## 5 Conclusions  
This study investigated the spatiotemporal characteristics of WUE and driving factors in the STC from 1982 to 2018. The main results are as follows. (1) The annual average WUE of the STC was 1.57 g C/m\({}^{2}\)/mm, and the annual average WUE of woodland was slightly higher than that of grassland and cropland. Meanwhile, from 1982 to 2018, the annual cropland WUE showed an upward trend while the annual grassland WUE trend was relatively stable. The annual WUE of all other landscape types showed a downward trend. (2). MAT and LAI had strong positive direct effects on WUE, while VPD showed a strong negative direct effect on WUE. In addition, the total effect of LAI on the WUE trend was very strong because both the indirect and direct effects were positive. The opposing indirect and direct effects cancelled each other out, resulting in the total effect of Ca and VPD on WUE trends being positive. The influence of SWC on WUE trends is mainly through indirect effects. The influence of MAT, MAP, and Rg on the WUE trend is mainly through direct effects, because the opposite indirect effects of their different paths are offset. (3). LAI had a major contribution to the increasing trend of WUE, whereas Ca and VPD had a major contribution to the decreasing trend of WUE. LAI was the primary driving factor of WUE change in the STC and dominated WUE changes in more than 50% of the total area. Ca and VPD were also important driving factors, while other drivers contributed less to the WUE trend. This study deepens our understanding of the impact of changes in the climatic environment and biological factors of vegetation on the annual variation in WUE. These findings can be used to guide ecological rehabilitation and management under a future changing climate in subtropical regions. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs14174296/s1](https://www.mdpi.com/article/10.3390/rs14174296/s1)]([https://www.mdpi.com/article/10.3390/rs14174296/s1](https://www.mdpi.com/article/10.3390/rs14174296/s1)). Figure S1: Comparison of GPP and ET based on FLUXNET and GLASS. Figure S2: Spatial pattern of significance of interannual trends in WUE (a), GPP (b), and ET (c) in subtropical China from 1982 to 2018. Figure S3. Time series of annual WUE (a), GPP (b), and ET(c) in different landscapes of subtropical China. Figure S4: Spatial pattern of the regression coefficients for the driving factors GPP (a), ET (b), LAI (c), Ca (d), SWC(e), VPD (f), MAT (g), MAP (h), and Rg (i) in subtropical China. Figure S5: Spatial pattern of ridge parameter \"a\" (a), coefficient of determination \"R2\" (b). Figure S6: Spatial pattern of the normalized interannual trends in GPP (a), ET (b), LAI (c), Ca (d), SWC (e), VPD (f), MAT (g), MAP (h), and Rg (i) in subtropical China from 1982 to 2018. Table S1: Main data. Table S2: Eddy covariance (EC) flux sites used for model verification [62; 63; 64; 65; 66; 67]. Table S3. All the variables with abbreviations and explanations used in this study. **Author Contributions:** J.X. ([PERSON]); Data curation, J.X. ([PERSON]); Formal analysis, J.X. ([PERSON]) and J.X. ([PERSON]); Funding acquisition, B.X. ; Methodology, J.X. ([PERSON]) and K.Z. ; Project administration, B.X. ; Software, [PERSON] ([PERSON]), K.Z., and C.L. ; Supervision, [PERSON]; Validation, [PERSON]; Visualization, [PERSON] ([PERSON]) and [PERSON]; Writing--original draft, [PERSON] ([PERSON]); Writing--review and editing, [PERSON], [PERSON], and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (grant number U19A2051).