Preprints
https://doi.org/10.5194/essd-2024-16
https://doi.org/10.5194/essd-2024-16
26 Feb 2024
 | 26 Feb 2024
Status: this preprint is currently under review for the journal ESSD.

Generation of global 1 km all-weather instantaneous and daily mean land surface temperature from MODIS data

Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang

Abstract. Land surface temperature (LST) serves as a crucial variable in characterizing climatological, agricultural, ecological, and hydrological processes. Thermal infrared (TIR) remote sensing provides high temporal and spatial resolution for obtaining LST information. Nevertheless, TIR-based satellite-LST products frequently exhibit missing values due to cloud interference. Prior research on estimating all-weather instantaneous LST has predominantly concentrated on regional or continental scales. This study involved generating a global all-weather instantaneous and daily mean LST product spanning from 2000 to 2020 using XGBOOST. Multisource data, including Moderate-Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) observations, surface radiation products, and reanalysis data, were employed. Validation using an independent dataset of 77 individual stations demonstrated the high accuracy of our products, yielding RMSEs of 2.787 K (instantaneous) and 2.175 K (daily). The RMSE for clear-sky conditions was 2.614 K for the instantaneous product, slightly lower than the cloudy-sky RMSE of 2.931 K. Our instantaneous and daily mean LST products exhibit higher accuracy compared to the MODIS official LST product (RMSE=3.583 K instantaneous, 3.105 K daily) and the land component of the 5th generation of European ReAnalysis (ERA5-Land) LST product (RMSE= 4.048 K instantaneous, 2.988 K daily). Significant improvements are observed in our LST product, notably at high latitudes, compared to the official MODIS LST product. The LST dataset from 2000 to 2020 at the monthly scale, the daily mean LST on the first day of 2010 can be freely downloaded from https://doi.org/10.5281/zenodo.4292068 (Li et al. 2024), and the complete product will be available at https://glass-product.bnu.edu.cn/dload.html.

Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang

Status: open (until 15 May 2024)

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  • RC1: 'Comment on essd-2024-16', Anonymous Referee #1, 20 Mar 2024 reply
Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang

Data sets

All-weather 1km land surface temperature at global scale from 2000-2020 from MODIS data Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang https://doi.org/10.5281/zenodo.4292067

Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang

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Short summary
This study describes 1-km all-weather instantaneous, and daily mean LST datasets for global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long time-series, global scale. The generated datasets were evaluated by the observations in-situ stations and other LST datasets, and the evaluation indicated the dataset is sufficiently reliable.
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