Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

Hui-Lin Wang; Ru An; Jia-jun You; Ying Wang; Yuehong Chen; Xiao-ji Shen; Wei Gao; Yi-nan Wang; Yu Zhang; Zhe Wang; Jonathan Arthur Quaye-Ballard

doi:10.1117/1.JRS.11.045003

6 October 2017 Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

Hui-Lin Wang, Ru An, Jia-jun You, Ying Wang, Yuehong Chen, Xiao-ji Shen, Wei Gao, Yi-nan Wang, Yu Zhang, Zhe Wang, Jonathan Arthur Quaye-Ballard

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 045003 (October 2017). https://doi.org/10.1117/1.JRS.11.045003

Abstract

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

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Hui-Lin Wang, Ru An, Jia-jun You, Ying Wang, Yuehong Chen, Xiao-ji Shen, Wei Gao, Yi-nan Wang, Yu Zhang, Zhe Wang, and Jonathan Arthur Quaye-Ballard "Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China," Journal of Applied Remote Sensing 11(4), 045003 (6 October 2017). https://doi.org/10.1117/1.JRS.11.045003

Received: 23 October 2016; Accepted: 6 September 2017; Published: 6 October 2017

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KEYWORDS

Soil science

Climatology

Data modeling

Lawrencium

Performance modeling

Microwave radiation

Spatial resolution

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