{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:09:08Z","timestamp":1776179348377,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["31472020"],"award-info":[{"award-number":["31472020"]}]},{"name":"National Natural Science Foundation of China","award":["31772485"],"award-info":[{"award-number":["31772485"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g\u00b7m\u22122 and R2 of 0.844 for RF, RMSE of 112.425 g\u00b7m\u22122 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g\u00b7m\u22122, RMSE = 125.879 g\u00b7m\u22122 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 \u00d7 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.<\/jats:p>","DOI":"10.3390\/rs13081595","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T13:58:04Z","timestamp":1618927084000},"page":"1595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0847-3710","authenticated-orcid":false,"given":"Chunhua","family":"Li","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration (Anhui University), Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-4658","authenticated-orcid":false,"given":"Lizhi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration (Anhui University), Hefei 230601, China"}]},{"given":"Wenbin","family":"Xu","sequence":"additional","affiliation":[{"name":"Management Bureau of Anhui Shengjin Lake National Nature Reserve, Chizhou 247210, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/0034-4257(95)00085-F","article-title":"Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine","volume":"53","author":"Sader","year":"1995","journal-title":"Remote Sens. 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