气候变化领域集成服务门户(CCPortal): A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region

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DOI	10.5194/hess-23-3037-2019
	A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region
	Li L.; Skidmore A.; Vrieling A.; Wang T.
发表日期	2019
ISSN	1027-5606
起始页码	3037
结束页码	3056
卷号	23 期号:7
英文摘要	Detailed knowledge on surface water distribution and its changes is of high importance for water management and biodiversity conservation. Landsat-based assessments of surface water, such as the Global Surface Water (GSW) dataset developed by the European Commission Joint Research Centre (JRC), may not capture important changes in surface water during months with considerable cloud cover. This results in large temporal gaps in the Landsat record that prevent the accurate assessment of surface water dynamics. Here we show that the frequent global acquisitions by the Moderate Resolution Imaging Spectrometer (MODIS) sensors can compensate for this shortcoming, and in addition allow for the examination of surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIS cell, we developed a global rule-based regression model for estimating the surface water fraction from a 500 m nadir reflectance product from MODIS (MCD43A4). The model was trained and evaluated with the GSW monthly water history dataset. A high estimation accuracy (R2 D 0:91, RMSE D 11:41 %, and MAE D 6:39 %) was achieved. We then applied the algorithm to 18 years of MODIS data (2000-2017) to generate a time series of surface water fraction maps at an 8 d interval for the Mediterranean. From these maps we derived metrics including the mean annual maximum, the standard deviation, and the seasonality of surface water. The dynamic surface water extent estimates from MODIS were compared with the results from GSW and water level data measured in situ or by satellite altimetry, yielding similar temporal patterns. Our dataset complements surface water products at a fine spatial resolution by adding more temporal detail, which permits the effective monitoring and assessment of the seasonal, inter-annual, and long-term variability of water resources, inclusive of small water bodies. © 2019 Author(s).
语种	英语
scopus关键词	Biodiversity; Conservation; Radiometers; Regression analysis; Spectrometers; Surface water resources; Time series; Water levels; Water management; Water supply systems; Biodiversity conservation; European Commission; Long-term variability; Mediterranean region; Moderate resolution imaging spectrometers; Monitoring and assessment; Rule based regression models; Similar temporal patterns; Surface waters; accuracy assessment; cloud cover; data set; estimation method; European Commission; fractionation; groundwater-surface water interaction; Landsat; Mediterranean environment; MODIS; satellite data; surface water; time series analysis; water management
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159643
作者单位	Li, L., Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, Netherlands; Skidmore, A., Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, Netherlands, Department of Environmental Sciences, Macquarie UniversityNSW, Australia; Vrieling, A., Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, Netherlands; Wang, T., Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, Netherlands
推荐引用方式 GB/T 7714	Li L.,Skidmore A.,Vrieling A.,et al. A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region[J],2019,23(7).
APA	Li L.,Skidmore A.,Vrieling A.,&Wang T..(2019).A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region.Hydrology and Earth System Sciences,23(7).
MLA	Li L.,et al."A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region".Hydrology and Earth System Sciences 23.7(2019).