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Remote Sens. 2015, 7(9), 11372-11388; doi:10.3390/rs70911372

Upscaling In Situ Soil Moisture Observations to Pixel Averages with Spatio-Temporal Geostatistics

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3
Soil Geography and Landscape Group, Wageningen University, Wageningen 6708 PB, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Xin Li, Yuei-An Liou, Qinhuo Liu, Nicolas Baghdadi, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 1 April 2015 / Revised: 31 August 2015 / Accepted: 1 September 2015 / Published: 7 September 2015
View Full-Text   |   Download PDF [1555 KB, uploaded 7 September 2015]   |  

Abstract

Validation of satellite-based soil moisture products is necessary to provide users with an assessment of their accuracy and reliability and to ensure quality of information. A key step in the validation process is to upscale point-scale, ground-based soil moisture observations to satellite-scale pixel averages. When soil moisture shows high spatial heterogeneity within pixels, a strategy which captures the spatial characteristics is essential for the upscaling process. In addition, temporal variation in soil moisture must be taken into account when measurement times of ground-based and satellite-based observations are not the same. We applied spatio-temporal regression block kriging (STRBK) to upscale in situ soil moisture observations collected as time series at multiple locations to pixel averages. STRBK incorporates auxiliary information such as maps of vegetation and land surface temperature to improve predictions and exploits the spatio-temporal correlation structure of the point-scale soil moisture observations. In addition, STRBK also quantifies the uncertainty associated with the upscaled soil moisture which allows bias detection and significance testing of satellite-based soil moisture products. The approach is illustrated with a real-world application for upscaling in situ soil moisture observations for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe Water Allied Telemetry Experimental Research experiment (HiWATER). The results show that STRBK yields upscaled soil moisture predictions that are sufficiently accurate for validation purposes. Comparison of the upscaled predictions with PLMR soil moisture observations shows that the root-mean-squared error of the PLMR soil moisture product is about 0.03 m3·m−3 and can be used as a high-resolution soil moisture product for watershed-scale soil moisture monitoring. View Full-Text
Keywords: remote sensing product validation; spatio-temporal variogram; upscaling; regional; HiWATER remote sensing product validation; spatio-temporal variogram; upscaling; regional; HiWATER
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, J.; Ge, Y.; Heuvelink, G.B.M.; Zhou, C. Upscaling In Situ Soil Moisture Observations to Pixel Averages with Spatio-Temporal Geostatistics. Remote Sens. 2015, 7, 11372-11388.

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