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However, accurate quantification of soil respiration (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    )\u2014the primary CO\n                    <jats:sub>2<\/jats:sub>\n                    efflux from forests\u2014remains challenging due to existing studies' overreliance on annual\u2010scale estimates, which obscure fine\u2010scale spatiotemporal dynamics and key drivers of\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    . Here, we developed a 500\u2009m resolution monthly\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    dataset for China's forests (2000\u20132020) using remote sensing data and a geographically weighted machine learning model. A geographically weighted extreme gradient boosting model achieved the highest accuracy in predicting monthly\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.74, RMSE\u2009=\u20090.8\u2009g C m\n                    <jats:sup>\u22122<\/jats:sup>\n                    \u2009day\n                    <jats:sup>\u22121<\/jats:sup>\n                    ). The mean total annual\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    from 2000 to 2020 was 2.32\u2009\u00b1\u20090.09 Pg C year\n                    <jats:sup>\u22121<\/jats:sup>\n                    , with summer contributing most and winter least. Annual total\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    and seasonal total\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    showed significant increasing trends across China's forests from 2000 to 2020, with the strongest increases in southern China's young\/middle\u2010aged natural management forests and plantations. The relationships between\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    and its driving factors varied: gross primary productivity (GPP) was the primary driver of annual\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    across all forest management and age classes. Seasonally, temperature dominated\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    in spring\/winter, and GPP dominated summer\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    across all forest management and age classes, while precipitation effects varied with management\/age classes and season. Our findings highlight the necessity of monthly\u2010scale analysis and the significant role of forest management and age in modulating\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>s<\/jats:sub>\n                    variability.\n                  <\/jats:p>","DOI":"10.1111\/gcb.70698","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:42:26Z","timestamp":1768819346000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Monthly Soil Respiration in China's Forests: Spatiotemporal Patterns and Drivers Over 21\u2009Years"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9770-7866","authenticated-orcid":false,"given":"Jiaming","family":"Chang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute, Chinese Academy of Sciences  Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ni","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute, Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute, Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5245-7813","authenticated-orcid":false,"given":"Luying","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang Ecological Environment Monitoring Center  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute, Chinese Academy of Sciences  Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute, Chinese Academy of Sciences  Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute, Chinese Academy of Sciences  Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dewen","family":"Cai","sequence":"additional","affiliation":[{"name":"The Third Surveying and Mapping Institute of Hunan Province, Hunan Geospatial Information Engineering and Technology Research Center Hunan Engineering Research Center of Geographic Information Security and Application  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2017.05.076"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.15376\/biores.19.3.6095\u20106119"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate3204"},{"key":"e_1_2_10_5_1","first-page":"369","volume-title":"Measurement of Soil Respiration Using Closed Chamber Method: An IRGA Technique","author":"Bekku Y.","year":"1995"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.soilbio.2010.02.020"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1029\/2018JG004613"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1029\/2023JG007637"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/gcb.15353"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature08930"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.5194\/bg\u201011\u20106173\u20102014"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41893\u2010019\u20100220\u20107"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep19693"},{"key":"e_1_2_10_15_1","doi-asserted-by":"crossref","unstructured":"Chen T. andC.Guestrin.2016.Xgboost: A Scalable Tree Boosting System. 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