Estimation of soil moisture through water cloud model using sentinel -1A SAR data

2019 ◽  
Author(s):  
Vijay Pratap Yadav ◽  
Rajendra Prasad ◽  
Ruchi Bala ◽  
Ajeet kumar Vishwakarma
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Water Cloud ◽  
Sar 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 Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 580
Author(s):  
Emna Ayari ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
Nicolas Baghdadi ◽  
Mehrez Zribi
Keyword(s):  
Soil Moisture ◽  
Drip Irrigation ◽  
Cloud Model ◽  
Bare Soil ◽  
Vegetation Fraction ◽  
Proposed Model ◽  
Heterogeneous Soil ◽  
Water Cloud ◽  
Sar Data ◽  
Underlying Soil

The objective of this paper was to estimate soil moisture in pepper crops with drip irrigation in a semi-arid area in the center of Tunisia using synthetic aperture radar (SAR) data. Within this context, the sensitivity of L-band (ALOS-2) in horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations and C-band (Sentinel-1) data in vertical-vertical (VV) and vertical-horizontal (VH) polarizations is examined as a function of soil moisture and vegetation properties using statistical correlations. SAR signals scattered by pepper-covered fields are simulated with a modified version of the water cloud model using L-HH and C-VV data. In spatially heterogeneous soil moisture cases, the total backscattering is the sum of the bare soil contribution weighted by the proportion of bare soil (one-cover fraction) and the vegetation fraction cover contribution. The vegetation fraction contribution is calculated as the volume scattering contribution of the vegetation and underlying soil components attenuated by the vegetation cover. The underlying soil is divided into irrigated and non-irrigated parts owing to the presence of drip irrigation, thus generating different levels of moisture underneath vegetation. Based on signal sensitivity results, the potential of L-HH data to retrieve soil moisture is demonstrated. L-HV data exhibit a higher potential to retrieve vegetation properties regarding a lower potential for soil moisture estimation. After calibration and validation of the proposed model, various simulations are performed to assess the model behavior patterns under different conditions of soil moisture and pepper biophysical properties. The results highlight the potential of the proposed model to simulate a radar signal over heterogeneous soil moisture fields using L-HH and C-VV data.

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 Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model

2020 ◽  
Vol 41 (14) ◽  
pp. 5503-5524 ◽  
Author(s):  
Dipankar Mandal ◽  
Vineet Kumar ◽  
Juan M. Lopez-Sanchez ◽  
Avik Bhattacharya ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Cloud Model ◽  
Biophysical Parameter ◽  
Water Cloud ◽  
Sar Data ◽  
Parameter Retrieval

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.

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