Speaker
Description
Soil moisture is a critical parameter when agricultural practices, such as irrigation management, are involved and in 2010 it has been included among the Essential Climate Variable (ECV). Synthetic Aperture Radar (SAR) data allow estimating soil moisture based on high temporal frequency and accuracy. Unfortunately, radar data are not only sensitive to the dielectric properties of the soil, i.e. soil moisture content, but also to other parameters such as surface roughness and vegetation (i.e., growth stage, plant height, vegetation water content, etc.). During the years, SAR polarimetry has offered an important framework for soil moisture retrieval [1], [2], [3]. In particular, the use of a polarimetric SAR decomposition could help to separate the effects introduced by the vegetation from the ones related to the soil by extracting different scattering contributions: surface; double-bounce, coming from the interaction between the soil and the vertical structure of the plants; volume, related to the canopy. The objectives of this analysis are: 1) to analyze which scattering mechanisms is mostly correlated to soil moisture variations or changes in vegetation in view of a future integration within a soil moisture retrieval algorithm; 2) to compare the scattering contributions derived from the application of different polarimetric decompositions with the ones obtained from the fully polarimetric model developed at Tor Vergata University [3], offering the possibility to tune the polarimetric decompositions with the model; 3) to apply the polarimetric decomposition approach to a time-series of simulated covariance/coherency matrices (C3/T3) obtained from the Tor Vergata model and evaluate if the scattered power from the decompositions is correctly assigned to the proper scattering mechanisms. These objectives will be accomplished by applying different polarimetric SAR decompositions, such as the well-known Freeman-Durden 3-components decomposition [4], to a time-series of L-band full-polarimetric SAOCOM-1A data collected over five agricultural fields (i.e., corn fields) located in the Monte Buey area (Córdoba Province, Argentina) between October 2019 and February 2020. The temporal evolution of the backscattering coefficients at different polarizations and the scattered powers associated with the scattering contributions extracted from the polarimetric decompositions are evaluated with respect to both in-situ and satellite-derived parameters. The results obtained by applying the polarimetric decompositions will be compared with the ones obtained by the application of the Tor Vergata electromagnetic model, which is able to simulate the radar backscatter along with the three different contributions (surface, double-bounce, and volume) by using the in-situ data as training dataset. Finally, the polarimetric decompositions are applied to a time-series of simulated covariance/coherency matrices obtained from the Tor Vergata model in order to evaluate and compare the results in terms of scattering mechanisms. We recall that the main objective of this analysis is to assess the capability of separating the scattering contributions that are mostly correlated with the soil moisture variations from those influenced by the vegetation, and integrate them within a future soil moisture retrieval scheme.
Keywords: electromagnetic modeling, Synthetic Aperture Radar, SAR polarimetry, L-band, SAOCOM-1A, soil moisture, vegetation, agriculture.
Acknowledgments
This work has been supported by the Italian Space Agency (ASI) in the framework of the Clexidra project.
References
[1] I. Hajnsek, T. Jagdhuber, H. Schon and K. P. Papathanassiou, “Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 2, pp. 442-454, Feb. 2009.
[2] H. Wang, R. Magagi, K. Goita, T. Jagdhuber, I. Hajnsek, “Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields,” Remote Sens. 2016, 8, 142.
[3] H. Shi, L. Zhao, J. Yang, J. M. Lopez-Sanchez, J. Zhao, W. Sun, L. Shi, P. Li, “Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques,” Remote Sens. Env., vol. 261, 2021.
[4] M. Bracaglia, P. Ferrazzoli, and L. Guerriero, “A fully polarimetric multiple scattering model for crops,” Remote Sens. Env., vol. 54, no. 3, pp. 170-179, 1995.
[5] A. Freeman and S. L. Durden, "A three-component scattering model for polarimetric SAR data," IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 963-973, May 1998.
