{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:49:38Z","timestamp":1773218978082,"version":"3.50.1"},"reference-count":211,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T00:00:00Z","timestamp":1602633600000},"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>Tropical forests are acknowledged for providing important ecosystem services and are renowned as \u201cthe lungs of the planet Earth\u201d due to their role in the exchange of gasses\u2014particularly inhaling CO2 and breathing out O2\u2014within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450\u2013650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and\/or forest cover change are related to spatial resolution; definition adapted for \u2018forest\u2019 classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.<\/jats:p>","DOI":"10.3390\/rs12203351","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-217X","authenticated-orcid":false,"given":"Sawaid","family":"Abbas","sequence":"first","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6439-6775","authenticated-orcid":false,"given":"Man Sing","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"},{"name":"Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8991-3970","authenticated-orcid":false,"given":"Jin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9424-8426","authenticated-orcid":false,"given":"Naeem","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9135-5143","authenticated-orcid":false,"given":"Syed","family":"Muhammad Irteza","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1175\/1520-0442(1991)004<0957:ADARCC>2.0.CO;2","article-title":"Amazonian deforestation and regional climate change","volume":"4","author":"Nobre","year":"1991","journal-title":"J. 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