NASA Technical Reports Server (NTRS) 20180000651: Multi-Timescale Analysis Of The Spatial Representativeness Of In Situ Soil Moisture Data Within Satellite Footprints - Info and Reading Options
By NASA Technical Reports Server (NTRS)
"NASA Technical Reports Server (NTRS) 20180000651: Multi-Timescale Analysis Of The Spatial Representativeness Of In Situ Soil Moisture Data Within Satellite Footprints" and the language of the book is English.
“NASA Technical Reports Server (NTRS) 20180000651: Multi-Timescale Analysis Of The Spatial Representativeness Of In Situ Soil Moisture Data Within Satellite Footprints” Metadata:
- Title: ➤ NASA Technical Reports Server (NTRS) 20180000651: Multi-Timescale Analysis Of The Spatial Representativeness Of In Situ Soil Moisture Data Within Satellite Footprints
- Author: ➤ NASA Technical Reports Server (NTRS)
- Language: English
Edition Identifiers:
- Internet Archive ID: NASA_NTRS_Archive_20180000651
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"NASA Technical Reports Server (NTRS) 20180000651: Multi-Timescale Analysis Of The Spatial Representativeness Of In Situ Soil Moisture Data Within Satellite Footprints" Description:
The Internet Archive:
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 (approx. 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.
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