Juliette Bernard, Catherine Prigent, Carlos Jimenez, F. Frappart, C. Normandin, P. Zeiger, Yi Xi, Shushi Peng
{"title":"评估 30 年来 GIEMS-2 卫星衍生地表水范围的时变性","authors":"Juliette Bernard, Catherine Prigent, Carlos Jimenez, F. Frappart, C. Normandin, P. Zeiger, Yi Xi, Shushi Peng","doi":"10.3389/frsen.2024.1399234","DOIUrl":null,"url":null,"abstract":"Inland waters, especially wetlands, play a crucial role in biodiversity, water resources and climate, and contribute significantly to global methane emissions. This study investigates the seasonal and inter-annual variability of the 0.25° × 0.25° surface water extent (SWE) from the Global Inundation Extent from Multi-Satellites (GIEMS-2) extended to a 30-year time series (1992–2020). Comparison with MODIS-derived SWE, CYGNSS-derived SWE and the Global Lakes and Wetlands Database (GLWD) shows consistent spatial patterns globally and over 10 different basins, although there are discrepancies in extent, partly due to different resolutions of the initial satellite observations. Strong cross-correlation (>0.8) in seasonal variability is observed when comparing GIEMS-2 with MODIS, CYGNSS and river discharge in most of the basins studied. Encouraging similarities were found in the inter-annual variability in most basins (cross-correlation >0.6) between GIEMS-2 and MODIS over 20 years, and between GIEMS-2 and river discharge over long time series, including over the Amazon and the Congo basins. These results highlight the reliability of GIEMS-2 in detecting changes in SWE in different environments, especially under dense vegetation, making it a valuable resource for calibrating hydrological models and studying global methane emissions.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"17 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the time variability of GIEMS-2 satellite-derived surface water extent over 30 years\",\"authors\":\"Juliette Bernard, Catherine Prigent, Carlos Jimenez, F. Frappart, C. Normandin, P. Zeiger, Yi Xi, Shushi Peng\",\"doi\":\"10.3389/frsen.2024.1399234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inland waters, especially wetlands, play a crucial role in biodiversity, water resources and climate, and contribute significantly to global methane emissions. This study investigates the seasonal and inter-annual variability of the 0.25° × 0.25° surface water extent (SWE) from the Global Inundation Extent from Multi-Satellites (GIEMS-2) extended to a 30-year time series (1992–2020). Comparison with MODIS-derived SWE, CYGNSS-derived SWE and the Global Lakes and Wetlands Database (GLWD) shows consistent spatial patterns globally and over 10 different basins, although there are discrepancies in extent, partly due to different resolutions of the initial satellite observations. Strong cross-correlation (>0.8) in seasonal variability is observed when comparing GIEMS-2 with MODIS, CYGNSS and river discharge in most of the basins studied. Encouraging similarities were found in the inter-annual variability in most basins (cross-correlation >0.6) between GIEMS-2 and MODIS over 20 years, and between GIEMS-2 and river discharge over long time series, including over the Amazon and the Congo basins. These results highlight the reliability of GIEMS-2 in detecting changes in SWE in different environments, especially under dense vegetation, making it a valuable resource for calibrating hydrological models and studying global methane emissions.\",\"PeriodicalId\":502669,\"journal\":{\"name\":\"Frontiers in Remote Sensing\",\"volume\":\"17 7\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frsen.2024.1399234\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frsen.2024.1399234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
内陆水域,尤其是湿地,在生物多样性、水资源和气候方面发挥着至关重要的作用,并对全球甲烷排放有重大影响。本研究调查了从多卫星全球淹没范围(GIEMS-2)扩展到 30 年时间序列(1992-2020 年)的 0.25° × 0.25° 地表水范围(SWE)的季节和年际变化。与 MODIS 导出的 SWE、CYGNSS 导出的 SWE 以及全球湖泊和湿地数据库(GLWD)进行比较后发现,全球和 10 个不同流域的空间模式是一致的,但范围存在差异,部分原因是初始卫星观测的分辨率不同。在将 GIEMS-2 与 MODIS、CYGNSS 和所研究的大多数流域的河流排放量进行比较时,可以观察到季节变化的强交叉相关性(>0.8)。在大多数流域,GIEMS-2 与 MODIS 之间 20 年的年际变化率(交叉相关性大于 0.6)以及 GIEMS-2 与河流排放量之间的长时间序列(包括亚马逊河流域和刚果河流域)的相似性令人鼓舞。这些结果凸显了 GIEMS-2 在探测不同环境下,特别是在茂密植被下的 SWE 变化的可靠性,使其成为校准水文模型和研究全球甲烷排放的宝贵资源。
Assessing the time variability of GIEMS-2 satellite-derived surface water extent over 30 years
Inland waters, especially wetlands, play a crucial role in biodiversity, water resources and climate, and contribute significantly to global methane emissions. This study investigates the seasonal and inter-annual variability of the 0.25° × 0.25° surface water extent (SWE) from the Global Inundation Extent from Multi-Satellites (GIEMS-2) extended to a 30-year time series (1992–2020). Comparison with MODIS-derived SWE, CYGNSS-derived SWE and the Global Lakes and Wetlands Database (GLWD) shows consistent spatial patterns globally and over 10 different basins, although there are discrepancies in extent, partly due to different resolutions of the initial satellite observations. Strong cross-correlation (>0.8) in seasonal variability is observed when comparing GIEMS-2 with MODIS, CYGNSS and river discharge in most of the basins studied. Encouraging similarities were found in the inter-annual variability in most basins (cross-correlation >0.6) between GIEMS-2 and MODIS over 20 years, and between GIEMS-2 and river discharge over long time series, including over the Amazon and the Congo basins. These results highlight the reliability of GIEMS-2 in detecting changes in SWE in different environments, especially under dense vegetation, making it a valuable resource for calibrating hydrological models and studying global methane emissions.