A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001–2015

Abstract. Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modelling, we have produced an integrated daily lake surface water temperature for 160 lakes across the Tibetan Plateau where in-situ observation is limited. The satellite-based lake-wide mean LSWT in the integrated dataset includes that for the daytime, night-time and for the daily mean for the period 2000–2017. The dataset is comparable with other satellite-based LSWT products (e.g., LSWT from AVHRR and ARC-Lake) and unique for its tempo-spatial span and resolution. Calibrated and validated against the satellite-based LSWT, complete and consistent daily LSWT dataset have been reconstructed and extended to the period 1978–2017 basing on the modified air2water model. According to the reconstructed LSWT dataset, it is found that annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with increase rate ranging at 0.01 to 0.4 °C 10 a−1. The warming trends of the lakes are more evident in winter than in summer. The integrated dataset together with the methods introduced herein can contribute to the research community to explore water and heat balance changes and the consequent ecological effects at the Tibetan Plateau in the future researches. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.5111400 (Guo et al., 2021).

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Supplementary material to "An integrated dataset of daily lake surface water temperature over Tibetan Plateau"

10.5194/essd-2021-151-supplement ◽

2021 ◽

Author(s):

Linan Guo ◽

Hongxing Zheng ◽

Yanhong Wu ◽

Lanxin Fan ◽

Mengxuan Wen ◽

...

Keyword(s):

Surface Water ◽

Tibetan Plateau ◽

Water Temperature ◽

Surface Water Temperature ◽

Lake Surface ◽

Supplementary Material

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Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes

Remote Sensing ◽

10.3390/rs10070990 ◽

2018 ◽

Vol 10 (7) ◽

pp. 990 ◽

Cited By ~ 9

Author(s):

Gian Lieberherr ◽

Stefan Wunderle

Keyword(s):

Surface Water ◽

Water Temperature ◽

Sensor Data ◽

Surface Water Temperature ◽

Lake Surface

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Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil

Water ◽

10.3390/w11010168 ◽

2019 ◽

Vol 11 (1) ◽

pp. 168 ◽

Cited By ~ 5

Author(s):

Matheus Tavares ◽

Augusto Cunha ◽

David Motta-Marques ◽

Anderson Ruhoff ◽

J. Cavalcanti ◽

...

Keyword(s):

Surface Water ◽

Water Temperature ◽

Single Channel ◽

Atmospheric Correction ◽

Ecological Status ◽

Surface Water Temperature ◽

Lake Surface ◽

Subtropical Lake ◽

Landsat 7 ◽

The Impact

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.

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