An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

Abstract

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques

“…Sentinel missions as Sentinel-1/-3 can deliver useful data and produce valuable information such as RWSC and release. Our SAR-based classification for mapping surface water areas performs considerably better than Landsat-based maps [46], and this will make the estimation of reservoir area more robust and hence improve the accuracy of RWSC and release estimates. The findings indicate that the accuracy of RWSC estimates is more sensitive to satellite SWE while less sensitive to WSE.…”

“…Both satellites carry a C-band SAR instrument which collects data in all-weather conditions enabling continuous 12 day repeat monitoring. The workflow was adapted from Druce et al [46] and consists of pre-processing, classification, and post-processing steps. In the pre-processing step, precise orbit vectors and range-Doppler terrain correction are applied to obtain a georeferenced SAR image.…”

Surface Water Extent Mapping in Denmark: Comparison of Satellite Earth Observation and Airborne Thermal Imagery

“…The approach for SWE mapping using satellite EO data is documented in [22]. The approach uses a multivariate logistic regression model to estimate surface water probability from a combination of optical imagery from the Sentinel-2 mission and synthetic aperture radar (SAR) imagery from the Sentinel-1 mission.…”

“…The approach uses a multivariate logistic regression model to estimate surface water probability from a combination of optical imagery from the Sentinel-2 mission and synthetic aperture radar (SAR) imagery from the Sentinel-1 mission. In the original approach by Druce et al [22], the input data are processed into monthly composites, but, specifically for Danish conditions, we modified the algorithm to output the results by individual sensor and acquisition date, thereby enabling the surface water extent products to correspond with the time of the flooding / drone activity. Optical data was prioritized where available due to the ability to identify smaller features than Sentinel-1.…”

Evaluating wetland hydrological performance under three different conservation programs in Nebraska, United States, during 2018–2021

“…Satellite data assist in estimating crop water requirements and evaluating water use efficiency for irrigation management. Furthermore, Sentinel images and GEE aid in assessing land use changes within watersheds, contributing to effective watershed management and long‐term groundwater monitoring (Amani et al, 2020; Druce et al, 2021). Moreover, it was an efficient tool for mapping all conserved land sites across Nebraska and detecting changes in surface water.…”