Work

Enhancing surface water mapping and monthly dynamics monitoring with a stepwise gap-filling method

Year: 2024

Type: article

Abstract: Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.

Source: International Journal of Digital Earth

DOI: 10.1080/17538947.2024.2413882

Authors: Zhongxiang Liu, Dejun Zhu, Lei Wang, Danxun Li

Institutions: Tsinghua University, Chinese Academy of Sciences, Institute of Tibetan Plateau Research, University of Chinese Academy of Sciences

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,390