Validating Data Interpolation Empirical Orthogonal Functions Interpolated Soil Moisture Data in the Contiguous United States

Haipeng Zhao (), Haoteng Zhao () and Chen Zhang
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Haipeng Zhao: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
Haoteng Zhao: Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD 20705, USA
Chen Zhang: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA

Agriculture, 2025, vol. 15, issue 11, 1-15

Abstract: Accurate and spatially detailed soil moisture (SM) data are essential for hydrological research, precision agriculture, and ecosystem monitoring. The NASA’s Soil Moisture Active Passive (SMAP) product offers unprecedented information on global soil moisture. To provide more detailed information about the cropland SM data for the Contiguous United States (CONUS), a 1-km SMAP product has been produced using the THySM model in support of USDA NASS operations. However, the current 1-km product contains substantial data gaps, which poses challenges for applications that require continuous daily data. Data Interpolation Empirical Orthogonal Functions (DINEOF+) is an interpolation technique that uses singular value decomposition (SVD) to address missing data problems. Previous studies have applied DINEOF+ to reconstruct the 1-km daily SM dataset but without further analysis of the reconstruction errors. In this study, we perform a comprehensive validation of DINEOF+ reconstructed SM by using both the original SMAP data and in situ measurements across the CONUS. Our results show that the reconstructed SM closely aligns with the original SM with R 2 > 0.65 and bias ranging from 0.01 to 0.02 m 3 /m 3 . When compared to in situ SM, the mean absolute error (MAE) ranges between 0.01 and 0.04 m 3 /m 3 and the time series correlation coefficient ranges from 0.6 to 0.8. Our findings suggest that DINEOF+ effectively recovers missing data and improves the temporal resolution of SM time series. However, we also note that the accuracy of the reconstructed SM is dependent on the quality of the original SMAP data, emphasizing the need for continued improvements in SM retrievals by satellite.

Keywords: soil moisture; data interpolation; SMAP; DINEOF+ (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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