Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

Abstract

Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.

Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

“…With the objective of simplifying the parameterization of the retrieval models the use of the proposed σ D (NDVI) allowed to integrate vegetation effects in the roughness parameter, as previously suggested by Capodici et al [52]. Note that, recently, Hamze et al [92] estimated soil roughness from L-band images, improving significantly soil moisture mapping derived using C-band SAR data. In addition, El Hajj et al [93] demonstrated that the use of L-band images can be attractive for high NDVI (>0.7) in several crops, overcoming the limitations of C-band.…”

“…In addition, El Hajj et al [93] demonstrated that the use of L-band images can be attractive for high NDVI (>0.7) in several crops, overcoming the limitations of C-band. Nowadays, L-band images are not available with high revisit time, in contrast with the C-band images of Sentinel 1, and, therefore, are not appropriate for operational soil moisture mapping [92]. In the next future, L-band images should be available at higher revisit time with the new SAR missions (e.g., NISAR (NASA-ISRO SAR), and Tandem-L; [92]), becoming attractive for operational approaches.…”

Comparative Analysis of the Sensitivity of SAR Data in C and L Bands for the Detection of Irrigation Events

“…SAR data has been recently exploited to detect irrigation events, especially with the availability of the free and open-access C-band SAR images offered by the Sentinel-1 (S1) constellation. The SAR backscattering signal is known to be sensitive to the soil water content [15][16][17][18][19][20], which makes it possible to detect the wetness information of the soil following an irrigation episode. The increase in the soil moisture caused by an irrigation episode is the key element that links the use of SAR data for irrigation mapping and the detection of irrigation events.…”

Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

“…To estimate the soil moisture within a homogenous vegetation context, several techniques have been employed to invert models, including direct inversion [44,49], change detection [29,60,61], Bayesian approaches [62], look-up tables [49,52] and artificial neural networks (ANNs) [23,[63][64][65][66][67].…”