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Chang Huang published an article in August 2018.
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(2012 - 2018)
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Article 1 Read 0 Citations Seeing Surface Water From Space Published: 01 August 2018
Eos, doi: 10.1029/2018eo103123
Satellite-based optical sensors can detect, measure and monitor changes in lakes, reservoirs, rivers and wetlands, providing useful data with multiple applications for science and society.
Article 0 Reads 3 Citations Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review Published: 06 June 2018
Reviews of Geophysics, doi: 10.1029/2018rg000598
Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite‐based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio‐temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio‐temporal fusion have been developed to alleviate this contradiction. Spatio‐temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with Synthetic Aperture Radar (SAR) data. Digital Elevation Model (DEM) data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques make the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multi‐source data is the future direction for improved global and regional water monitoring.
Article 0 Reads 0 Citations GPM卫星降水数据在黑河流域的适用性评价 Published: 01 January 2018
JOURNAL OF NATURAL RESOURCES, doi: 10.31497/zrzyxb.20171180
Article 4 Reads 0 Citations Analysis of the use of NDWI green and NDWI red for inland water mapping in the Yellow River Basin using Landsat-8 OLI im... Published: 27 June 2017
Remote Sensing Letters, doi: 10.1080/2150704X.2017.1341664
Article 0 Reads 2 Citations Impact of land use change on profile distributions of organic carbon fractions in peat and mineral soils in Northeast Ch... Published: 01 May 2017
CATENA, doi: 10.1016/j.catena.2016.12.022
Article 3 Reads 3 Citations A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data Published: 30 April 2017
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi6050140
The Sentinel-1 mission provides frequent coverage of global land areas and is hence able to monitor surface water dynamics at a fine spatial resolution better than any other Synthetic Aperture Radar (SAR) mission before. However, SAR data acquired by Sentinel-1 also suffer from terrain effects when being used for mapping surface water, just as other SAR data do. Terrain indices derived from Digital Elevation Models (DEMs) are easy but effective approaches to reduce this kind of interference, considering the close relationship between surface water movement and topography. This study compares two popular terrain indices, namely the Multi-resolution Valley Bottom Flatness (MrVBF) and the Height Above Nearest Drainage (HAND), toward their performance on assisting surface water mapping using Sentinel-1 SAR data. Four study sites with different terrain characteristics were selected to cover a very wide range of topographic conditions. For two of these sites that are floodplain dominated, both normal and flooded scenarios were examined. MrVBF and HAND values for the whole study areas, as well as statistics of these values within water areas were compared. The sensitivity of applying different thresholds for MrVBF and HAND to mask out terrain effect was investigated by adopting quantity disagreement and allocation disagreement as the accuracy indicators. It was found that both indices help improve water mapping, reducing the total disagreement by as much as 1.6%. The HAND index performs slightly better in most of the study cases, with less sensitivity to thresholding. MrVBF classifies low-lying areas with more details, which sometimes makes it more effective in eliminating false water bodies in rugged terrain. It is therefore recommended to use HAND for large scale or global scale water mapping. However, for water detection in complex terrain areas, MrVBF also performs very well.