the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Generation of global 1 km all-weather instantaneous and daily mean land surface temperature from MODIS data
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.
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Status: open (until 15 May 2024)
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RC1: 'Comment on essd-2024-16', Anonymous Referee #1, 20 Mar 2024
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The manuscript entitled “Generation of global 1 km all-weather instantaneous and daily mean land surface temperature from MODIS data” has been reviewed. The authors generated a global all-weather instantaneous and daily mean LST product spanning from 2000 to 2020 using XGBOOST, and then they systematically evaluated the accuracy of the produced LST dataset. The manuscript is written well and can be easily understood. I think the manuscript can be accepted if the following concerns have been answered well.
- Several most recent studies corresponding to all-weather LST datasets have been published on the ESSD, but they are not cited in this manuscript. Although the current study focuses on a global scale. Some regional/national-scale studies on all-weather LST datasets also have important contributions and need to be mentioned in this manuscript.
- I am concerned about the effects of station density on the model accuracy. Please include some analysis on this concern.
- I am especially concerned about the spatial pattern of the estimated LST data. The cloudy-sky LST may be mainly decided by the ERA5 data (coarse spatial resolution), which may lose some spatial details. Figs. 13 and 14 have shown the spatial pattern of LSTs. However, the terrains on the two selected tiles may be not representative. 1) Does the estimated LST can show spatial details in mountainous regions? 2) Meanwhile, can the urban heat island effects be shown clearly? 3) Are the estimated LST data spatially and naturally smoothing without any abnormal boundaries?
- I would like to check more daily mean LST data, instead of the daily mean LST on the first day of a year, nor the monthly-scale LSTs. The shared LSTs are not representative of quality checking by reviewers.
- Why not include LST data in 2021 and 2022?
Citation: https://doi.org/10.5194/essd-2024-16-RC1
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
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