Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and 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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Assessment of Water Cloud Model based on SAR and optical satellite data for surface soil moisture retrievals over agricultural area

EURASIAN JOURNAL OF SOIL SCIENCE (EJSS) ◽

10.18393/ejss.926813 ◽

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

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Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data

Geocarto International ◽

10.1080/10106049.2019.1624984 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Vijay Pratap Yadav ◽

Rajendra Prasad ◽

Ruchi Bala

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Satellite Data ◽

Cloud Model ◽

Wheat Crop ◽

Area Index ◽

Water Cloud ◽

Optical Satellite Data ◽

Index Estimation

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ESTIMATION OF BIOPHYSICAL PARAMETERS OF WHEAT CROP THROUGH MODIFIED WATER CLOUD MODEL USING SATELLITE DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-239-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 239-244 ◽

Cited By ~ 4

Author(s):

V. P. Yadav ◽

R. Prasad ◽

R. Bala ◽

A. K. Vishwakarma ◽

S. A. Yadav

Keyword(s):

Satellite Data ◽

Cloud Model ◽

Parameters Estimation ◽

Optical Data ◽

Wheat Crop ◽

Landsat 8 ◽

Biophysical Parameters ◽

Area Index ◽

Vegetation Fraction ◽

Water Cloud

Abstract. A modified water cloud model (WCM) was used to estimate the biophysical parameters of wheat crop using Sentinel-1A and Landsat-8 satellite images. The approach of combining the potential of SAR and optical data provided a new technique for the estimation of biophysical parameters of wheat crop. The biophysical parameters estimation was done using non-linear least squares optimization technique by minimizing the cost function between the backscattering coefficients (&sigma;0) computed from the Sentinel-1A image and simulated by the modified WCM followed by look up table algorithm(LUT). The modified WCM integrates the full account of backscattering response on vegetation and bare soil by adding vegetation fraction. The modified WCM was found more sensitive than the original WCM because of incorporation of vegetation fraction (fveg) derived from the Landsat-8 satellite data. The estimated values of leaf area index (LAI) by modified WCM at VV polarization shows good correlation (R2=83.08% and RMSE=0.502m2/m2) with the observed values. Whereas, leaf water area index (LWAI) shows comparatively poor correspondence (R2=76% and RMSE=0.560m2/m2) with the observed data in comparison to LAI estimation at VV polarization. The performance indices show that the modified WCM was found more accurate for the estimation of wheat crop parameters during the whole growth season in Varanasi district, India. Thus, the modified WCM shows significant potential for the accurate estimation of LAI and LWAI of wheat crop on incorporating both SAR and optical satellite data.

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Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

Remote Sensing ◽

10.3390/rs12142303 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2303 ◽

Cited By ~ 1

Author(s):

Chunfeng Ma ◽

Xin Li ◽

Matthew F. McCabe

Keyword(s):

Surface Roughness ◽

Soil Moisture ◽

High Resolution ◽

Water Content ◽

Cloud Model ◽

Cosmic Ray ◽

Vegetation Water Content ◽

Water Cloud ◽

Vegetation Water ◽

Sentinel 2

Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale.

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Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields

Remote Sensing ◽

10.3390/rs13193889 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3889

Author(s):

Chunfeng Ma ◽

Shuguo Wang ◽

Zebin Zhao ◽

Hanqing Ma

Keyword(s):

Sensitivity Analysis ◽

Surface Roughness ◽

Soil Moisture ◽

Cloud Model ◽

Incidence Angle ◽

Agricultural Field ◽

Estimation Of Parameters ◽

Spatiotemporal Resolution ◽

Water Cloud ◽

Vegetation Parameters

The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.

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Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

Sensors ◽

10.3390/s22020580 ◽

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.

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A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

Remote Sensing ◽

10.3390/rs13071348 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1348

Author(s):

Mehdi Hosseini ◽

Heather McNairn ◽

Scott Mitchell ◽

Laura Dingle Robertson ◽

Andrew Davidson ◽

...

Keyword(s):

Support Vector Machine ◽

Soil Moisture ◽

Leaf Area Index ◽

Satellite Data ◽

Cloud Model ◽

Support Vector ◽

Area Index ◽

Svm Model ◽

Water Cloud

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.

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