Nonlinear effects of surface soil moisture changes on vegetation greenness over the Tibetan plateau

As an essential link between terrestrial and climatic ecosystems, vegetation has been altered by the soil hydrological environment associated with frozen soil thaw. However, it is not clear whether fluctuating soil moisture (SM) within the frozen soil zone alters the hydrologic environment to alleviate water stress in plants further, and there are scant previous studies at large scale on whether there is a threshold for SM on vegetation greening. This study integrated SM monitoring data at 125 stations from existing studies, then quantified the advantages of six remote sensing/reanalysis SM products: QTP-DNN-Sm, Global Land Data Assimilation System (GLDAS-Noah), European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land), European Space Agency Climate Change Initiative (ESA CCI), Global-SM, and QTP-SM. Moreover, we assessed the influence of single and multiple regional environmental elements (temperature, precipitation, land surface temperature (LST), normalized difference vegetation index (NDVI), and snow) on SM, as well as identified four trends of SM and vegetation growth for 51.26% of the Tibetan Plateau (TP). The results are as follows: 1) The overall performance of QTP-DNN-Sm products was slightly better than that of Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, and ERA5-Land, with higher median Pearson correlation coefficient (R) value (0.685, 0.686, 0.699, 0.704, 0.300, and 0.582 for QTP-DNN-Sm, Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, and ERA5-Land, respectively) and lowest median unbiased Root Mean Square Error (ubRMSE) (0.061, 0.064, 0.068, 0.064, 0.042, 0.076, and 0.047 m3/m3 for QTP-DNN-Sm, Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, ERA5-Land, and ESA CCI, respectively). 2) NDVI in the frozen soil zone was the best variable to explain SM based on the GeoDetector-based factor detection, and interaction detection results indicated that the interaction between NDVI and temperature was gradually emerging to explain SM from permafrost zones to seasonally frozen ground zones. 3) The nonlinear relationship function between SM and NDVI showed that vegetation growth in 47.76% of the area (mainly distributed in the Changjiang River, Yarlung Zangbo River, and Yellow River basins) was more influenced by phenology. Thresholds existed in 3.49% of TP, where the cumulative effect of SM affects vegetation growth. In 0.65% of the regions, vegetation growth experienced eco-physiological processes of positive relief of water stress and physical processes of negative damage. The ease with which SM altered vegetation growth trends was consistent with the degradation degree of frozen soil type. Although the percentage of regions where the thresholds exist is relatively small, the positive/negative effects of the complex localized intersection between SM and vegetation in these regions could threaten the balance and stability of fragile alpine ecosystems sustained by permafrost.