Continuous and operational monitoring of forest canopy structure plays an important role in assessing the global carbon budget, mapping forest disturbance, planning restoration activities, and informing decision-making. Several studies have taken advantage of synthetic aperture radar (SAR) for forest mapping and monitoring because of its regular reliable acquisitions and high sensitivity to the structural and dielectric properties of the forest. This work utilizes the Senitnel-1 C- and ALOS-2 PALSAR-2 L-band interferometric coherence for canopy height estimation in savanna woodlands. A simplified physics-based Random Volume over Ground (RVoG) model is used for the height estimation. This study uses datasets collected over two test sites, one in Injune, Australia, and the second in Kruger National Park (KNP), South Africa. The proposed method achieved an overall RMSE of 2.39m for canopy height with a Pearson coefficient, r = 0.83 by simultaneous use of both C- and L-band coherence.

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. We have trained and validated the proposed model utilising backscatter information from E-SAR C- and L-band dual-polarimetric data acquired during the AGRISAR 2006 campaign. Further, we use the Product of Experts (PoE) fusion strategy to combine decisions from the proposed Gaussian Process (GP) models trained and validated independently over C- and L-band data to analyze the changes in the classification performance. The synergistic C- and L- band information show an improved classification accuracy during various phenological stages of major crop types by (a) 4 to 37 % for VV-VH backscatter intensity channels and (b) 1 to 39 % for HH-HV backscatter intensity channels.

This study proposes an extended temporal correlation model for targets with a noticeable periodic seasonal trend. Several studies have explored the nature of decorrelation in synthetic aperture radar (SAR) interferograms. Specifically, providing a model the decay in interferometric correlation over time between two images remains a challenging task. Initially, it is necessary to assume that the contributions of distributed elements within the same pixel undergo a change, leading to a reduction in correlation with previous acquisitions. The exponential decay model is the simplest and most widely used in the scientific community by considering coherent and incoherent groups of scatterers within a resolution cell. However, the coherence over vegetation canopies with seasonal behavior does not exhibit a monotonic exponential decay with time. Hence, in this study, we introduce a periodic term to account for the nature of this seasonality. The performance of the proposed model is evaluated with a total of nearly 2000 Sentinel-1 interferometric SAR (InSAR) pairs acquired over two test sites located one in Nallamala, India, and the other in Injune, Australia. The proposed model performed significantly better than the exponential model with up to 83% improvement in RMSE in modeling the long-term coherence over vegetation with strong seasonal patterns.

The study of soil moisture is crucial for understanding the hydrological cycle and its impact on energy and water exchanges at the land-atmosphere interface. Synthetic Aperture Radar (SAR) data, such as Sentinel-1, has shown potential for estimating soil moisture at high spatio-temporal resolutions. However, the sensitivity of SAR responses to soil moisture and the applicability of Sentinel-1 data for soil moisture estimation at different land covers require further investigation. In this paper, we evaluate the sensitivity of Sentinel-1 data for soil moisture estimation and compare the estimated soil moisture for different land covers. A change detection approach combined with a vegetation scattering model is employed to estimate soil moisture. The results demonstrate that while the change detection approach with vegetation correction improves soil moisture estimation, the accuracy is still not within an acceptable range for plot-level decision-making, such as irrigation management. However, the results show that the soil moisture information obtained from Sentinel-1 data can be suitable for regional-level monitoring applications and decision-making.

Polarimetric Synthetic Aperture Radar (SAR) data has been extensively used to estimate soil permittivity because of its high sensitivity to the dielectric properties of the target. However, the presence of vegetation cover induces bias in the permittivity estimates. This work utilizes the scattering-type parameters: alpha$(\overline{\alpha})$and theta$(\theta_{\text{FP}})$to estimate soil permittivity using the X-Bragg as the dominant surface scattering model. A theoretical study ascertains that the recently proposed$\theta_{\text{FP}}$is fairly robust towards the depolarizing component in the X-Bragg model. Hence, it is expected to produce better inversion accuracy. This study analyzes major phenology stages of Canola using the UAVSAR full-pol SAR data and the ground measurements acquired during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method achieved an RMSE of 5.9 for soil permittivity with a Pearson coefficient,$r=0.83$. Further, the temporal trend of the soil permittivity estimates also agrees with in-situ measurements for the entire timeframe.

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θFPand θCP, respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θFPand θCP, unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient,r≥ 0.62.

Accurate and high-resolution spatio-temporal information on wheat growth is an essential factor for agronomic management and grain yield estimation. In this study, we propose a new vegetation descriptor from the Sentinel-1 Synthetic Aperture Radar (SAR) GRD data for monitoring the growth stages of wheat. We also assess the performance of the proposed vegetation descriptor for estimating wheat biophysical parameters: Plant Area Index (PAI), Dry Biomass (DB), and Vegetation Water Content (VWC) over the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) test site in Manitoba, Canada. The proposed vegetation descriptor produced good correlation$(R^{2})$with the biophysical parameters of wheat: 0.63 (PAI), 0.64 (DB), and 0.57 (VWC) compared to$\sigma_{\text{VH}}^{\mathrm{o}}/\sigma_{\text{VV}}^{\mathrm{o}}$and the dual-pol Radar Vegetation Index (RVI).

Built-up area extraction from remote sensing images is essential for urban planning, disaster management and industrial development. In this study, we propose two built-up area indices for full (FP) and dual (DP) polarimetric Synthetic Aperture Radar (SAR) data. The built-up area index for FP SAR data is based on the dominant scattering mechanism of the electromagnetic (EM) waves from urban targets. In contrast, the built-up area index for DP SAR data is based on the scattering reflection symmetry property. The two proposed indexes are validated with full and extracted dual pol (VV-VH) scenes of a C-band RADARSAT-2 SAR data over urban San-Francisco. They show encouraging results in detecting urban areas within a SAR resolution cell. The overall accuracy of delineating built-up area is 84.2% for FP SAR data and 79% for DP SAR data.

In contrast to the widely used van Zyl received wave polarimetric signature, the Touzi scattered wave signature in term of the total power ($S$0), and the degree of polarization ($p$) is also helpful for target characterization. Although, the van Zyl polarimetric signature includes the contribution of So, and p, the explicit consideration of the two scattered wave parameters (i.e., independent of the received wave polarization basis) can provide additional information about the target. Hence, in this study, we have used both the van Zyl received, and Touzi scattered wave information to characterize scattering from Paddy at a particular phenological stage with C- and L-band full polarimetric SAR data.

In radar remote sensing applications, soil moisture retrieval over the vegetated surface is a challenging issue due to complex interaction of radar waves with vegetation layer and the underlying soil. Several studies utilized the Water Cloud Model (WCM) directly or by coupling it with surface inversion models, to compensate vegetation effects while estimating soil moisture. The realization of vegetation component in the original form of WCM utilizes various plant descriptors (e.g., vegetation water content (VWC) and plant area index (PAI)). These descriptors were eventually replaced with vegetation metric obtained from ancillary sources (e.g., the Normalized Difference Vegetation Index -NDVI derived from the optical sensor). To overcome this dependency on ancillary data, we utilize radar derived vegetation descriptors to estimate soil moisture over canola fields. We investigated the performance of WCM for soil moisture retrieval utilizing the PAI and radar derived vegetation descriptors, i.e., the ratio of backscatter intensities (HH/VV and VH/VV) and indices (viz., Radar Vegetation Index (RVI), and Generalized Radar Vegetation Index (GRVI)) in WCM. The radar data derived vegetation descriptors provides encouraging retrieval accuracy with RMSE ranging from 0.04 (for GRVI) to 0.08 m3m-3(for HH/VV). This comparative analysis using different polarizations indicates that the HH polarization outperforms VV, while VH has marginal deviations for all descriptors.

In this study, we propose a new vegetation index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed vegetation index is derived for two dual-pol (HH-HV and VV-VH) modes obtained through a simulation from L-band full-pol UAVSAR data. Time-series simulated NISAR data are obtained from the UAVSAR data acquired during the SMAPVEX12 campaign over the CAL/VAL test site in Winnipeg (Canada), to assess the proposed vegetation index. The temporal trend of DpRVI follows the growth stages of canola with a promising correlation of DpRVI with several biophysical variables. Correlation analysis indicates that DpRVI derived for VV-VH mode correlates better with the canola biophysical parameters than the HH-HV mode.