Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations

Pham-Duc, Binh; Prigent, Catherine; Aires, Filipe

doi:10.3390/w9060366

Cited by 117 publications

(93 citation statements)

References 25 publications

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“…This corresponds well with Clement et al [76] who also noted that VH outperformed VV polarization for turbid water mapping. Our study observed that the refined Lee speckle filter can suppress the speckle effect and maintain details of the water boundary [14], which is important for the identification of water pixels at the water/soil interface.…”

Section: Discussionmentioning

confidence: 80%

“…Satellite data can provide real-time, dynamic, and cost-effective information, and Earth observation procedures can be set up to provide operational (autonomous) monitoring of water resources [9,10]. Several methods have been proposed to classify surface water areas using either multispectral [9,11,12] or SAR remotely sensed data [13,14]. Popular techniques are image thresholding (rule-based classification) and supervised/unsupervised classification [15].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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S.M.alfieri-1@tudelft.nl (S.M.A.); M.Menenti@tudelft.nl (M.M.)Abstract: Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polari...

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Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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“…Sentinel‐1 Synthetic Aperture Radar (SAR) radar backscatter images were also classified for water by identifying pixels with VV polarization backscatter less than −14 decibels (dB) as containing water. Note that Pham‐Duc et al () found that −15 dB was suitable for Sentinel‐1 VV polarization water classification in the Mekong Delta. This threshold was adjusted to −14 dB because it resulted in water surface areas similar to the Landsat‐8 and Sentinel‐2 water classifications.…”

Section: Methodsmentioning

confidence: 98%

Assessing the Potential of the Surface Water and Ocean Topography Mission for Reservoir Monitoring in the Mekong River Basin

Bonnema

Hossain

2019

Water Resources Research

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The planned Surface Water and Ocean Topography (SWOT) mission, scheduled to launch in 2021, is designed to significantly improve on current lake and reservoir remote monitoring capacity. These improvements are especially important for the Mekong River Basin (MRB), where rapid dam construction puts downstream food security at risk and upstream dam operations are largely unknown to downstream water managers due to lack of upstream hydrologic information. This study aims to answer: How accurately will SWOT estimate reservoir storage change in the MRB? A physically based simulation tool developed by NASA Jet Propulsion Laboratory to mimic SWOT‐like observations of water bodies was applied to 20 reservoirs in the MRB and for 17 of those reservoirs, synthetic SWOT‐based storage change estimates demonstrated high skill, with errors typically less than 8%. The remaining three reservoirs showed higher errors due to complex neighboring topography or long narrow reservoir shape. In comparison with current satellite observations of six reservoirs in the MRB, the synthetic SWOT observations showed between 4% and 10% lower storage change normalized root‐mean‐square error. Finally, the simulated elevation and surface area was successfully used to estimate the area‐elevation relationships for each reservoir, with a median percent difference of 6.9%. These relationships are a potential avenue for multisensor cooperation toward improved understanding of reservoir dynamics. Such cooperation, where observations from many difference remote sensors are synthesized to provide a more complete view of human interactions on the river system, is vital for the MRB as more dams as human influence over the system increases.

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“…For example, Suzuoki et al () used SAR to map surface inundation area in the Louisiana coastal zone. Pham‐Duc et al () mapped surface water in the Mekong Delta using SAR and found that it compared well with floodability maps from high‐resolution topographic data. Despite the usefulness of SAR for inundation mapping, it does have some limitations.…”

Section: Methodsmentioning

confidence: 99%

Using Steady‐State Backwater Analysis to Predict Inundated Area from National Water Model Streamflow Simulations

Shastry

Egbert

Aristizabal

et al. 2019

J American Water Resour Assoc

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National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts for 2.7 million reaches in the National Hydrography Dataset for continental United States (U.S.). NWM uses Muskingum–Cunge channel routing, which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain more accurate estimates of streamflow and stage in rivers, especially for applications such as flood‐inundation mapping. Here, we used a steady‐state backwater version of Simulation Program for River NeTworks (SPRNT) model. We evaluated SPRNT’s and NWM’s abilities to predict inundated area for the record flood of Hurricane Matthew in October 2016. The Neuse River experienced record‐breaking floods and was well‐documented by U.S. Geological Survey. Streamflow simulations from NWM retrospective analysis were used as input for the SPRNT simulation. Retrospective NWM discharge predictions were converted to stage. The stages (from both SPRNT and NWM) were utilized to produce flood‐inundation maps using the Height Above Nearest Drainage method which uses the local relative heights to find out the local draining potentials and provide spatial representation of inundated area. The inundated‐area accuracies for NWM and SPRNT (based on comparison to a remotely sensed dataset) were 65.1% and 67.6%, respectively. These results show using steady‐state SPRNT results in a modest improvement of inundation‐forecast accuracy compared to NWM.

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Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations

Cited by 117 publications

References 25 publications

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Assessing the Potential of the Surface Water and Ocean Topography Mission for Reservoir Monitoring in the Mekong River Basin

Using Steady‐State Backwater Analysis to Predict Inundated Area from National Water Model Streamflow Simulations

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