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

• Main Authors: Y. Chen, S. Liang, H. Ma, B. Li, T. He, Q. Wang
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-08-01
• Series: Earth System Science Data
• Online Access: https://essd.copernicus.org/articles/13/4241/2021/essd-13-4241-2021.pdf

<p>Surface air temperature (<span class="inline-formula"><i>T</i>_a</span>), 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, and reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated <span class="inline-formula"><i>T</i>_a</span> under clear-sky conditions or with limited temporal and spatial coverage. In this study, an all-sky daily mean land <span class="inline-formula"><i>T</i>_a</span> product at a 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 <span class="inline-formula"><i>T</i>_a</span> estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The random sample validation results showed that the <span class="inline-formula"><i>R</i>²</span> and root-mean-square error (RMSE) values of the three models ranged from 0.984 to 0.986 and from 1.342 to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. Two cross-validation (CV) strategies of leave-time-out (LTO) CV and leave-location-out (LLO) CV were also used to evaluate the models. Finally, we developed the all-sky <span class="inline-formula"><i>T</i>_a</span> dataset from 2003 to 2009 and compared it with the China Land Data Assimilation System (CLDAS) dataset at a 0.0625<span class="inline-formula">^∘</span> spatial resolution, the China Meteorological Forcing Data (CMFD) dataset at a 0.1<span class="inline-formula">^∘</span> spatial resolution, and the GLDAS dataset at a 0.25<span class="inline-formula">^∘</span> spatial resolution. Validation accuracy of our product in 2010 was significantly better than other datasets, with <span class="inline-formula"><i>R</i>²</span> and RMSE values of 0.992 and 1.010 K, respectively. In summary, the developed all-sky daily mean land <span class="inline-formula"><i>T</i>_a</span> 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 the hydrological cycle. This dataset is currently freely available at <a href="https://doi.org/10.5281/zenodo.4399453">https://doi.org/10.5281/zenodo.4399453</a> (Chen et al., 2021b) and the University of Maryland (<span class="uri">http://glass.umd.edu/Ta_China/</span>, last access: 24 August 2021). A sub-dataset that covers Beijing generated from this dataset is also publicly available at <a href="https://doi.org/10.5281/zenodo.4405123">https://doi.org/10.5281/zenodo.4405123</a> (Chen et al., 2021a).</p>