Remote Sensing for Water Resources and Environmental Management

被引:13
|
作者
Dube, Timothy [1 ]
Shekede, Munyaradzi D. D. [2 ]
Massari, Christian [3 ]
机构
[1] Univ Western Cape, Inst Water Studies, Dept Earth Sci, Private Bag X17, ZA-7535 Cape Town, South Africa
[2] Univ Zimbabwe, Dept Geog Geospatial Sci & Earth Observat GGEO, P Bag MP 167, Harare, Zimbabwe
[3] CNR, Via Madonna Alta 126, I-06128 Perugia, Italy
关键词
algorithms; climate change; droughts; environmental degradation; Earth observation; monitoring and assessment; sustainable management; water scarcity and security; VARIABILITY; DROUGHT;
D O I
10.3390/rs15010018
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales.
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页数:5
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