{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:56:48Z","timestamp":1760147808402,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171025","2023A1515012069"],"award-info":[{"award-number":["42171025","2023A1515012069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Resources and Environmental Information System and the Guangdong Natural Science Foundation Program","award":["42171025","2023A1515012069"],"award-info":[{"award-number":["42171025","2023A1515012069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO2 monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS\u00a0LST fusion images and a linear mixed effect model to estimate FSCO2 at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R2 = 0.47) of FSCO2 variability in the Monthly random effect model, while it only accounted for 19% of FSCO2 variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model\u2019s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R2 = 0.55 and t value &gt; 1.9) for FSCO2 variability and yielded the smallest deviation from observed FSCO2. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO2 monitoring in tropical and subtropical forests.<\/jats:p>","DOI":"10.3390\/rs15051415","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T01:43:00Z","timestamp":1677807780000},"page":"1415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Modeling Soil CO2 Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Xarapat","family":"Ablat","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Chong","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-2048","authenticated-orcid":false,"given":"Guoping","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"}]},{"given":"Nurmemet","family":"Erkin","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Xinjiang Agricultural University, Urumqi 830052, China"}]},{"given":"Rukeya","family":"Sawut","sequence":"additional","affiliation":[{"name":"College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.scitotenv.2016.04.124","article-title":"Extreme warm temperatures alter forest phenology and productivity in Europe","volume":"563","author":"Crabbe","year":"2016","journal-title":"Sci. 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