Back to Search Start Over

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data.

Authors :
Xu, Nan
Ma, Yue
Zhang, Wenhao
Wang, Xiao Hua
Yang, Fanlin
Su, Dianpeng
Source :
Remote Sensing. Dec2020, Vol. 12 Issue 23, p4004. 1p.
Publication Year :
2020

Abstract

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth's surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
23
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
147501997
Full Text :
https://doi.org/10.3390/rs12234004