Location: Hydrology and Remote Sensing LaboratoryTitle: Multi-time scale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints
|MOLERO, B. - University Of Toulouse|
|LEROUX, D. - Universite Grenoble Alpes|
|RICHAUME, P. - University Of Toulouse|
|KERR, Y. - University Of Toulouse|
|MERLIN, O. - University Of Toulouse|
|BINDLISH, R. - Goddard Space Flight Center|
Submitted to: Journal of Geophysical Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2017
Publication Date: 1/23/2018
Citation: Molero, B., Leroux, D., Richaume, P., Kerr, Y., Merlin, O., Cosh, M.H., Bindlish, R. 2018. Multi-time scale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints. Journal of Geophysical Research. 123(1):3-21. https://doi.org/10.1002/2017JD027478.
Interpretive Summary: Soil moisture estimation can be accomplished by several methods, including remote sensing, modeling, and in situ networks. However, these data points are collected on diverse spatial and temporal scales. A variety of analyses are applied to these different soil moisture products to determine how well the product source represents the region of study with respect to time and space. These analyses include temporal stability analysis, triple collocation, percentage of correlated areas, and a new wavelet based correlations analysis. It was determined that this new wavelet analysis is the most promising across the scales of interest for national (sparse) network analysis, which is the most common type of analysis for soil moisture estimation. These results are promising to improve the validation and downscaling of satellite soil moisture data products and for optimizing in situ network development.
Technical Abstract: We conduct a novel comprehensive investigation that seeks to prove the connection between spatial and time scales 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 time scales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per time-scale basis by comparison to large-spatial scale datasets (the in situ spatial average, SMOS, AMSR2 and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), the 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 time scale 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 datasets cross-correlations are found low. In contrast, WCor and CArea give consistent results at all time-scales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (1 station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.