Landsat observations reveal increasing trend in lake clarity on the Qinghai-Tibetan Plateau: Insights into the lake optical response to climate change

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. • A new framework harmonizing Landsat sensors for consistent long-term Z SD estimation. • Employed MODIS to cross-calibrate Z SD derived from Landsat 5/TM and Landsat 8/OLI. • 53.0% of the TP lakes showed significant increases in Z SD during 1986–2024. • Revealed three response patterns of lake clarity to climate change on TP.

Authors

Institutions

Publication Details

Journal
Remote Sensing of Environment
Published
2026-04-16
DOI
https://doi.org/10.1016/j.rse.2026.115424
Primary Topic
Marine and coastal ecosystems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Landsat observations reveal increasing trend in lake clarity on the Qinghai-Tibetan Plateau: Insights into the lake optical response to climate change

Evangelos Spyrakos, Deepakrishna Somasundaram, Shenglei Wang, Zijun Wu et al.
Remote Sensing of Environment
Marine and coastal ecosystems
article

Landsat observations reveal increasing trend in lake clarity on the Qinghai-Tibetan Plateau: Insights into the lake optical response to climate change

Evangelos Spyrakos, Deepakrishna Somasundaram, Shenglei Wang, Zijun Wu, Junsheng Li, Kun Shi, Fangfang Zhang, Wenzhi Zhang, Bing Zhang, Zhangru Tan
article en

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. • A new framework harmonizing Landsat sensors for consistent long-term Z SD estimation. • Employed MODIS to cross-calibrate Z SD derived from Landsat 5/TM and Landsat 8/OLI. • 53.0% of the TP lakes showed significant increases in Z SD during 1986–2024. • Revealed three response patterns of lake clarity to climate change on TP.

Remote Sensing of EnvironmentVol. 340
University of Stirling (GB), Chinese Academy of Sciences (CN), Ministry of Ecology and Environment (CN), Beijing Institute of Big Data Research (CN), Nanjing Institute of Geography and Limnology (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences
Climate action
Openalex Percentile: Top 6%
Marine and coastal ecosystems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.