Multi‐Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints. Issue 1 (4th January 2018)

• Record Type: Journal Article
• Authors: Molero, B.; Leroux, D. J.; Richaume, P.; Kerr, Y. H.; Merlin, O.; Cosh, M. H.; Bindlish, R.
• Is Part Of: Journal of geophysical research. Volume 123:Issue 1(2018)
• Page Start: 3
• Page End: 21
• Publication Date: 2018-01-04
• Subjects: soil moisture -- spatial representativeness -- timescales -- spatial scales -- wavelet decomposition -- satellite validation; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027478
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: We conduct a novel comprehensive investigation that seeks to prove the connection between spatial scales and timescales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at timescales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per‐timescale basis by comparison to large spatial scale data sets (the in situ spatial average, SMOS, AMSR2, and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), percentage of correlated areas (CArea), and a new proposed approach that uses wavelet‐based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the timescale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero‐crossings, and TC is suitable for week and month scales but not for other scales where data set cross‐correlations are found low. In contrast, WCor and CArea give consistent results at all timescales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (one station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks. Key Points: The spatial representativeness of in situ stations tends to increase with the timescale within the satellite footprint Stations poorly represent the satellite footprint at subweekly scales, while either very well or poorly at seasonal scales The wavelet correlation (WCor) method is a useful tool to study the spatial scale mismatch between in situ and satellite observations