Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China

Feng，Lian1; Pi，Xuehui1,2; Luo，Qiuqi1; Li，Weifeng2,3,4

2023-11-01

10.1016/j.rse.2023.113775

Remote Sensing of Environment   Impact Factor & Quartile

0034-4257

1879-0704

Temporal monitoring of inland water bodies using remote sensing images is often impeded by missing data caused by clouds and other adverse conditions. To date, various data recovery algorithms have been developed based on the water occurrence threshold (WOT), where the contaminated pixels are recovered by using long-term historical water distribution information. Here, we propose an improved algorithm, enhanced WOT (EWOT), which addresses the issue of mismatch between the water occurrence product and the actual historical water presence that has been neglected by previous WOT algorithms. The EWOT algorithm achieved an overall high accuracy (with a mean absolute percentage error (MAPE) = 5.1%) and prevailed against a representative WOT algorithm. The accuracy could be further reduced (MAPE = 1.6%) after the application of a novel quality control process. In addition, the temporal coverage of the high-quality surface water area time series was improved by an average of 26.2%, and the percent count and percent area of lakes with high-quality reconstructed data reached as high as 84.5% and 94.7%, respectively, facilitating the utilization of these data in further time series analysis. In general, the improvement was closely associated with the extent of the contamination before recovery. We evaluated the algorithm's ability to be implemented on a large scale in China, and the results generally were in line with previous studies. Nonetheless, our high-quality annual-based dataset presented a more comprehensive and continuous representation of the changes in lake area spanning from 2000 to 2019. The significance of improving the existing WOT algorithms is highlighted in this study, and the proposed method can be readily extended to lakes worldwide, thus providing a valuable data source to examine the causes and possible impacts of lake dynamics.

China Image contamination Inland water Surface water area changes Water classification recovery Water occurrence

SCI

English

First ; Corresponding

National Natural Science Foundation of China[41971304];National Natural Science Foundation of China[42271322];

Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:001090554700001

ELSEVIER SCIENCE INC

GEOSCIENCES

2-s2.0-85169571285

Scopus

1.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.Department of Urban Planning and Design,The University of Hong Kong,Hong Kong SAR,China
3.Urban Systems Institute,The University of Hong Kong,Hong Kong SAR,China
4.Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities,Hong Kong SAR,China

Feng，Lian,Pi，Xuehui,Luo，Qiuqi,et al. Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China[J]. Remote Sensing of Environment,2023,297.

Feng，Lian,Pi，Xuehui,Luo，Qiuqi,&Li，Weifeng.(2023).Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China.Remote Sensing of Environment,297.

Feng，Lian,et al."Reconstruction of long-term high-resolution lake variability: Algorithm improvement and applications in China".Remote Sensing of Environment 297(2023).