{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:18:24Z","timestamp":1774966704258,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Aerospace Research Centre"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Biomass is a crucial indicator of the carbon sequestration capacity of a vegetation ecosystem. Its dynamic is of interest because it impacts on the carbon cycle, which plays an important role in the global climate and its changes. This work presents a novel technique, able to transfer a calibrated regression model between different areas by exploiting an active learning methodology and using Shannon\u2019s entropy as a discriminator for sample selection. Model calibration is performed based on a reference area for which an extended ground truth is available and implemented via regression bootstrap. Then, re-calibration samples for model transfer are selected through active learning, allowing for choosing a limited number of points to be investigated for training data collection. Different sampling strategies and regression techniques have been tested to demonstrate that a significant reduction in the number of calibration samples does not affect the estimation performance. The proposed workflow has been tested on a dataset concerning Finnish forests. Experimental results show that the joint exploitation of regression ensembles and active learning dramatically reduces the amount of field sampling, providing aboveground biomass estimates comparable to those obtained using literature techniques, which need extended training sets to build reliable predictions.<\/jats:p>","DOI":"10.3390\/rs15215138","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T09:56:36Z","timestamp":1698400596000},"page":"5138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Forest Aboveground Biomass Estimation Using Machine Learning Ensembles: Active Learning Strategies for Model Transfer and Field Sampling Reduction"],"prefix":"10.3390","volume":"15","author":[{"given":"Donato","family":"Amitrano","sequence":"first","affiliation":[{"name":"Italian Aerospace Research Centre, Via Maiorise snc, 81043 Capua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7143-1244","authenticated-orcid":false,"given":"Giovanni","family":"Giacco","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy"},{"name":"Latitudo 40 srl, Via Emanuele Gianturco 31C, 80142 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-0377","authenticated-orcid":false,"given":"Stefano","family":"Marrone","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1079-7741","authenticated-orcid":false,"given":"Antonio Elia","family":"Pascarella","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy"}]},{"given":"Mattia","family":"Rigiroli","sequence":"additional","affiliation":[{"name":"Latitudo 40 srl, Via Emanuele Gianturco 31C, 80142 Naples, Italy"},{"name":"Department of Civil, Chemical and Environmental Engineering, University of Genova, Via Montallegro 1, 16145 Genova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8176-6950","authenticated-orcid":false,"given":"Carlo","family":"Sansone","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/s41586-018-0300-2","article-title":"The tropical forest carbon cycle and climate change","volume":"559","author":"Mitchard","year":"2018","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Franklin, S.E. 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