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Polarimetric Decompositions for Soil Moisture Retrieval from Vegetated Soils in TERENO Observatories Thomas Jagdhuber1, Irena Hajnsek2,1, Konstantinos P

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Polarimetric Decompositions for Soil Moisture Retrieval from Vegetated Soils in TERENO Observatories Thomas Jagdhuber1, Irena Hajnsek2,1, Konstantinos P Polarimetric Decompositions for Soil Moisture Retrieval from Vegetated Soils in TERENO Observatories Thomas Jagdhuber1, Irena Hajnsek2,1, Konstantinos P. Papathanassiou1 1Microwave and Radar Institute, German Aerospace Center (DLR), PO BOX 1116, 82234 Wessling, Germany, Phone/Fax: +49-8153-28-2329 / -1449 2ETH Zurich, Institute of Environmental Engineering, HIF D28.1, Schafmattstr. 6, CH-8093 Zurich Email: [email protected], [email protected], [email protected] Therefore four terrestrial environmental observatories ABSTRACT across Germany were selected to investigate the shift in environmental conditions caused by climate change A refined hybrid polarimetric decomposition and (Fig. 1). These observatories are equipped with a inversion method for soil moisture estimation under variety of geo-physical instruments to monitor on vegetation is investigated for its potential to retrieve different scales the long-term changes on eco-systems. soil moisture from vegetated soils in TERENO Hence, they form an ideal test bed for analysis of soil observatories. The refined algorithm is applied on L- moisture estimation with remote sensing platforms. In band fully polarimetric data acquired by DLR’s novel order to support flood forecasting or precision farming F-SAR sensor. Two flight and field measurement techniques the spatial assessment of soil moisture is of campaigns were conducted in 2011 and 2012 for the great benefit. Polarimetric SAR remote sensing is TERENO Harz, Eifel and DEMMIN observatories, capable to cover these large areas. Since most of the located all across Germany. The applied algorithm landscape in middle latitudes is at least seasonally reveals distinct potential to invert soil moisture with covered by vegetation, the estimation of soil moisture inversion rates higher than >98% for a variety of crop increases in complexity due to the presence and types, phenological conditions and for pronounced gradual increase of vegetation along the growing topography. A quality assessment is conducted by season. For this paper a developed hybrid polarimetric validation with FDR, TDR and a wireless soil moisture decomposition is refined for an improved soil moisture network. The RMSE stays below 6.1vol.% for the inversion under vegetation cover [3]. different test sites and data takes including a variety of vegetation types in different phenological stages. 2. REFINED HYBRID POLARIMETRIC DECOMPOSITION FOR SOIL MOISTURE 1. INTRODUCTION INVERSION The initiative for Terrestrial Environmental Soil moisture retrieval under vegetation cover involves Observatories (TERENO) of the German Helmholtz soil and vegetation components. Therefore the Association tries to assess the impact of land use recorded polarimetric SAR signature ([TData]) is changes, climate changes, socioeconomic develop- decomposed into three canonical scattering ments and human interventions in terrestrial systems components (surface [TS], dihedral [TD] and volume by an interdisciplinary research approach on the [TV]) using an analytically-solvable, polarimetric regional scale [1]. hybrid decomposition algorithm [3]. In Fig. 2 the scheme for the decomposition and inversion algorithm describes the single steps within the algorithm. Figure 1: Overview of the four TERENO observatories Figure 2: Scheme of hybrid polarimetric decomposition in Germany [2]. and inversion for soil moisture under vegetation cover. [ ]−==+[ ] [ ] [ ] [ ] TTTTTData V G S D αα22+−() αα TT11 120 VV 11 12 0 fs cos s f d sin d ff d s cos ds,, sin ds 0 (1) TT**0−=−+ fVV 0 () ff cosαα sin f cos α22 f sin α 0 12 22vdsdsdsddss 12 22 , , 00TV33 00 33 0 0 0 1 2 f=+−−±−−+− T T fV fV4() T()() T fV T fV() fV −+ T* f() fV − T V ++−+() T T f() V V (2) ds,2 11221122 V V 22111112121212 V V V V V 1111221122 V 1122 −1 2 2 TfV* − α =+ 12V 12 ds, arccos 1 4 (3) −− + ±2* +()() +2 + −() − −() +() − −() + +22 T11 T 22 fVVV 11 fV 22 T 11 T 22 fV V 11422 T 12 fV V 12 T 12 fV V 12 T 11 T 22 f V V 11 V 22 f V T 22 fV V 11 V 22 fV V 22 _________________________________________________________________________________________ In a first step a forward modeled Bragg surface Within the first part of the hybrid decomposition a scattering component is used to confine the volume model-based removal of the volume scattering scattering intensity fv in a physical manner according to component (cf. Eq. 1) is conducted including the the approach in [3,5]. This constraint includes a variety physical constrained volume intensity fv to remove the of assumed dielectric levels (εs-levels), which are vegetation contribution and to obtain the ground incorporated into the forward model. Hence, multiple scattering components (surface, dihedral) [3,5]. solutions for different εs-levels are created and a Afterwards the second part of the hybrid unique solution will be found in the third step of the decomposition involves an eigen-based separation of algorithm, as indicated in Fig. 2. the surface from the dihedral scattering components. In the second step this physically constrained volume The corresponding intensities (fd, fs) and scattering intensity component fv is incorporated into the hybrid mechanisms (αd, αs) of the ground components decomposition for separation of volume and ground ([TG]=[TS]+[TD]) can be calculated from the scattering components. In contrast to [3] a generalized eigenvalues and eigenvectors of [TG] (cf. Eqs. 2-3). An vegetation volume, included in [5], is proposed instead orthogonality condition, proposed in [4], ensures the of the classical case of a random volume of dipoles: physically meaningful separation of surface from dihedral scattering as detailed in [3,5]. VV11 12 0 * TfVV= 0 Hence, the surface/dihedral scattering contributions are Vv12 22 (4) 00V33 extracted for a set of assumed εs-levels originating 112 ()AA+−Δ11sinc20()2 ()ψ from the volume constraining. 22pp Thus in the determination step, the corresponding εs- f 112 2 =−Δ−Δv ()AA2 1sinc2()ψψ() 1() 1+sinc4() 0 12+ A2 pp 4 level for each pixel is found. Two alpha criteria enable p 1 −Δ2 ()ψ the retrieval of the best matching ε -level for an 0011-sinc4()Ap () s 4 optimum selection of the respective surface and In Eq. 4 Ap denotes the particle anisotropy, which is dihedral scattering component after a physically linked to the shape of the vegetation constituents constrained volume removal. forming the volume. Δψ represents the distribution The first alpha criterion, called α1-criterion, was width of the orientation angles and stands for the already introduced in [3,5] and is refined for this degree of orientation of the particles within the approach by including an urban/forest mask before vegetation volume. Fig. 3 characterizes the meaning of applying the criterion in order to exclude inappropriate both volume parameters in a conceptual way [5]. scatterers from the evaluation. Hence a mean εs-level is defined for the entire scene. With the second alpha criterion, called αmin-criterion, a pixel-wise refinement of the mean εs-level is achieved, as depicted in Fig. 4. Figure 3: Conceptual description of vegetation volume parameters Ap (particle anisotropy) and Δψ (particle orientation distribution). Figure 4: Schematic workflow of the αmin-criterion for a pixel-wise refinement of the εs-level determination. Therefore αmin is calculated, which represents the vegetation parameters were measured on selected test minimum of the first and the second scattering alpha fields with a great variety of different crop and soil angle (α1, α2) of the eigen-decomposition of [TData]. types to characterize the soil and the biomass layer. This assures that for pixels, where the most dominant Fig. 5 displays the results of the soil moisture inversion ground scattering in α1 is dihedral and the surface under vegetation for the TERENO 2012 campaign. The scattering mechanism is located in α2, the appropriate soil moisture is scaled for each data take individually, alpha angle, representing mostly surface scattering, is but always within the range of 0vol.% to 50vol.%. used for comparison with the αs-solutions from the White areas represent non-physical results due to hybrid decomposition. Also for this criterion an model mismatch in the inversion process, while urban/forest mask is applied to exclude inappropriate forested as well as urban areas are masked gray. scatterers from the evaluation. Subsequently, the The inversion rate is constantly on a very high level minimum between αmin and the multiple solutions for αs and does not drop below 98% for the two scenes, is calculated pixel-wise to retrieve the corresponding respectively. Compared to previous results in [3,5] the εs-level. If no minimum can be found, the mean εs-level almost continuous and gapless inversion is also from the already applied α1-criterion is used for the ensured for these data sets by application of the corresponding pixel. In the end the εs-level, the physically constrained hybrid decomposition and physically constrained fv and the respective, inversion for soil moisture under vegetation cover. decomposed surface scattering mechanism αs are obtained for inversion of soil moisture Hence, a low parameterized electro-magnetic surface scattering model (Bragg/ SPM) is applied to retrieve the closest match between modeled (βm,) and decomposed surface scattering mechanism (β=f(αs)) for subsequent soil moisture (mv) inversion [3,5]. β −=−ββα (5) Surface: mv = min{ ms} with tan( ) βm is the link to the volumetric soil moisture [3], because it is formed by the horizontal and vertical Bragg reflection coefficients (RHH, RVV) [5]. These θ coefficients depend only on the local incidence angle of the acquisition system and the dielectric constant of the soil εs. Finally, the dielectric constant εs is transformed into the volumetric soil moisture by a universal conversion polynomial of Topp et al. [6]. 3. EXPERIMENTAL RESULTS The refined, hybrid polarimetric decomposition and soil moisture inversion algorithm is applied on the data set of the TERENO campaigns, whereby the assumption of a random volume was applied within the generalized volume model (Ap=0, Δψ=90°).
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