Soil Moisture Mapping Using Sentinel-1A Synthetic Aperture Radar Data

Şekertekin, Aliihsan; Marangoz, Aycan Murat; Abdikan, Saygın

doi:10.30897/ijegeo.425606

Cited by 34 publications

(18 citation statements)

References 30 publications

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“…The statistical relation derived from the regression analysis to type. In [43], bare soil data with no vegetation have been used to propose a generalized model. The accuracy assessment has been conducted using 7 test sites, and the statistical metrics, namely R 2 and RMSE, were determined as 0.84 and 0.0246, respectively.…”

Section: Resultsmentioning

confidence: 99%

A Statistical Model for Estimation of Soil Moisture in Paddy Field Using Microwave Satellite Data

Pari¹,

Packirisamy²,

Ramalingam³

et al. 2020

PIER M

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Estimation of soil moisture using Synthetic Aperture Radar (SAR) backscatter values, over agricultural area, is still difficult. SAR backscatter is sensitive to the surface properties like roughness, crop cover, and soil type, along with its strong sensitivity to soil moisture. Hence, to develop a methodology for agricultural area soil moisture estimation using SAR, it is necessary to incorporate the effects of crop cover and soil texture in the soil moisture retrieval model. A field experiment was conducted by the authors and used along with Sentinel 1A SAR data to estimate the soil moisture in the paddy agricultural fields. Generally, the water used for irrigation in the study region was obtained from ground water. As in the hot climate conditions ground water level would be reduced, and the water for irrigation must be supplied optimally. Hence, available soil moisture in the field was estimated from SAR data on the day of satellite passing the crop fields and utilized for deciding the amount of water to be supplied. The soil moisture values of soil samples that are collected from the agricultural field are calculated with the laboratory experiments. A soil moisture retrieval model is derived and proposed in this paper after a comparative analysis of experimental soil moisture values and satellite values. The feasibility of above model for paddy agricultural fields is validated using the field measurements.

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Section: Resultsmentioning

confidence: 99%

A Statistical Model for Estimation of Soil Moisture in Paddy Field Using Microwave Satellite Data

Pari¹,

Packirisamy²,

Ramalingam³

et al. 2020

PIER M

View full text Add to dashboard Cite

show abstract

“…Estimation of soil moisture involved the testing of empirical models to present a correlation between measured soil moisture and predicted soil moisture derived from environmental covariates and backscattering coefficients with the aid of the ordinary least square (OLS) linear regression function [45]. Many researchers established empirical relationships to estimate soil moisture, and these models revealed that there is a fundamental relation between soil moisture and the backscattering coefficient [45][46][47][48][49].…”

Section: Estimation Of Soil Moisturementioning

confidence: 99%

Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Kibirige

Dobos

2020

Water

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Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

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“…Over the past four decades, several studies have shown the great potential of synthetic aperture radar (SAR) data for SSM mapping at an agricultural plot scale with high revisit time [5,[9][10][11][12]. Even though SAR data in the X-, C-and L-bands have been widely used in numerous research projects [13][14][15][16][17][18], only SAR data in the C-band are used today in operational approaches for SSM retrieving.…”

Section: Introductionmentioning

confidence: 99%

“…Figure12. Calculated error on the retrieved SSM using C-band real SAR data versus in situ measurements.…”

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confidence: 99%

Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

et al. 2021

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Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.

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Soil Moisture Mapping Using Sentinel-1A Synthetic Aperture Radar Data

Cited by 34 publications

References 30 publications

A Statistical Model for Estimation of Soil Moisture in Paddy Field Using Microwave Satellite Data

A Statistical Model for Estimation of Soil Moisture in Paddy Field Using Microwave Satellite Data

Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

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