scholarly journals Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands

2020 ◽  
pp. 1-20
Author(s):  
Zhen Wang ◽  
Tianjie Zhao ◽  
Jianxiu Qiu ◽  
Xuesheng Zhao ◽  
Rui Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Water Cloud ◽  
L Band

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.

Soil moisture estimation over cereals fields using l-band alos2 data (merguellil case – KAIROUAN)

2021 ◽  
Author(s):  
Emna Ayari ◽  
Zeineb Kassouk ◽  
Zohra Lili Chabaane ◽  
Safa Bousbih ◽  
Mehrez Zribi
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Radar Data ◽  
Radar Signal ◽  
Biophysical Parameters ◽  
Vegetation Development ◽  
L Band ◽  
Cereal Fields ◽  
Bare Soils ◽  
Band Signal

<p>Soil moisture is a key component for water resources management especially for irrigation needs estimation. We analyze in the present study, the potential of L-band data, acquired by (Advanced Land Observing Satellite-2) ALOS-2, to retrieve soil moisture over bare soils and cereal fields located in semi-arid area in the Kairouan plain.</p><p>In this context, we evaluate radar signal sensitivity to roughness, soil moisture and vegetation biophysical parameters. Based on multi-incidence radar data (28°, 32.5° and 36°), high correlations characterize relationships between backscattering coefficients in dual-polarization (HH and HV) and root mean square of heights (Hrms) and Zs, parameters, Sensitivity of radar data to soil moisture was discussed for three classes of NDVI (less than 0.25 for bare soils and dispersed vegetation, between 0.25 and 0.5 for medium vegetation and greater than 0.5 for dense cereals). With vegetation development, where NDVI values are higher than 0.25, SAR signal remains sensitive to soil moisture in HH pol. This sensitivity to moisture disappears, in HV pol for dense vegetation. For covered fields, L-band signal is very sensitive to Vegetation Water Content (VWC), with R² values ranging between 0.76 and 0.61 in HH and HV polarization respectively.</p><p>Simulating signal behavior is carried out through various models over bare soils and covered cereal fields. Over bare soils, proposed empirical expressions, modified versions of Integral Equation Model (IEM-B) and Dubois models (Dubois-B) are evaluated, generally for HH and HV polarizations. Best consistency is observed between real data and IEM-B backscattering simulations in HH polarization. More discrepancies between real and modelled data are observed in HV polarization.</p><p>Furthermore, to simulate L-band signal behavior over covered fields, the inversion of Water Cloud Model (WCM) coupled to different bare soil models is realized through direct equations and Look-up tables. Two options of WCM, are tested (with and without soil-vegetation interaction scattering term). For the first option, results highlight the good performance of IEM-B coupled to WCM in HH polarization with RMSE value between estimated and in situ moisture measurements equal to 4.87 vol.%. By adding soil – cereal interaction term in the second option of WCM, results reveal a stable accuracy in HH polarization and an important improvement of soil moisture estimations in HV polarization, with RMSE values are ranging between 6 and 7 vol.%.</p>

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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