An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data

Yadav, Vijay Pratap; Prasad, Rajendra; Bala, Ruchi; Vishwakarma, Ajeet Kumar

doi:10.1016/j.compag.2020.105447

Cited by 38 publications

(15 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies have shown the appreciable capability of C-band Sentinel-1 SAR data in estimating soil moisture and vegetation parameters. For example, Vijay et al [12] retrieved soil moisture through a modified water cloud model (WCM) and an improved inversion algorithm. Kumar et al [9] estimated winter wheat crop growth parameters using time series of Sentinel-1A SAR data.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

et al. 2021

View full text Add to dashboard Cite

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R2 and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m3/m3, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.

show abstract

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Meanwhile, soil moisture is an important index for early warning of crop drought and flood disasters and an important parameter for evaluating crop growth. Therefore, the estimation and description of the temporal and spatial dynamics of soil moisture are of great significance for hydrology, ecology and agriculture [1][2][3][4][5]. With the development of satellite remote sensing technology, it has become possible to obtain regional soil moisture information.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, many semi-empirical soil moisture inversion models (Oh [16][17][18], Dubois [19], Chen [20], Shi [21], etc. ), as well as promising machine learning methods such as artificial neural network (ANN), random forest regression (RFR) and support vector machine (SVM) have also been proposed [3,4,7,8,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the vegetation layer as a homogeneous scatterer and ignoring the cross-scattering term between soil and vegetation, the WCM has better performance in dense vegetation areas than sparse or complex vegetation areas. To handle this issue, some researchers have attempted to introduce additional vegetation information, such as vegetation fraction ( f veg ) parameters, provided by optical remote sensing [4,44]. The contribution of vegetation and bare soil at the pixel level is calculated on the basis of vegetation fraction.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the continuous progress in surface soil moisture retrieval, it is still a tedious task to retrieve soil moisture by microwave remote sensing under different vegetation conditions (sparse and dense vegetation) due to the influence of vegetation volume scattering, underlying soil surface scattering and cross-scattering between vegetation and soil [4]. In conclusion, it is worth further studying how to improve the accuracy of soil moisture retrieval in crop coverage areas and propose a highly accurate soil moisture remote sensing retrieval approach that can be applied to different stages of crop growth, so as to realize real-time quantitative monitoring of soil moisture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Lang

Zheng

2021

Water

View full text Add to dashboard Cite

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.

show abstract

Surface Soil Moisture Retrieval Over Partially Vegetated Areas from the Remote Sensing Data Using a Modified Water Cloud Model

Krishnankutty,

Sathish Kumar

2022

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data

Cited by 38 publications

References 43 publications

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

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

Surface Soil Moisture Retrieval Over Partially Vegetated Areas from the Remote Sensing Data Using a Modified Water Cloud Model

Contact Info

Product

Resources

About