{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T09:06:31Z","timestamp":1779181591561,"version":"3.51.4"},"reference-count":81,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Projects of the Inner Mongolia Autonomous Region, China"},{"DOI":"10.13039\/501100015308","name":"Science and Technology Projects of the Xizang Autonomous Region, China","doi-asserted-by":"publisher","award":["XZ202402ZD0001"],"award-info":[{"award-number":["XZ202402ZD0001"]}],"id":[{"id":"10.13039\/501100015308","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Program of Qinghai Province","award":["2024-ZJ-904"],"award-info":[{"award-number":["2024-ZJ-904"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Under the pronounced warming\u2013wetting trend in Northwest China, understanding vegetation responses to the redistribution of hydrothermal resources is essential for interpreting regional ecohydrological processes. Here, we developed a bivariate Long Short-Term Memory (LSTM) model to simulate leaf area index (LAI) dynamics for four representative vegetation types (cold temperate forest, shrubland, grassland, and cropland), using air temperature and soil moisture as predictors. The model reproduces seasonal vegetation phenology well across vegetation types (R2 &gt; 0.9), indicating that LSTM effectively captures the cumulative and lagged effects of hydrothermal drivers. However, its performance diverges at the interannual scale. Interannual variability in grasslands in water-limited environments is reasonably represented (R2 = 0.31), consistent with their sensitivity to short-term hydroclimatic variability under warming\u2013wetting conditions. In contrast, the model fails to reproduce the observed long-term greening trend in forests when driven solely by hydrothermal variables. This contrast suggests distinct underlying mechanisms across ecosystem types. Grassland dynamics are closely linked to high-frequency hydroclimatic variability, whereas forest growth appears to be governed by slower processes and low-frequency drivers, including CO2 fertilization, nitrogen deposition, and ecological inertia. As a result, hydrothermal variables alone are insufficient to explain long-term forest dynamics. Overall, these findings highlight a transition from water-limited to energy- and process-limited controls across vegetation types and underscore the limitations of purely climate-driven models. Integrating biogeochemical processes or process-based constraints into machine learning frameworks may therefore be necessary to improve predictions of long-term vegetation change under climate change.<\/jats:p>","DOI":"10.3390\/plants15101542","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:35:43Z","timestamp":1779176143000},"page":"1542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Differential Effects of Soil Moisture and Air Temperature on Vegetation Dynamics in Northwest China\u2019s Warming and Wetting Region: An LSTM Modeling Approach"],"prefix":"10.3390","volume":"15","author":[{"given":"Yajun","family":"Si","sequence":"first","affiliation":[{"name":"College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1853-8614","authenticated-orcid":false,"given":"Junpo","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Yangtze University, Wuhan 430100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Yangtze University, Wuhan 430100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-4352","authenticated-orcid":false,"given":"Jesus","family":"Carrera","sequence":"additional","affiliation":[{"name":"Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Cient\u00edficas (CSIC), Jordi Girona 18, 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiming","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Yangtze University, Wuhan 430100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihua","family":"Bai","sequence":"additional","affiliation":[{"name":"Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China"},{"name":"Key Laboratory of Grassland and Agricultural Ecological Remote Sensing, Ministry of Agriculture and Rural Affairs, Hohhot 010010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and climate change: Forcings, feedbacks, and the climate benefits of forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating soil moisture\u2013climate interactions in a changing climate: A review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth-Sci. 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