Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

Abstract

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

Contribution of Polarimetry and Multi-Incidence to Soil Moisture Estimation Over Agricultural Fields Based on Time Series of L-Band SAR Data

“…In equation (4), the number of unknown soil dielectric constants (N) is larger than the observation equations (N-1), hence, the soil moisture retrieval is expressed as an underdetermined problem which is usually solved by employing a bounded linear least-squares optimization [27]. Once the α P P values in (6) have been estimated, the complex-valued dielectric constant is inverted for each observation and mapped to soil moisture content using a dielectric mixing model [29].…”

“…Over the last four decades, considerable research efforts have been devoted to soil moisture estimation by means of synthetic aperture radar (SAR) and proved the potential of SAR data at L, C, and X bands for estimating SSM over bare and vegetated soils [2], [3]. The use of physical models (IEM, AIEM), semi-empirical models (Oh, Dubois, and Shi) [4], decomposition theorems (Freeman-Durden, Yamaguchi, and Cloude-Pottier) [5], [6], [7], change detection techniques [8], [9], [10], and statistics-based methods [11], [12], [13] applied to multiple SAR observations have improved the capability to obtain SSM information at high spatial resolution (less than 10 m).…”

“…IEM and AIEM) scattering models could be used to estimate the SSM from the remaining ground scattering components [19], [20]. Notably, the complexity of the volume scattering model [4], [5] and the number of modeled scattering components are important aspects in the description of the scattering properties of this type of scenes. Another strategy to take into account the presence of vegetation on the ground consists in adopting statistical approaches, for instance based on Bayes theorem or on machine learning methods [11], [12], [21].…”

Evaluation of Terrestrial Laser Scanner and Structure from Motion photogrammetry techniques for quantifying soil surface roughness parameters over agricultural soils

“…Profilometer data processing was done in three steps: (1) correction of the aluminum beam bending using a lab determined TLS AND SFM FOR QUANTIFYING SURFACE ROUGHNESS OVER AGRICULTURAL SOILS parabolic function, (2) outlier filtering by deleting and interpolating records larger than a threshold (i.e., 2 cm) with the previous and following records (to filter out vegetation elements eventually present on the soil surface), and (3) terrain slope correction (i.e., profile detrending) subtracting the linear trend observed in the data, if any. The laser profilometer was considered a benchmark for 2D roughness measurements for several reasons: (1) its vertical accuracy is high; (2) its nadir-looking geometry avoids occlusions, and; (3) although it measures 2D profiles and not 3D surfaces, it has been the standard technique to characterize surface roughness for different applications for the last decades (Oh et al, 1992;Helming et al, 1998;Davidson et al, 2000;Darboux and Huang, 2003;Callens et al, 2006;Baghdadi et al, 2008;Verhoest et al, 2008) and is still used at present (El Hajj et al, 2019;Zribi et al, 2019). Thus, it can be considered a state of the art technology in the field of surface roughness measurement.…”

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

“…In a study, Zribi et al [29] have applied Water Cloud Model (WCM) on L-band PALSAR/ALOS-2 satellite data to estimate soil moisture in a tropical agricultural area under dense vegetation cover conditions. Yang et al [30] have used the fully polarimetric C-band Radarsat-2 SAR data for soil moisture mapping in Juyanze Basin, China.…”