This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman-Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the Root Mean Square Error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in situ data, shows a significant improvement: the calibrated GFD powers leads to an increased linear correlation (0.32 to 0.57), while the RMSE is reduced (0.096 to 0.055 m³/m³).

This study explores the synergy of electromagnetic data modeling and Artificial Intelligence (AI) algorithms for soil moisture retrieval over agricultural fields using Polarimetric SAR (PolSAR) data. SAR acquisitions are considered as a valuable source for accurate estimation of soil moisture in agricultural areas. However, its retrievals are influenced by various factors, including vegetation cover. In this context, the polarimetric information of SAR acquisitions allows the interpretation of the occurring scattering processes. The proposed approach involves a physics-aware AI algorithm based on two Artificial Neural Networks (ANNs), trained on electromagnetic (EM) model simulations at L-band. Starting from a full polarimetric Covariance Matrix, the first AI model separates the different scattering contributions, estimating surface and double-bounce scattering mechanisms while minimizing the attenuation effects caused by the vegetation layer. Then, the estimated surface and double-bounce components are fed to an additional AI model to retrieve soil moisture. Field campaign data over actual corn fields were considered and ingested by the EM model to generate synthetic SAOCOM-like case studies which were used to validate the approach within the simulated domain.

In this paper, the results obtained by applying two polarimetric SAR decompositions to a time-series of L-band radar data, in terms of scattering contributions, are compared with the simulations of the Tor Vergata electromagnetic model. The objective was to evaluate the capability of polarimetric SAR decompositions to single out those scattering mechanisms mostly correlated to soil moisture or vegetation. We performed the analysis by using L-band full-polarimetric SAOCOM-1A data acquired over an agricultural region in the Monte Buey site (Córdoba Province, Argentina) and by considering five corn fields.

Wildfires are one of the most significant threats to ecosystems and are increasing in frequency globally. The aim of this study is to monitor the evolution of selected wildfires in Greece that occurred during August 2021 using Sentinel-1 SAR data and unsupervised k-means clustering in Google Earth Engine. First, changes in time series after the start of the fire and the influence of precipitation were investigated. In this study, the influence of different speckle filters and post-classification filters on clustering results was tested. The difference Normalized Burn Ratio Index (dNBR) derived from Sentinel-2 data was used as a validation dataset to assess accuracy using the F1-score, overall accuracy, omission and commission error. The best achieved F1-scores were higher than 0.70 with omission error lower than 35% in all selected areas, where the Lee speckle filter with an 11x11 kernel window size and a 2 ha post-classification filter performed the best.

Soil moisture represents a significant guiding factor for agricultural activities, especially for smart irrigation and crop yield estimation. In this context, SAR data is one of the most valuable sources of information for accurate and continuative estimation of soil moisture in agricultural areas. However, SAR-derived soil moisture retrievals are affected by several factors, including the vegetation cover, which is responsible for additional signal attenuation and scattering mechanisms. Concerning the algorithms for soil moisture estimation, Machine Learning (ML) has proved to be a valuable instrument for finding relations between SAR data and the soil dielectric properties. For this purpose, this study aims to synergically adopt electromagnetic data modelling and a ML algorithm to estimate the scattering contributions associated with the ground and demonstrate that they are more sensitive to soil moisture than the total received signal. To apply such approach to real SAR data, airborne acquisitions at L-band will be considered.

We present a physics-based machine learning (ML) approach for estimating corn plant height from simulated synthetic aperture radar (SAR) data. The proposed study intends to demonstrate the physical awareness of datadriven approaches such as ML. In this regard, a multilayer perceptron (MLP) artificial neural network (ANN), designed for corn plant height estimation, was trained with simulated C- and L-band SAR data generated using a state of the art electromagnetic model for microwave backscattering from terrain covered with vegetation. Here we show how the most significant connections between the nodes composing the network and the most relevant input variables can be detected, demonstrating the physical meaning behind the mapping criteria of the network itself.