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By adopting non-multicollinearity vegetation indices (feature sets) from PlanetScope and Sentinel-2, and using benthic seaweed biomass inverted from multispectral UAV imagery as the label set for satellite pixel biomass values, machine learning methods (Gradient boosting decision tree, GBDT) can effectively improve the accuracy of biomass estimation results for Ulva pertusa and Sargassum thunbergii species (Ulva pertusa, RSentinel22 = 0.74, RPlanetScope2 = 0.8; Sargassum thunbergii, RSentinel22 = 0.88, RPlanetScope2 = 0.69). The average biomasses of Ulva pertusa and Sargassum thunbergii in the intertidal zone of Gouqi Island are 456.84 g\/m2 and 2606.60 g\/m2, respectively, and the total resources are 3.5 \u00d7 108 g and 1.4 \u00d7 109 g, respectively. In addition, based on the hyperspectral data, it was revealed that a major source of error is the patchy distribution of seaweed.<\/jats:p>","DOI":"10.3390\/rs15184428","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:57:17Z","timestamp":1694159837000},"page":"4428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Satellite Imagery-Estimated Intertidal Seaweed Biomass Using UAV as an Intermediary"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3113-7961","authenticated-orcid":false,"given":"Jianqu","family":"Chen","sequence":"first","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510310, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tropical Marine Ecosystem and Bioresource, MNR, Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai 536007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, MNR, East China Sea Environmental Monitoring Center, Shanghai 201206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xunmeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai 201306, China"},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510310, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","unstructured":"Diazpulido, G.J., and Mccook, L. 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