2024 QN13), Xinjiang Uygur Autonomous Region Key Research and Development Program: 2023B02004-2, the 2024 Detection Center Observation Test Program Project (GCSYJH24-01, GCSYJH24-11, GCSYJH24-18), and the Special Funds for Basic Scientific Research Business Expenses of Central-level Public Welfare Scientific Research Institutes (IDM2021001). The data used in this paper will be provided by [PERSON] ([EMAIL_ADDRESS]) upon request. The in situ observation data were obtained from the Institute of Desert Meteorology, China Meteorological, Urumqi. We extend our sincere appreciation for their substantial contributions to the completion of this paper.