Estimation of Surface Soil Moisture during Corn Growth Stage from SAR and Optical Data Using a Combined Scattering Model

As an indispensable ecological parameter, surface soil moisture (SSM) is of great significance for understanding the growth status of vegetation. The cooperative use of synthetic aperture radar (SAR) and optical data has the advantage of considering both vegetation and underlying soil scattering information, which is suitable for SSM monitoring of vegetation areas. The main purpose of this paper is to establish an inversion approach using Terra-SAR and Landsat-7 data to estimate SSM at three different stages of corn growth in the irrigated area. A combined scattering model that can adequately represent the scattering characteristics of the vegetation coverage area is proposed by modifying the water cloud model (WCM) to reduce the effect of vegetation on the total SAR backscattering. The backscattering from the underlying soil is expressed by an empirical model with good performance in X-band. The modified water cloud model (MWCM) as a function of normalized differential vegetation index (NDVI) considers the contribution of vegetation to the backscattering signal. An inversion technique based on artificial neural network (ANN) is used to invert the combined scattering model for SSM estimation. The inversion method is established and verified using datasets of three different growth stages of corn. Using the proposed method, we estimate the SSM with a correlation coefficient R ≥ 0.72 and root-mean-square error R M S E ≤ 0.043 cm 3 /cm 3 at the emergence stage, with R ≥ 0.87 and R M S E ≤ 0.046 cm 3 /cm 3 at the trefoil stage and with R ≥ 0.70 and R M S E ≤ 0.064 cm 3 /cm 3 at the jointing stage. The results suggest that the method proposed in this paper has operational potential in estimating SSM from Terra-SAR and Landsat-7 data at different stages of early corn growth.

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

Remote Sensing ◽

10.3390/rs11161956 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1956 ◽

Cited By ~ 9

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.

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Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.05.026 ◽

2018 ◽

Vol 72 ◽

pp. 76-85 ◽

Cited By ~ 30

Author(s):

Yansong Bao ◽

Libin Lin ◽

Shanyu Wu ◽

Khidir Abdalla Kwal Deng ◽

George P. Petropoulos

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cloud Model ◽

Landsat 8 ◽

Surface Soil Moisture ◽

Water Cloud

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Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

10.5194/egusphere-egu2020-7408 ◽

2020 ◽

Author(s):

Nadia Ouaadi ◽

Lionel Jarlan ◽

Jamal Ezzahar ◽

Saïd Khabba ◽

Mehrez Zribi ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Resolution ◽

Surface Soil ◽

Cloud Model ◽

Microwave Remote Sensing ◽

Radar Data ◽

Optical Data ◽

Surface Soil Moisture ◽

Backscattering Coefficient ◽

New Approach

High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2&#176; for VV, with R=0.82, RMSE=0.05m3/m3 and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m3/m3 and 0.03 m3/m3, respectively even that the incidence angle is higher (40&#176;). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.

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First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

Remote Sensing ◽

10.3390/rs9070714 ◽

2017 ◽

Vol 9 (7) ◽

pp. 714 ◽

Cited By ~ 34

Author(s):

Xiaojing Bai ◽

Binbin He ◽

Xing Li ◽

Jiangyuan Zeng ◽

Xin Wang ◽

...

Keyword(s):

Integral Equation ◽

Soil Moisture ◽

Tibetan Plateau ◽

Surface Soil ◽

Cloud Model ◽

Equation Model ◽

The Tibetan Plateau ◽

Surface Soil Moisture ◽

Water Cloud

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Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Water ◽

10.3390/w13141981 ◽

2021 ◽

Vol 13 (14) ◽

pp. 1981

Author(s):

Yan Li ◽

Chengcai Zhang ◽

Weidong Heng

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cloud Model ◽

Vegetation Indices ◽

Multiple Linear Regression Model ◽

Optical Data ◽

Support Vector ◽

Surface Soil Moisture ◽

Vegetation Fraction ◽

Sentinel 2

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

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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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Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

Water ◽

10.3390/w12030866 ◽

2020 ◽

Vol 12 (3) ◽

pp. 866 ◽

Cited By ~ 5

Author(s):

Myriam Foucras ◽

Mehrez Zribi ◽

Clément Albergel ◽

Nicolas Baghdadi ◽

Jean-Christophe Calvet ◽

...

Keyword(s):

Soil Moisture ◽

Optical Sensors ◽

Surface Soil ◽

Remote Sensing Data ◽

Climatic Conditions ◽

Optical Data ◽

Surface Soil Moisture ◽

Moisture Index ◽

Wide Range ◽

Moderate Resolution Imaging Spectroradiometer

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

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Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

Remote Sensing ◽

10.3390/rs12010183 ◽

2020 ◽

Vol 12 (1) ◽

pp. 183 ◽

Cited By ~ 2

Author(s):

Chenyang Xu ◽

John J. Qu ◽

Xianjun Hao ◽

Di Wu

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Water Content ◽

Vegetation Index ◽

Surface Soil ◽

Cloud Model ◽

Incidence Angle ◽

Permafrost Region ◽

Surface Soil Moisture ◽

The Impact

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

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