{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:07:44Z","timestamp":1776179264740,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Memorial University of Newfoundland and the research funding provided by VPR\/SGS Pilot program and the Natural Sciences and Engineering Research Council (NSERC) Discovery","award":["RGPIN-2022-04766"],"award-info":[{"award-number":["RGPIN-2022-04766"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha\u22121 was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems.<\/jats:p>","DOI":"10.3390\/rs16050831","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T07:56:02Z","timestamp":1709106962000},"page":"831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-9591","authenticated-orcid":false,"given":"Mohammadali","family":"Hemati","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7234-959X","authenticated-orcid":false,"given":"Masoud","family":"Mahdianpari","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"},{"name":"C-Core, St. John\u2019s, NL A1B 3X5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6473-8712","authenticated-orcid":false,"given":"Hodjat","family":"Shiri","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Faculty of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X7, Canada"}]},{"given":"Fariba","family":"Mohammadimanesh","sequence":"additional","affiliation":[{"name":"C-Core, St. John\u2019s, NL A1B 3X5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1890\/110004","article-title":"A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2","volume":"9","author":"Mcleod","year":"2011","journal-title":"Front. 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