{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:40:03Z","timestamp":1760146803619,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Emergency Satellite Engineering and Application, Ministry of Emergency Management","award":["CKSD20231247\/KJ","2023YFC3209502"],"award-info":[{"award-number":["CKSD20231247\/KJ","2023YFC3209502"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["CKSD20231247\/KJ","2023YFC3209502"],"award-info":[{"award-number":["CKSD20231247\/KJ","2023YFC3209502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning and four inversion methods and verified the effectiveness of different super resolution and inversion methods in three waterbodies based on HJ-2 hyperspectral images. Results indicated that it was feasible to use HJ-2 hyperspectral images with a higher spatial resolution via super resolution methods to estimate water depth. Deep learning improves the spatial resolution of hyperspectral images from 48 m to 24 m and shows less information loss with peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapper (SAM) values of approximately 37, 0.92, and 2.42, respectively. Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. The proposed method can be used for water depth estimation of different water bodies and can improve the spatial resolution of water depth mapping, providing refined technical support for water environment management and protection.<\/jats:p>","DOI":"10.3390\/rs16234607","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T10:11:47Z","timestamp":1733739107000},"page":"4607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4660-3996","authenticated-orcid":false,"given":"Shuangyin","family":"Zhang","sequence":"first","affiliation":[{"name":"Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4430-3287","authenticated-orcid":false,"given":"Kailong","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Emergency Satellite Engineering and Application, Ministry of Emergency Management, Beijing 100024, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6369-4994","authenticated-orcid":false,"given":"Xinsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Baocheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, China"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Emergency Satellite Engineering and Application, Ministry of Emergency Management, Beijing 100024, China"}]},{"given":"Changjun","family":"Gu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Emergency Satellite Engineering and Application, Ministry of Emergency Management, Beijing 100024, China"}]},{"given":"Jian","family":"Xu","sequence":"additional","affiliation":[{"name":"Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, China"}]},{"given":"Xuejun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan 430010, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111619","DOI":"10.1016\/j.rse.2019.111619","article-title":"Remote Sensing of Shallow Waters\u2013A 50 Year Retrospective and Future Directions","volume":"240","author":"Kutser","year":"2020","journal-title":"Remote Sens. 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