Landsat-derived gap-free monthly 30-m dataset (2000–2021) unraveling intra-annual surface water dynamics on the Tibetan Plateau

The Tibetan Plateau (TP) is highly sensitive to climate change, yet existing Landsat-based surface water products suffer from pervasive data gaps caused by clouds, terrain shadows, and seasonal snow/ice, hindering fine-scale intra-annual hydrological analysis. Here we present a spatially complete, monthly 30-m resolution surface water dataset for the entire TP spanning 2000–2021 (TP MWH), constructed using a stepwise gap-filling (SGF) framework. The SGF method synergistically integrates Joint Research Centre (JRC)'s Yearly Water Classification History, Monthly Water Recurrence, and spatiotemporal neighborhood consistency, forming a four-step hierarchical judgment system that preserves the high precision of the original JRC classification while compensating for its inherent seasonal water omission bias. Validated against 12,141 stratified random samples covering 12 months, 12 hydrological basins, and 8 typical plateau surface features, the TP MWH dataset achieves an overall accuracy of 98.1%, a recall of 97.8%, a precision of 98.4%, an F1-score of 0.981, a Kappa coefficient of 0.961, and maintains stable high performance (>97.4% accuracy, >97.0% recall, >96.5% precision, >0.946 Kappa) throughout the year, outperforming mainstream JRC and GLAD products especially in winter freezing periods. Cross-comparison with the global monthly lake dataset confirms consistency in both interannual trends (Spearman r = 0.998) and intra-annual seasonal variability (Spearman r = 0.949). Leveraging this gap-free dataset, we reveal a three-stage interannual expansion of surface water and intensified intra-annual fluctuations after 2016, indicating growing hydrological instability of the TP. The 12 sub-basins exhibit four distinct heterogeneous evolution regimes, reflecting differentiated responses to monsoon, westerly circulation, and cryospheric meltwater changes. This dataset provides a reliable baseline for seasonal hydrological research and water resource management on the TP, and is publicly available at https://doi.org/10.5281/zenodo.13910635.

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Publication Details

Journal
GIScience & Remote Sensing
Published
2026-07-27
DOI
https://doi.org/10.1080/15481603.2026.2707752
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
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article

Landsat-derived gap-free monthly 30-m dataset (2000–2021) unraveling intra-annual surface water dynamics on the Tibetan Plateau

Zhongxiang Liu, Shihan Shan, Fuling Wen, Danxun Li et al.
GIScience & Remote Sensing
Hydrology and Watershed Management Studies
article

Landsat-derived gap-free monthly 30-m dataset (2000–2021) unraveling intra-annual surface water dynamics on the Tibetan Plateau

Zhongxiang Liu, Shihan Shan, Fuling Wen, Danxun Li, Qiuyu Yao, Dejun Zhu, Lei Wang, Guiliang Zhong
article en

Abstract

The Tibetan Plateau (TP) is highly sensitive to climate change, yet existing Landsat-based surface water products suffer from pervasive data gaps caused by clouds, terrain shadows, and seasonal snow/ice, hindering fine-scale intra-annual hydrological analysis. Here we present a spatially complete, monthly 30-m resolution surface water dataset for the entire TP spanning 2000–2021 (TP MWH), constructed using a stepwise gap-filling (SGF) framework. The SGF method synergistically integrates Joint Research Centre (JRC)'s Yearly Water Classification History, Monthly Water Recurrence, and spatiotemporal neighborhood consistency, forming a four-step hierarchical judgment system that preserves the high precision of the original JRC classification while compensating for its inherent seasonal water omission bias. Validated against 12,141 stratified random samples covering 12 months, 12 hydrological basins, and 8 typical plateau surface features, the TP MWH dataset achieves an overall accuracy of 98.1%, a recall of 97.8%, a precision of 98.4%, an F1-score of 0.981, a Kappa coefficient of 0.961, and maintains stable high performance (>97.4% accuracy, >97.0% recall, >96.5% precision, >0.946 Kappa) throughout the year, outperforming mainstream JRC and GLAD products especially in winter freezing periods. Cross-comparison with the global monthly lake dataset confirms consistency in both interannual trends (Spearman r = 0.998) and intra-annual seasonal variability (Spearman r = 0.949). Leveraging this gap-free dataset, we reveal a three-stage interannual expansion of surface water and intensified intra-annual fluctuations after 2016, indicating growing hydrological instability of the TP. The 12 sub-basins exhibit four distinct heterogeneous evolution regimes, reflecting differentiated responses to monsoon, westerly circulation, and cryospheric meltwater changes. This dataset provides a reliable baseline for seasonal hydrological research and water resource management on the TP, and is publicly available at https://doi.org/10.5281/zenodo.13910635.

GIScience & Remote SensingVol. 63(1)
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (CN), PowerChina (China) (CN), Institute of Tibetan Plateau Research (CN), University of Chinese Academy of Sciences (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Clean water and sanitation
Openalex Percentile: Top 17%
Hydrology and Watershed Management Studies
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