Conference Agenda

The X-band Synthetic Aperture Radar (SAR), with its short wavelength (~3 cm), offers exceptional spatial resolution and sensitivity to surface micro-structures—an attractive asset for precision agriculture. However, its limited penetration into vegetation and soil has historically constrained its utility for deeper biomass or subsurface retrievals.

Previous studies have demonstrated that X-band SAR data are particularly effective in distinguishing between surface roughness and tillage states over bare or sparsely vegetated soils (Baghdadi et al., 2002), owing to their enhanced sensitivity to micro-topographical variations. Nevertheless, several works have emphasized that this sensitivity decreases markedly in the presence of vegetation, as canopy volume scattering masks the soil contribution (Lopez-Sanchez & Ballester-Berman, 2009). More recently, multi-frequency comparative analyses have further confirmed that the X-band signal is predominantly sensitive to surface moisture and roughness, whereas deeper soil layers are better probed by lower-frequency systems (Zhou et al., 2025).

In this study, we explore two complementary aspects of repeat-pass PolInSAR data at X-band for agricultural applications: (1) the potential of repeat-pass interferometric phase coherence to reveal subsurface moisture and drainage features beneath cropped fields; and (2) the value of full polarimetry (orientation sensitivity, depolarisation metrics) to detect sowing ridges, tillage patterns, and crop growth stage via structural anisotropy of the soil–vegetation system.

Firstly, using TerraSAR-X Staring Spotlight acquisitions (single-polarisation, high resolution), we demonstrate that under specific seasonal and hydrological conditions, the interferometric phase (and coherence) signal exhibits sensitivity to subsurface moisture contrasts. In particular, we observe coherent phase-pattern anomalies aligned with known subsurface drainage networks beneath agricultural parcels—a surprising result, given the limited penetration of X-band. We interpret these signals as the result of dielectric contrasts in the shallow subsurface (wet vs dry soil), modulating the scattering phase behavior.

Secondly, employing an airborne, fully-polarimetric X-band sensor (SETHI), we investigate how polarimetric parameters (e.g., orientation angles and depolarization indices) vary with the presence or absence of sowing ridges and as a function of early crop growth stage. Our results show a clear correlation: fields with visible ridging manifest stronger anisotropy in orientation angle domains and lower depolarization, whereas mature crop covers produce more isotropic scattering and higher depolarization. Consequently, we find that the polarimetric signature can serve as a proxy for both soil preparation state (ridges vs flat) and early crop phenology.

Together, these findings illustrate the under‐exploited potential of X-band PolInSAR for precision agriculture: (i) as a sensor of subtle subsurface moisture heterogeneity via coherence/phase analytics, and (ii) as a structural probe of soil and crop architecture via polarimetry.

References

[1] Baghdadi, N., Holah, N., Dubois-Fernandez, P., Prévot, L., Hosford, S., Chanzy, A., ... & Zribi, M. (2004). Analysis of X-Band Polarimetric Sar Data for The Derivation of The Surface Roughness Over Bare Agricultural Fields. A A, 3, 2.

[2] Lopez-Sanchez, J. M., & Ballester-Berman, J. D. (2009). Potentials of polarimetric SAR interferometry for agriculture monitoring. Radio science, 44(02), 1-20.

[3] Zhou, X., Wang, J., Shan, B., He, Y., & Xing, M. (2025). Sensitivity of multi-frequency and multi-polarization SAR to soil moisture at different depths in agricultural regions. Journal of Hydrology, 133513.

This paper introduces Adaptive sum of Kronecker products for QUAntitative- Soil Moisture Anomalies retrieval (AKQUA-SMA), a novel polarimetric framework for SMA estimation. The core innovation lies firstly in the use of Temporal-SKP (T-SKP) [1], which exploits the temporal-polarimetric domain to isolate two scattering components: the moisture-related contribution, namely the Latent contribution, from the other scattering mechanism, namely Ground, i.e., above-ground scattering components. To do so, the SKP solution is chosen through exhaustive search as the one that minimizes the l1-norm of the error between the decomposed coherence values in the Latent structure matrix and the theoretical expected moisture related complex coherences. In particular, the search grid originates from the model proposed in [2], which has been slightly modified to account for a double-scattering mechanism. Then, the Ground component is chosen as the one that minimizes the phase residues, i.e., the most triangular scattering mechanism, and used for phase calibration similarly to what is done for the BIOMASS tomography. Finally, absolute N (zero-mean) SMA values are regressed by LS estimation from the estimated variations in the N(N-1)/2 InSAR pairs.

The AKQUA-SMA algorithm was applied to the Hydrosoil dataset [3], which was collected over a 20m × 58m agricultural field using a C-Band ground-based PolSAR (GB-PolSAR). The campaign aimed to simulate the frequent monitoring capability of the HydroTerra mission [3] for soil moisture and vegetation parameter retrieval. The data comprises two phases: the Barley Crop (March–June 2020), a Dual-Pol dataset (18,055 acquisitions), and the Corn Crop (July–November 2020), a Quad-Pol dataset (12,945 acquisitions), both acquired every 10 minutes. This SAR data is supplemented with essential ancillary information, including probe-based volumetric moisture, plant density, and crop height. Due to the limited field size, the entire area was treated as a single resolution cell. Processing utilized a full overlapping sliding window approach, scanning the dataset in steps of six acquisitions with five temporal samples overlapping between adjacent windows. The first results reveal good estimation accuracy, with a mode RMSE of 0.2% across the entire Corn dataset. Experiments involving the estimation of SMA through exhaustive search using the single polarization channels after phase linking are currently running, in order to assess the benefit of the multi-polarimetric approach w.r.t. the single-polarization one.

References

[1] Tebaldini, Stefano. ”Algebraic synthesis of forest scenarios from multibaseline PolInSAR data.” IEEE Transactions on Geoscience and Remote Sensing 47.12 (2009): 4132-4142.

[2] De Zan, Francesco, et al. ”A SAR interferometric model for soil moisture.” IEEE Trans actions on Geoscience and Remote Sensing 52.1 (2013): 418-425.

[3] Aguasca, Albert, et al. ”Hydrosoil, soil moisture and vegetation parameters retrieval with a C-band GB-SAR: Campaign implementation and first results.” 2021 IEEE Inter national Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021

Soil moisture is a crucial parameter in hydrology and agronomy applications [1,2] playing an important role in water resource and irrigation management. Synthetic Aperture Radar (SAR) data have been extensively used in several studies to retrieve the soil moisture across various land covers from bare to vegetated soil, including cropland, grassland, and forest [3,4]. Several scattering models have been developed to retrieve soil moisture beneath vegetation [5–7]. Using first–order radiative transfer (RT) backscattering models requires a complete description of vegetation structure. Thus, extensively used semi–physical models such as the Water Cloud Model (WCM) [8], rely on vegetation information (e.g., vegetation water content and biomass) derived from optical vegetation indices including LAI and NDVI. However, such indices provide a partial description of vegetation structure, leading to soil moisture retrieval errors. In this context, Deep Learning (DL) techniques can be synergically used with physics–based models to optimize their parameters and compensate the lack of vegetation structure description.

In this study, we apply the Physics-informed Residual Network (ResNet) model RTNet, proposed and validated using airborne data in our previous work [9], and specifically designed for accurate and physically meaningful soil moisture retrieval. Particularly, we extend the use of RTNet to spaceborne data from ALOS–2 acquisitions at HV polarization, together with multi-source remote sensing (RS) data, e.g., vegetation water content (VWC), soil texture, and weather information. While the HV polarization is used as input to the AI model, the HH polarization is employed, as described later, for the model optimization task.

The RTNet is designed to simultaneously perform two optimization tasks. The first one aims to estimate the parameters of a first–order RT model, namely the four scattering contributions (i.e., surface, double, volume, and triple scattering) and the attenuation term. The second task focuses on soil moisture retrieval starting from the optimized scattering components, inspired by the purely physical modeling approach proposed in [10]. Thus, two loss functions are defined: the first minimizes the difference between the measured total backscatter at HH polarization and the estimated one, computed as the sum of the optimized attenuated scattering components, while the second minimizes the difference between the retrieved soil moisture and in–situ measurements. These two losses are properly combined to guide the RTNet training, ensuring an accurate and consistent soil moisture retrieval by coupling it with the optimization of the scattering mechanisms based on physical modeling. To guarantee the physical validity of the estimation and to avoid ill–posed solutions, additional physical constraints are imposed within the loss function.

The methodology is validated using soil moisture measurements from the SCAN network dataset [11] and ALOS–2 L–band acquisitions aggregated to a 100 meter resolution over six test sites characterized by different land cover types. On the other hand, the optimized RT parameters, i.e., the scattering mechanisms, are evaluated through a comparison with well–established polarimetric decompositions, e.g., the Freeman–Durden three–component decomposition (FD3) [12], the Yamaguchi four–component decomposition with rotation of the coherency matrix (Y4R) [13], to assess the strengths and limitations of the proposed approach.

As a future development, the framework will be applied to L–band NISAR data, offering the opportunity to evaluate the robustness of the proposed framework against global soil-vegetation condition variabilities.

To conclude, this methodology goes beyond retrieving soil moisture. Instead, through the ingestion of data from heterogeneous sensors and the decomposition of the radar signal into its scattering components, it provides a comprehensive tool useful for investigating soil–vegetation interactions, seasonal changes, and other applications such as land cover characterization and crop type identification, opening new possibilities for development, training, and validation of explainable physics–informed AI models.

References

[1] Schaufler, G., Kitzler, B., Schindlbacher, A., Skiba, U., Sutton, M.A., & Zechmeister-Boltenstern, S. (2010). Greenhouse gas emissions from European soils under different land use: effects of soil moisture and temperature. European Journal of Soil Science, 61.

[2] Sheffield, J., Goteti, G., Wen, F., & Wood, E.F. (2004). A simulated soil moisture based drought analysis for the United States. Journal of Geophysical Research, 109.

[3] Hosseini, M., & Mcnairn, H. (2017). Using multi-polarization C- and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields. Int. J. Appl. Earth Obs. Geoinformation, 58, 50-64.

[4] Baghdadi, N.N., El-Hajj, M., & Zribi, M. (2016). Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 1229-1243.

[5] Baghdadi, N.N., & Zribi, M. (2006). Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations. International Journal of Remote Sensing, 27, 3831 - 3852.

[6] Fung, A.K., & Chen, K. (2004). An update on the IEM surface backscattering model. IEEE Geoscience and Remote Sensing Letters, 1, 75-77.

[7] Bracaglia, M., Ferrazzoli, P., & Guerriero, L. (1995). A fully polarimetric multiple scattering model for crops. Remote Sensing of Environment, 54, 170-179.

[8] Attema, E., & Ulaby, F.T. (1978). Vegetation modeled as a water cloud. Radio Science, 13, 357-364.

[9] Huang, X., Papale, L.G., Lavalle, M., Del Frate, F., Fattahi, H., Chan, S.K., Lohman, R.B., Xu, X., & Kim, Y. (2025). Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 12697–12712.

[10] Hajnsek, I., Jagdhuber, T., Schon, H., & Papathanassiou, K. P. (2009). Potential of estimating soil moisture under vegetation cover by means of PolSAR.IEEE Transactions on Geoscience and Remote Sensing, 47(2), 442-454

[11] Schaefer, G.L., Cosh, M.H., & Jackson, T.J. (2007). The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). Journal of Atmospheric and Oceanic Technology, 24, 2073-2077.

[12] Freeman, A., & Durden, S.L. (1998). A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote. Sens., 36, 963-973.

[13] Yamaguchi, Y., Sato, A., Sato, R., Yamada, H., & Boerner, W. (2010). Four-component scattering power decomposition with rotation of coherency matrix. 2010 IEEE International Geoscience and Remote Sensing Symposium, 1327-1330.

Observing changes in soil moisture with differential Synthetic Aperture Radar (SAR) interferometry (D-InSAR) has gained relevance due to shorter satellite revisit times, bigger swath widths, higher spatial resolution of SAR systems, and the introduction of L-band satellites. The geometric configuration of D-InSAR mitigates topographic effects, baseline-induced distortions, and geometric decorrelation.

In this context, the separation of volume, surface and dihedral scattering in the SAR signal is essential for limiting uncertainty in the accurate estimation of soil moisture. Using D-InSAR recent soil moisture forward models are able to account for varying soil states and surface and subsurface volume scattering combinations, although they assume the absence of topographical variations and vegetation between acquisitions [1], [2]. Whereas former can be approximately removed by using phase triplets, separating above ground volume from surface scattering remains a challenge. To address this, polarimetric information has been shown to be sensitive to vegetation [3].

To enhance soil moisture estimation we combine existing D-InSAR electromagnetic forward models with polarimetric information. Here, the copolar phase differences (CPD) [4] can been used to counteract the need of auxiliary geometries and fully-polarimetric systems. CPD has been shown to be highly sensitive to volume scattering due to the structural anisotropy of vegetation.

This analysis will focus on quantifying the performance of Zwieback et al. [2] at L-Band and HH/VV polarizations by deploying it on the recent AgriROSE-L dataset [5]. The limits of the model will be explored in respect to its parameterization, the degree of vegetation and numerous soil moisture levels. First, we test the model performance over bare ground. Secondly, we analyze the value of CPD for identifying vegetated and non-vegetated pixels. Third, we provide a first approach of integrating a CPD indicator into current soil moisture forward models.

References

[1] F. De Zan, A. Parizzi, P. Prats-Iraola, and P. López-Dekker, “A SAR interferometric model for soil moisture,” IEEE Transactions on Geoscience and Remote Sensing, 2013.

[2] S. Zwieback, S. Hensley, and I. Hajnsek, “A polarimetric first-order model of soil moisture effects on the DInSAR coherence,” Remote Sensing, vol. 7, no. 6, pp. 7571–7596, 2015.

[3] G. Anconitano, M. Lavalle, M. A. Acuña, and N. Pierdicca, “Sensitivity of polarimetric SAR decompositions to soil moisture and vegetation over three agricultural sites across a latitudinal gradient,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3615–3634, 2023.

[4] V. Brancato and I. Hajnsek, “Analyzing the influence of wet biomass changes in polarimetric differential SAR interferometry at L-band,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1494–1508, 2018.

[5] H. I. Schauer, N. Basargin, and I. Hajnsek, “Airborne L-Band soil moisture retrieval over agricultural areas in preparation for ESA ROSE-L mission,” 2025.

Recently, the German Aerospace Center (DLR) conducted the AgriROSE-L 2025 (CROPEX 2025) airborne F-SAR campaign in preparation for the upcoming ESA ROSE-L mission. Multiple flights acquired a long and dense (6-day revisit) time series of fully polarimetric SAR acquisitions over agricultural areas between April and July 2025. Ground teams accompanied each flight to collect in situ measurements and record the soil and vegetation conditions. Agricultural areas are subject to rapid changes caused by crop growth, transitions of phenological stages, and alterations in soil conditions, including changes in soil moisture and roughness. The campaign provides a valuable dataset for analyzing the dynamics of the polarimetric signal over time and developing new methods for geophysical parameter retrieval.

In this study, we provide an early evaluation of the new dataset, focusing on soil moisture estimation, an essential climate variable (ECV) important for hydrology and agriculture. A significant challenge for SAR-based high-resolution soil moisture estimation is the presence of vegetation, which interferes with the ground signal. Here, the use of longer wavelengths (L-band) with deeper penetration is beneficial in minimizing the influence of vegetation. Additionally, using fully polarimetric data enables a certain degree of separation between the ground and vegetation contributions.

We apply a model-based tensor decomposition [1] to separate the signal into surface, dihedral, and volume contributions. The method jointly exploits polarimetric and temporal information, analyzes a time series of acquisitions, and provides estimates for different geophysical parameters, including soil moisture. We validate the accuracy across different crop types and growth stages using the in situ measurements acquired during the campaign.

References

[1] N. Basargin, A. Alonso-González, and I. Hajnsek, "Model-based tensor decompositions for geophysical parameter retrieval from multidimensional SAR data," Submitted to IEEE Transactions on Geoscience and Remote Sensing, under review.