The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values. Large- and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C- or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.

Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5–13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur.

Wetland ecosystems play a key role in hydrological and biogeochemical cycles. In emergent vegetation targets, the occurrence of double-bounce scatter is indicative of the presence of water and can be valuable for hydrological monitoring. Double-bounce scatter would lead to an increase of σ⁰_HH over σ⁰_VV and a non-zero co-polarized phase difference (CPD). In the Lower Paraná River floodplain, a total of 11 full polarimetric RADARSAT-2 scenes from a wide range of incidence angles were acquired during a month. Flooded targets dominated by two herbaceous species were sampled: Schoenoplectus californicus (four sites, Bulrush marshes) and Ludwigia peruviana (three sites, Broadleaf marshes). As a general trend, σ⁰_HH was higher than σ⁰_VV, especially at the steeper incidence angles. By modeling CPD with maximum likelihood estimations, we found results consistent with double-bounce scatter in two Ludwigia plots, at certain scene incidence angles. Incidence angle accounted for most of the variation on σ⁰_HH, whereas emergent green biomass was the main feature influencing σ⁰_HV. Multivariate models explaining backscattering variation included the incidence angle and at least two of these variables: emergent plant height, stem diameter, number of green stems, and emergent green biomass. This study provides an example of using CPD to decide on the contribution of double-bounce scatter and highlights the influence of vegetation biomass on radar response. Even with the presence of water below vegetation, the contribution of double-bounce scatter to C-band backscattering depends on scene incidence angles and may be negligible in dense herbaceous targets.

This research aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged with fully polarimetric synthetic aperture radar (SAR) images with incidence angles ranging from 20° to 60°. The dataset comprised two field campaigns, one over Canada with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR, 1.258 GHz) and the other one over Argentina with Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) (ALOS-2/PALSAR-2, 1.236 GHz), totaling 60 data measurements over 28 grown corn fields at peak biomass with stalk gravimetric moisture larger than 0.8 g/g. Co-polarized phase differences were computed using a maximum likelihood estimation technique from each field’s measured speckled sample histograms. After minimizing the difference between the model and data measurements for varying incidence angles by a nonlinear least-squares fitting, well agreement was found with a root mean squared error of 24.3° for co-polarized phase difference measurements in the range of −170.3° to −19.13°. Model parameterization by stalk gravimetric moisture instead of its complex dielectric constant is also addressed. Further validation was undertaken for the UAVSAR dataset on earlier corn stages, where overall sensitivity to stalk height, stalk gravimetric moisture, and stalk area density agreed with ground data, with the sensitivity to stalk diameter being the weakest. This study provides a new perspective on the use of co-polarized phase differences in retrieving corn stalk features through inverse modeling techniques from space.

Validation of soil moisture products derived from synthetic aperture radar (SAR) remotely sensed observations involves a comparison against ground-truth data. This validation step helps one to state the performance of competing retrieval algorithms. Nevertheless, the design of a field experiment in the context of SAR retrieval is not straightforward. Ground-based measurements are affected by instrument errors due to both the physical limitations of the measurement technique and the uncertainties related to the spatial variability of the soil moisture. To properly assess the performance of the retrieved estimates, both of the mentioned sources of uncertainties should be considered in the ground-based sampling and in the subsequent error assessment analysis. This letter addresses the rationale behind an optimal field experiment designed to assess the suitability of soil moisture retrieval algorithms.

A dense time series of Synthetic Aperture Radar acquisitions at 6-day intervals between July 2017 to January 2019 collected with the C-band constellation Sentinel 1A and 1B is used to study salt crust evolution in an highland salar. Microwave response of halite crystal aggregates is linked to surface roughness of the salt crusts by means of a surface scattering model which includes multiple scattering at second order in media with complex permittivity such as brine-soil mixtures. The time series enabled to estimate co-polarised vertical-vertical backscattering coefficient variations as large as 8.8 dB on a 4-month period which implied a height standard deviation increase from 0.5 to 4.5 mm as modeled by the surface scattering model. Backscattering coefficient variations between 0.8 to 2 dB per month are found for three different crusts, which demonstrated different growth rates of the crystals. Crystal growth rate might be driven by the kind of water input (rainfall or snow in Andean salars), probably due to the negative effect of water droplets on impinging halite crystal surface in comparison to snow. Results showed that cross-polarised backscattering coefficient is sensitive to snow accumulation and appeared to be sensitive to subsurface conditions.