4.7 Article

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

Journal

EARTH SYSTEM SCIENCE DATA
Volume 14, Issue 6, Pages 2613-2637

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/essd-14-2613-2022

Keywords

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Funding

  1. National Natural Science Foundation of China [42001304]
  2. Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS202117]
  3. CAS Pioneer Talents Program
  4. CAS-CSIRO International Cooperation Program
  5. International Partnership Program of Chinese Academy of Sciences [183311KYSB20200015]

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Surface soil moisture is crucial for understanding the hydrological process of our earth surface. In this study, a high-resolution soil moisture product with all-weather coverage was developed in China using the passive microwave technique. The product showed good performance compared to in situ soil moisture measurements and outperformed other satellite-based products. It has great potential for applications in hydrology, agriculture, and water resource management.
Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. The passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellites, while the coarse resolution (usually >similar to 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 the 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 (Advance Microwave Scanning Radiometer of the Earth Observing System and its successor) SSM at 36 km, covering all on-orbit times of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermalinfrared 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 compares well against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased root mean square difference (RMSD) ranging from 0.052 to 0.059 vol vol(-1). Moreover, the evaluation results also show that the developed product outperforms the SMAP (Soil Moisture Active Passive) and 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 by the hydrological community, by the agricultural industry, and for water resource and environment management. The new product is available for download at https://doi.org/10.11888/Hydro.tpdc.271762 (Song and Zhang, 2021b).

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