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

Abstract

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.

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

“…Pollard et al [61] discussed Big Data approaches for handling coastal flooding on issues related to synthesis of coastal datasets, data handling and validation, and integration with processbased models in real time. Huang et al [62] reviewed sources and techniques for detecting, extracting, and monitoring surface water extents using optical remote sensing. Remote sensing of surface water bodies can be done using multispectral, hyperspectral, and microwave sensor data (e.g.…”

Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2

“…AWEI significantly improved the surface water detection in such situations, but it actually has two indices: AWEI sh for areas where there are dominant shadow contaminations and AWEI nsh for area where there are not. The second limitation is that MNDWI often misclassified snow as water [34], because the differences of the two used bands values for water and snow are close, even though individual band values vary a lot. So existing water indices could probably be further improved for more universal applications, notwithstanding relatively high overall accuracy of surface water detection that has been achieved in many cases.…”

Discharge Estimates for Ungauged Rivers Flowing over Complex High-Mountainous Regions based Solely on Remote Sensing-Derived Datasets

“…Many studies had clearly proved that remote sensing can be potentially used to monitor river discharge [4,9,11,15,21,30,[40][41][42]. Satellite data have also been used to calibrate hydrological models, thus providing a new insight of discharge estimation over ungauged river basins [43][44][45][46][47][48].The extraction of water surface area (WSA) from optical satellite images, using several water indices calculated from two or more bands, has been widely used in the last few decades to identify water and non-water areas [10,[49][50][51][52][53]. The tasseled cap wetness (TCW) index [54], normalized difference water index (NDWI) [55], modified normalized difference water index (MNDWI) [56] and the automated water extraction index (AWEI) [57] are the most well-known water indices.…”

“…The extraction of water surface area (WSA) from optical satellite images, using several water indices calculated from two or more bands, has been widely used in the last few decades to identify water and non-water areas [10,[49][50][51][52][53]. The tasseled cap wetness (TCW) index [54], normalized difference water index (NDWI) [55], modified normalized difference water index (MNDWI) [56] and the automated water extraction index (AWEI) [57] are the most well-known water indices.…”