Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

Abstract

Abstract:In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover.

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

“…The Tunisian database, collected between 2009 and 2017, will not be described in the present study because it is well described in Baghdadi et al [26]. It is composed of SAR data acquired by ASAR and Sentinel-1 sensors, NDVI values derived from optical images (between 0.08 and 0.86), and in situ measurements of soil moisture (between 4.0 and 39.7 vol %) and surface roughness (between 0.7 and 4.6 cm) for winter crop fields and grasslands.…”

“…It is composed of SAR data acquired by ASAR and Sentinel-1 sensors, NDVI values derived from optical images (between 0.08 and 0.86), and in situ measurements of soil moisture (between 4.0 and 39.7 vol %) and surface roughness (between 0.7 and 4.6 cm) for winter crop fields and grasslands. This Tunisian database was used in Baghdadi et al [26] to calibrate the Water Cloud Model (WCM) [25] at the C-band. This parameterized water cloud model will be used in the present study for training the neural network, which will serve to estimate the soil moisture.…”

“…However, soil moisture estimates could be inaccurate for plots with well-developed vegetation cover because the vegetation contribution dominates the soil contribution in some cases of vegetation. Baghdadi et al [26] showed from the C-band data collected mainly on wheat and grasslands that the NDVI threshold from which the soil contribution dominates the vegetation contribution depends mainly on the soil moisture condition and the SAR parameters (polarization and incidence angle). This threshold on the NDVI increases with the increase of soil moisture or with the decrease of SAR incidence angle.…”

“…The direct vegetation contribution and the attenuation are computed using one or more vegetation descriptors. Several studies showed that the use of the NDVI (Normalized Differential Vegetation Index) as the only vegetation descriptor allows for computation of the vegetation effects on the total backscattered coefficients with good accuracy [26][27][28]. The parametrization of the WCM was provided in several studies for different SAR configurations and crop types [27,29].…”

“…The parametrization of the WCM was provided in several studies for different SAR configurations and crop types [27,29]. Recently, [26] parameterized the WCM for a radar signal in the C-band and for various crop types (grassland, wheat, and cereals). Using the WCM, several studies estimated the soil moisture from the C-and X-band SAR with an accuracy better than 8 vol % [27][28][29][30][31][32][33].…”