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

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

Remote Sensing ◽

10.3390/rs12111844 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1844

Author(s):

Li Zhang ◽

Xiaolei Lv ◽

Qi Chen ◽

Guangcai Sun ◽

Jingchuan Yao

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cloud Model ◽

Optical Data ◽

Inversion Method ◽

Surface Soil Moisture ◽

Scattering Model ◽

Landsat 7 ◽

Water Cloud ◽

Underlying Soil

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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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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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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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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ESTIMATION OF SOIL MOISTURE AND EARTH’S SURFACE TEMPERATURE USING LANDSAT-8 SATELLITE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-327-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 327-330

Author(s):

M. Entezari ◽

A. Esmaeily ◽

S. Niazmardi

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Moisture Content ◽

Surface Temperature ◽

Soil Moisture Content ◽

Surface Soil ◽

Landsat 8 ◽

Surface Soil Moisture ◽

Surface Moisture ◽

Moisture Estimation

Abstract. Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the variability of soil characteristics, topography and vegetation in time and place. Soil moisture measurements are performed directly using in situ methods and indirect, by means of transfer functions or remote sensing. Since in-site measurements are usually costly and time-consuming in large areas, we can use methods such as remote sensing to estimate soil moisture at very large scales. The purpose of this study is to estimate soil moisture using surface temperature and vegetation indices for large areas. In this paper, ground temperature was calculated using Landsat-8 thermal band for Mashhad city and was used to estimate the soil moisture content of the study area. The results showed that urban areas had the highest temperature and less humidity at the time of imaging. For this purpose, using the LANDSAT 8 images, the indices were extracted and validated with soil moisture data. In this research, the study area was described and then, using the extracted indices, the estimated model was obtained. The results showed that there is a good correlation between surface soil moisture content with LST and NDVI indices (95%). The results of the verification of the soil moisture estimation model also showed that this model with a mean error of less than 0.001 can predict the surface moisture content, this small amount of error indicates the precision of the proposed model for estimating surface moisture.

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