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Open AccessArticle

Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression

1
Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
2
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 665; https://doi.org/10.3390/rs12040665
Received: 28 January 2020 / Revised: 10 February 2020 / Accepted: 11 February 2020 / Published: 18 February 2020
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., European Space Agency Climate Change Initiative (ESA CCI), National Aeronautics and Space Administration Soil Moisture Active Passive (NASA SMAP)); however, there are regions where satellite-derived soil moisture cannot be estimated because of certain conditions such as high canopy density, frozen soil, or extremely dry soil. We compared and tested three approaches, ordinary kriging (OK), regression kriging (RK), and generalized linear models (GLMs), to model soil moisture and fill spatial data gaps from the ESA CCI product version 4.5 from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation (r = 0.558, RMSE = 0.069 m3m−3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation with NASMD data when using 100% (r = 0.579, RMSE = 0.067 m3m−3), 75% (r = 0.575, RMSE = 0.067 m3m−3), and 50% (r = 0.569, RMSE = 0.067 m3m−3) of available valid pixels for each month of the study period. RK showed comparable values to OK when using different percentages of available valid pixels, 100% (r = 0.582, RMSE = 0.067 m3m−3), 75% (r = 0.582, RMSE = 0.067 m3m−3), and 50% (r = 0.571, RMSE = 0.067 m3m−3). GLM had slightly lower correlation with NASMD data (average r = 0.475, RMSE = 0.070 m3m−3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for using geostatistical approaches (OK and RK) as alternative techniques to gap-fill missing spatial values of satellite-derived soil moisture. View Full-Text
Keywords: soil moisture; remote sensing; geostatistics; gap-filling; mesonet soil moisture; remote sensing; geostatistics; gap-filling; mesonet
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MDPI and ACS Style

Llamas, R.M.; Guevara, M.; Rorabaugh, D.; Taufer, M.; Vargas, R. Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression. Remote Sens. 2020, 12, 665. https://doi.org/10.3390/rs12040665

AMA Style

Llamas RM, Guevara M, Rorabaugh D, Taufer M, Vargas R. Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression. Remote Sensing. 2020; 12(4):665. https://doi.org/10.3390/rs12040665

Chicago/Turabian Style

Llamas, Ricardo M.; Guevara, Mario; Rorabaugh, Danny; Taufer, Michela; Vargas, Rodrigo. 2020. "Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression" Remote Sens. 12, no. 4: 665. https://doi.org/10.3390/rs12040665

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