Preprints
https://doi.org/10.5194/essd-2021-428
https://doi.org/10.5194/essd-2021-428

  10 Jan 2022

10 Jan 2022

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

A 1-km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019

Peilin Song1,4, Yongqiang Zhang1, Jianping Guo2, Jiancheng Shi3, Tianjie Zhao4, and Bing Tong2 Peilin Song et al.
  • 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, The Chinese Academy of Sciences, Beijing 100101, China
  • 2State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 3National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
  • 4State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences. Beijing 100101, China

Abstract. Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellite, while the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003–2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, so that the “all-weather” quality was achieved for the 1-km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April–September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations, through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product is well compared against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased RMSD ranging from 0.052 vol/vol to 0.059 vol/vol. Moreover, the evaluation results also show that the developed product outperforms the SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used for hydrological community, agricultural industry, water resource and environment management.

Peilin Song et al.

Status: open (until 07 Mar 2022)

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Peilin Song et al.

Data sets

Daily all weather surface soil moisture data set with 1 km resolution in China (2003-2019) Peilin Song, Yongqiang Zhang https://doi.org/10.11888/Hydro.tpdc.271762

Peilin Song et al.

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Short summary
Soil moisture information is cruicial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatio-temporal resolutions or in esnuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has great potential to promote investigation on earth hydrology and to be extended to the global scale.