A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang, Chang; Nguyen, Ba Duy; Zhang, Shiqiang; Cao, Senmao; Wagner, Wolfgang

doi:10.3390/ijgi6050140

Cited by 41 publications

(26 citation statements)

References 43 publications

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“…DEM data can be valuable supplementary information to eliminate the confusion caused by shadows (Gianinetto et al, ; Qi et al, ). Terrain indices, such as Multi‐resolution Valley Bottom Flatness (MrVBF; Gallant & Dowling, ) and Height Above Nearest Drainage (HAND; Rennó et al, ), were developed to identify those areas with higher probability of water presence, and have provided excellent assistance for detecting surface water from remote sensing data (Donchyts et al, ; Huang et al, ; Mueller et al, ). More and more DEM data sources are becoming accessible, including SRTM DEM at up to 1 arc sec resolution and TanDEM‐X at 12 m resolution.…”

Section: Progresses and Challengesmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

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541

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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 data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.Plain Language Summary Observing surface water is essential for ecological and hydrological studies. 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. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

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Section: Progresses and Challengesmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

Self Cite

541

267

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“…Long et al (2014) and Clement et al (2017) have defined thresholds based on image differencing between wet and dry periods, where regions with the greatest negative change in backscatter were classified as inundated. Huang et al (2017) have proposed a threshold based on terrain indices derived from coarse-resolution digital elevation models (DEMs), as a way of defining surface water and shadow effects. Flood depth and volume can be estimated by combining RADAR imagery with DEMs to determine the elevation of the land-water interface (Puech and Raclot, 2002;Matgen et al, 2007).…”

Section: Satellite Datamentioning

confidence: 99%

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

O’Hara

Green

McCarthy

2019

Irish Journal of Agricultural and Food Research

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The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales.

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“…HAND has been used for assisting remote sensing inundation mapping as a post-processing mask that eliminates areas of low inundation risk which was shown to be useful for reduction of commission errors [7,29]. HAND has also been used to assist the single band thresholding but further work is needed utilizing more advanced detection algorithms [30].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

2020

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Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset Height Above Nearest Drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features dual-polarized SAR only and dual-polarized SAR along with HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+36.0%), critical success index (CSI) (+39.95%), true positive rate (TPR) (+42.02%), and negative predictive value (NPV) (+17.26%). A marginal change of +0.15% was seen for positive predictive value (PPV), but true negative rate (TNR) fell −14.4%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

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A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Cited by 41 publications

References 43 publications

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

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