Landsat observations reveal increasing trend in lake clarity on the Qinghai-Tibetan Plateau: Insights into the lake optical response to climate change
Year: 2026
Type: article
Abstract: Lakes across the Qinghai-Tibetan Plateau (TP) have undergone substantial environmental changes in recent decades, under the influence of climate warming and enhanced precipitation. But the long-term changes in lake optical properties and their responses to climate change on the TP remain less explored. This study utilizes Landsat series datasets from 1986 to 2024 to estimate a fundamental lake optical property—the Secchi disk depth ( Z SD ) in TP lakes, for tracking the long-term shifts in TP lake ecosystems. To accommodate the limited spectral information of Landsat 5/TM imagery, an improved semi-analytical QAARGB model was proposed to enhance the applicability of the semi-analytical model on lakes with relatively low Z SD (<1 m). To maintain the consistency of Z SD retrieval across Landsat series datasets, MODIS data were also employed for cross-calibration. The results unveil a noteworthy upward trend of Z SD across TP lakes over the nearly 40 years (with average Sen's slope of 0.019 m/yr, P < 0.05), with more pronounced improvements observed in the western and central TP regions. Analysis of long-term lake Z SD and meteorological factors revealed three distinct response patterns of lake Z SD to climate change on the TP. These patterns were predominantly associated with NDVI (33.0% of lakes), lake area (31.0%), and air temperature (4.9%), unveiling clear spatial heterogeneity in the dominant meteorological factors associated with interannual Z SD variations. These findings highlight the cascading impacts of climate change on TP lakes—mediated by hydrological expansion, watershed vegetation, and thermal regime—ultimately reshaping their optical and ecological characteristics.
Source: Remote Sensing of Environment
Institutions: Chinese Academy of Sciences, Aerospace Information Research Institute, International Research Center of Big Data for Sustainable Development Goals, University of Chinese Academy of Sciences, Ministry of Ecology and Environment, University of Stirling, Nanjing Institute of Geography and Limnology, State Key Laboratory of Lake Science and Environment
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,450
Awards: G1917208702, G7284161631, G8916927129
OpenAlex ID: W7154577530
