Multi-scale statistical properties of disaggregated SMOS soil moisture products in Australia. (December 2019)

• Record Type: Journal Article
• Authors: Neuhauser, M.; Verrier, S.; Merlin, O.; Molero, B.; Suere, C.; Mangiarotti, S.
• Is Part Of: Advances in water resources. Volume 134(2019)
• Publication Date: 2019-12
• Subjects: Soil moisture -- Multi-scale analysis -- Multifractals -- Disaggregation -- SMOS -- DisPATCh; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2019.103426
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: Fractal and multifractal properties were observed on remotely sensed soil moisture products acquired from SMOS satellite (Soil Moisture and Ocean Salinity), over space scales ranging from the kilometric field scale to the continental scale. Two scaling regimes were noticed for the soil moisture data disaggregated with DisPATCh algorithm (Disaggregation based on Physical And Theoretical scale Change), with a scaling break observed at about ten kilometers. Fractality and multifractality were also found on remotely sensed vegetation indices and surface temperature. Abstract: Soil moisture has a strong impact on climate, hydrology and agronomy at different space scales, from the continent global scale to the local watershed. Passive microwave sensors, like SMOS satellite (Soil Moisture and Ocean Salinity), allow a global study of soil moisture on the entire globe. To have access to kilometric variability, disaggregation algorithms have been developed, such as the Disaggregation based on Physical And Theoretical scale Change (DisPATCh). This method improves the space resolution of SMOS soil moisture from 40 km to 1 km. To do this, it combines coarse-scale (≈40 km) SMOS products with fine-scale (≈1 km) optical/thermal data. Validation studies on specific scales showed the potential of DisPATCh to enhance the spatio-temporal correlation of disaggregated SM with in-situ measurements, under low-vegetated semi-arid regions. Although the efficiency of the method was revealed in these regions, no studies fully explored its statistical behavior over a continuum of space scales. In this paper, we studied and compared the spatial multi-scale statistics of the different input and output datasets involved in DisPATCh downscaling. To do this, we applied spectral and multifractal analysis on the respective products for the region of southeastern Australia, from June to December 2010. Fractal and multifractal properties (in the framework of the Universal Multifractal model) were observed on inputs of DisPATCh (SMOS soil moisture, MODIS vegetation indices and surface temperature), which confirmed and completed some results reported in existing literature. For the output disaggregated soil moisture, two scaling regimes were observed, with a transition scale observed at about ten kilometers. Considering spectral analysis, at large scales (> 10 km), disaggregated soil moisture was found to have the same scaling as the original SMOS soil moisture. On finer scales (< 10 km), a different behavior was noticed, with a higher value of the slope of the power spectrum. The same scale break was detected on statistical moments, showing that both spectral and multifractal properties of DisPATCh soil moisture are characterized by this twofold scaling signature.