An efficient and robust soil moisture (SM) sampling scheme that can capture the spatial variability of SM is required for the accurate calibration and validation of satellite-based SM retrievals. Often, this process requires numerous sampling points, consuming a significant amount of time. Therefore, it is crucial to develop efficient sampling methods for the improvement of satellite-based SM estimations. The objectives of this study were to define an efficient sampling strategy that could be beneficial for the validation of satellite SM estimations; investigate the role of RS covariates in developing such a strategy; and evaluate the performance of the new sampling scheme over various spatial and temporal domains. In this study, we used the conditioned Latin hypercube sampling (cLHS) algorithm to define an efficient sampling strategy. To this end, remote sensing (RS) raster and digital elevation models (DEM) were used to identify numerous environmental covariates to locate sampling points for characterizing spatial variability of SM at the agricultural field scale. A random forest-based technique, the Boruta algorithm, was also applied to select the most important covariates for utilization into the cLHS algorithm. We used the statistical moments (mean and standard deviation, SD) of the field to select the efficient sample size that can best represent SM status in the field. To evaluate the new sampling scheme, a second data set obtained during a different month for the same agricultural field was used. However, because of the potential for high spatial and temporal correlations between training and test covariates when obtained for the same region, we also used different test datasets in New Zealand to evaluate the sampling scheme. Results showed that the RS covariates obtained from SAR and optical imagery were among the most significant covariates for capturing the spatial variability of SM even if they were not acquired on the day of collection. Also, the new sampling scheme could capture the SM spatial pattern of the field for both test datasets with RMSE less than 4% volumetric SM, which is within the range of the expected performance for most satellite SM products. The evaluation of the new sampling scheme on the New Zealand datasets confirmed the functionality of the proposed sampling scheme for a different temporal and spatial domain.

Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R2 = 0.70) and a relatively close agreement with pin profiler (R2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.

Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters.

Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters.