Preprints
https://doi.org/10.5194/essd-2021-31
https://doi.org/10.5194/essd-2021-31

  12 Mar 2021

12 Mar 2021

Review status: this preprint is currently under review for the journal ESSD.

An all-sky 1 km daily surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data

Yan Chen1, Shunlin Liang2, Han Ma1, Bing Li1, Tao He1, and Qian Wang3,4 Yan Chen et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
  • 3State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
  • 4Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100875, China

Abstract. Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, while reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions, or with limited temporal and spatial coverage. In this study, an all-sky daily mean Ta product at 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R2 and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. The relative contributions of different features to models were also quantitatively analysed. Finally, values of our Ta product in 2010 were validated and compared with the China Land Data Assimilation System (CLDAS) dataset at 0.0625° spatial resolution, China Meteorological Forcing Data (CMFD) dataset at 0.1° spatial resolution and GLDAS dataset at 0.25° spatial resolution. The R2 and RMSE values of our product were 0.992 and 1.010 K, respectively, indicating this high-resolution satellite product has significantly higher accuracy. In summary, the all-sky daily mean Ta dataset developed in this study has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and hydrological cycle. This dataset is freely available at http://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta_China/) currently. A sub-dataset that covers Beijing generated from this dataset is publicly available at http://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a).

Yan Chen et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2021-31', Joshua Fisher, 12 Mar 2021
    • AC1: 'Reply on CC1', Yan Chen, 24 Mar 2021
  • RC1: 'Comment on essd-2021-31', Anonymous Referee #1, 02 Apr 2021
    • AC2: 'Reply on RC1', Yan Chen, 26 Apr 2021
      • RC2: 'Reply on AC2', Anonymous Referee #1, 26 Apr 2021
  • RC3: 'Comment on essd-2021-31', Anonymous Referee #2, 15 Jun 2021

Yan Chen et al.

Data sets

An All-sky 1 km Daily Surface Air Temperature Product over Mainland China Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang https://doi.org/10.5281/zenodo.4399453

An All-sky 0.01° Daily Surface Air Temperature Product over Beijing (2003-2019) Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang https://doi.org/10.5281/zenodo.4405123

Yan Chen et al.

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Short summary
This study used remotely sensed data and assimilation data to estimate all-sky surface air temperature (Ta) using machine learning method, and developed an all-sky 1 km daily mean Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and hydrological cycle.