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

Daniel Druce,Cecile M.M Kittel,Christian Tottrup,Xiaoye Tong,Tao Guo,Kenneth Grogan,Xia Lei

doi:10.3390/rs13091663

Daniel Druce, Cecile M.M Kittel + Show 5 more

Open Access

https://doi.org/10.3390/rs13091663

Copy DOI

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.

Highlights

The dedicated goal of water in the 2030 agenda for sustainable development has put the spotlight on water policy at the global level and in national planning
When looking at the multi-annual water occurrence map, it is clear that surface water resources are unevenly distributed in mainland China (Figure 3)
Despite the convincing visual assessment, our algorithm showed reduced performance in the seasonal strata compared to the land or water strata

Summary

Introduction

The dedicated goal of water in the 2030 agenda for sustainable development has put the spotlight on water policy at the global level and in national planning. Population growth, socioeconomic development and urbanization are all contributing to increased water demand, while climate change induced impacts on precipitation patterns and temperature extremes further exacerbate water resource availability and predictability [2]. Surface water mapping is an established application in remote sensing, yet by reviewing the extensive literature on the subject, the approaches vary according to the objectives and scale of the study, sensor used, and environmental settings. The thresholding approach tends to be less reproducible, especially over large areas, as it often requires the intervention of experts to determine the optimal threshold. Water indices and thresholding are impacted by factors such as water turbidity, shadows from terrain, buildings, and clouds, as well as the presence of snow and ice. For instance, the AWEI comes with optimized coefficients for water extraction in situations with shadows and/or other dark surfaces. Topographic issues, caused by cast shadows from the terrain, can be suppressed by developing masks from Digital Elevation models, e.g., terrain shadow masking [35] or using the Height Above Nearest Drainage (HAND) index [36], to eliminate commission errors located in areas where water is not expected to accumulate [7,37]

Objectives

Results

Discussion

Conclusion