{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:56:54Z","timestamp":1760144214028,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T00:00:00Z","timestamp":1711756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm for AGB downscaling that integrated a ML for AGB mapping with another for residual mapping. The accuracies of six bias-corrected ensemble ML algorithms were evaluated at resolutions of 0.05\u00b0, 0.025\u00b0, and 0.01\u00b0. Moreover, a step-by-step downscaling (SBSD) method was introduced, utilizing bias-corrected ensemble ML algorithms to downscale AGB from 0.1\u00b0 to 0.05\u00b0, 0.025\u00b0, and 0.01\u00b0 resolutions and was compared with the direct downscaling (DD) at three scales. A comparative analysis was conducted in the Daxing\u2019anling Mountains and Xiaoxing\u2019anling Mountains. AGB and corresponding uncertainty maps at three scales were generated using SBSD. The results showed that the efficacy of the XGBoost-based AGB model combined with the random forest-based residual correction model was superior. Spatial patterns in AGB maps generated by SBSD and DD were found to be similar. Notably, SBSD yielded enhanced accuracy in the Daxing\u2019anling Mountains with complex topography, while both performed comparably in the Xiaoxing\u2019anling Mountains with milder topography, highlighting SBSD\u2019s advantages in high heterogeneity areas.<\/jats:p>","DOI":"10.3390\/rs16071228","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:28:00Z","timestamp":1711891680000},"page":"1228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6932-3877","authenticated-orcid":false,"given":"Jingjing","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-5770","authenticated-orcid":false,"given":"Yuzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev-ecolsys-110512-135914","article-title":"The structure, distribution, and biomass of the world\u2019s forests","volume":"44","author":"Pan","year":"2013","journal-title":"Annu. 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