{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:18:27Z","timestamp":1784222307847,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["42374017"],"award-info":[{"award-number":["42374017"]}]},{"name":"Natural Science Foundation of China","award":["SSKP202201"],"award-info":[{"award-number":["SSKP202201"]}]},{"name":"Shanghai Sheshan National Geophysical Observatory","award":["42374017"],"award-info":[{"award-number":["42374017"]}]},{"name":"Shanghai Sheshan National Geophysical Observatory","award":["SSKP202201"],"award-info":[{"award-number":["SSKP202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km2 and 813.18 km2, with those estimated TWS changes 17.37 cm and \u221217.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km2. The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE\/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently.<\/jats:p>","DOI":"10.3390\/rs16132408","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T08:17:29Z","timestamp":1719821849000},"page":"2408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Dynamic Monitoring of Poyang Lake Water Area and Storage Changes from 2002 to 2022 via Remote Sensing and Satellite Gravimetry Techniques"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-2599","authenticated-orcid":false,"given":"Fengwei","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai Sheshan National Geophysical Observatory, Shanghai 201602, China"},{"name":"State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haipeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Bayan Nur Natural Resources Surveying and Mapping Institute, Bayannur 015000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlin","family":"Wen","sequence":"additional","affiliation":[{"name":"Shanghai Sheshan National Geophysical Observatory, Shanghai 201602, China"},{"name":"Shanghai Earthquake Agency, Shanghai 200062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijian","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15829","DOI":"10.3402\/tellusa.v64i0.15829","article-title":"On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model","volume":"64","author":"Balsamo","year":"2012","journal-title":"Tellus A Dyn. 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