Abstract
The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies.
Highlights
A positive impact on hydrology as well as on modern agricultural practices can be achieved by monitoring spatial and temporal dynamics of some land surface variables such as soil water content, mv, and vegetation
No significant correlations were found between backscattering and soil roughness, whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering
This novel approach allowed a widening in the number of significant correlations and their strengths by encompassing the use of synthetic aperture radar (SAR) data acquired at two different frequencies
Summary
A positive impact on hydrology as well as on modern agricultural practices can be achieved by monitoring spatial and temporal dynamics of some land surface variables such as soil water content, mv , and vegetation. Despite the fact that synthetic aperture radar (SAR) allows acquiring in all weather conditions, unassessed strengthenings of the relations among backscattering, σ◦ , at different frequencies and polarizations and soil-vegetation variables (SVV) are limiting the operational application of SAR. SAR configuration controls the wave-target interactions [1,2] and, affects models’ assessments depending on its frequency, polarization and acquisition geometry. The dependence of bare soils σ◦ , σ◦ S , on both water content and surface roughness was modeled by several authors [1,2,3,4,5]. Reliable roughness measures are hardly achievable: (i) in situ measurements with contact methods (e.g., grid board [9]) are point based and usually not representative at plot scale; (ii) innovative methodologies (e.g., non-contact ultrasonic profiling) are capable of describing wide areas but still economically expensive (i.e., requiring airborne acquisitions)
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