Soil Moisture in a Vegetation-Covered Area Using the Improved Water Cloud Model Based on Remote Sensing

2021 ◽  
Author(s):  
Junjie Lei ◽  
Wunian Yang ◽  
Xin Yang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Model Based ◽  
Covered Area ◽  
Water Cloud

scholarly journals Assessment of Water Cloud Model based on SAR and optical satellite data for surface soil moisture retrievals over agricultural area

2021 ◽  
Vol 10 (3) ◽  
pp. 243-250
Author(s):  
Rida KHELLOUK ◽  
Ahmed BARAKAT ◽  
Aafaf EL JAZOULİ ◽  
Hayat LİONBOUİ ◽  
Tarik BENABDELOUAHAB
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Surface Soil ◽  
Cloud Model ◽  
Agricultural Area ◽  
Surface Soil Moisture ◽  
Model Based ◽  
Water Cloud ◽  
Optical Satellite Data

scholarly journals Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1956 ◽  
Author(s):  
Minfeng Xing ◽  
Binbin He ◽  
Xiliang Ni ◽  
Jinfei Wang ◽  
Gangqiang An ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Cloud Model ◽  
Growing Season ◽  
Retrieval Algorithm ◽  
Surface Soil Moisture ◽  
Covered Area ◽  
Water Cloud ◽  
Sar Data ◽  
Underlying Soil

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.

scholarly journals ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

2016 ◽  
Vol XLI-B1 ◽  
pp. 453-460
Author(s):  
I. Hosni ◽  
L. Bennaceur Farah ◽  
M. S. Naceur ◽  
I. R. Farah
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cloud Model ◽  
Optical Data ◽  
Vegetation Coverage ◽  
Physical Parameters ◽  
Atmospheric Effects ◽  
Optical Remote Sensing ◽  
Statistical Parameters ◽  
The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. &lt;br&gt;&lt;br&gt; Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. &lt;br&gt;&lt;br&gt; Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. &lt;br&gt;&lt;br&gt; This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. &lt;br&gt;&lt;br&gt; Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

scholarly journals Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

2022 ◽  
Vol 14 (2) ◽  
pp. 404
Author(s):  
Yaqing Gou ◽  
Casey M. Ryan ◽  
Johannes Reiche
Keyword(s):  
Soil Moisture ◽  
Aboveground Biomass ◽  
Cloud Model ◽  
Large Area ◽  
Moisture Effects ◽  
Radar Imagery ◽  
Water Cloud ◽  
L Band ◽  
The Difference ◽  
Moisture Conditions

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

scholarly journals Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

2018 ◽  
Vol 10 (9) ◽  
pp. 1370 ◽  
Author(s):  
Junhua Li ◽  
Shusen Wang
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Cloud Model ◽  
Data Availability ◽  
Agricultural Crop ◽  
Sar Images ◽  
Area Index ◽  
Water Cloud ◽  
The Impact

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

scholarly journals Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 135
Author(s):  
Min Zhang ◽  
Fengkai Lang ◽  
Nanshan Zheng
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Satellite Data ◽  
Cloud Model ◽  
Growth Cycle ◽  
Wheat Growth ◽  
Vegetation Fraction ◽  
Effective Roughness ◽  
Water Cloud ◽  
Optical Satellite Data

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

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