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Evaluating the potential of Sentinel-1 images for the estimation of soil moisture on an alluvial Fan

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Abstract

We evaluate the potential of Sentinel-1A & 1B satellite images to estimate the volumetric soil moisture content over an alluvial fan of the Kosi River in the North Bihar, India. Over this region, only dual polarised images (VH and VV) are available. However, the existing backscattering models (i.e., Dubois, Oh and IEM models) uses quad polarised (VV, VH, HH and HV) images for the estimation of soil permittivity and surface roughness over the bare land. To overcome the constraint of dual polarised data, we eliminated one of the unknown (i.e. surface roughness) by developing a regression model between the in-situ measured surface roughness and the ratio of backscatter values (VH/VV) in dB. In a field campaign in the Kosi Fan from December 10-21, 2019, we have measured surface roughness, soil temperature, soil pH and soil moisture at 78 different location using the pin-meter, soil survey instrument (soil temperature and pH), and Time Domain Reflectometer (TDR) respectively. The average surface roughness and soil moisture vary between (0.61-5.45) cm and (0.12-0.53) m^3 /m^3 respectively in the study area. Further, using the surface roughness, we modify the Dubois, Oh and IEM models. This reduces the number of unknowns in the models from two to one; the soil permittivity. We compute the soil permittivity from the inversion of the existing backscattering models. Finally, we use the permittivity values in the Top's model to estimate the volumetric soil moisture in the study area. Our initial results exhibit a good correlation (R^2 = 0.85) to the in-situ measured soil moisture.
EGU2020-19614, updated on 10 Mar 2020
https://doi.org/10.5194/egusphere-egu2020-19614
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Evaluating the potential of Sentinel-1 images for the estimation of
soil moisture on an alluvial Fan
Abhilash Singh1, Kumar Gaurav1, and Shashi Kumar2
1Indian Institute of Science Education and Research Bhopal, India (sabhilash@iiserb.ac.in, kgaurav@iiserb.ac.in)
2Indian Institute of Remote Sensing, Dehradun (shashi@iirs.gov.in)
We evaluate the potential of Sentinel-1A & 1B satellite images to estimate the volumetric soil
moisture content over an alluvial fan of the Kosi River in the North Bihar, India. Over this region,
only dual polarised images (VH and VV) are available. However, the existing backscattering models
(i.e., Dubois, Oh and IEM models) uses quad polarised (VV, VH, HH and HV) images for the
estimation of soil permittivity and surface roughness over the bareland. To overcome the
constraint of dual polarised data, we eliminated one of the unknown (i.e. surface roughness) by
developing a regression model between the in-situ measured surface roughness and the ratio of
backscatter values (VH/VV) in dB. In a field campaign in the Kosi Fan from December 10-21, 2019,
we have measured surface roughness, soil temperature, soil pH and soil moisture at 78 different
location using the pin-meter, soil survey instrument (soil temperature and pH), and Time Domain
Reflectometer (TDR) respectively. The average surface roughness and soil moisture varies between
(0.61 - 5.45) cm and (0.12-0.53) m3/m3 respectively in the study area.
Further, using the surface roughness we modify the Dubois, Oh and IEM models. This reduces the
number of unknowns in the models from two to one; the soil permittivity. We compute the soil
permittivity from the inversion of the existing backscattering models. Finally, we use the
permittivity values in the Top’s model to estimate the volumetric soil moisture in the study area.
Our initial results exhibit a good correlation (R2 = 0.85) to the in-situ measured soil moisture.
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... The empirical models are simple because they need a minimum number of input parameters but have some limitations due to site-specific factors [41,42]. Moreover, machine learning methods, such as random forest (RF), support vector machine (SVM), and neural network, were employed for retrieving SSM over different crops [43,44]. Using in-situ measurements, most of those studies demonstrated that VV polarization was more effective than that of the VH for modeling SSM. ...
... The unknown surface roughness parameter (s) has been assigned as 1.8 cm, which is an average value of surface roughness (0.61-5.45 cm) based on in-situ measurements of 78 sample points over the Kosi fan in North Bihar [43,58]. The average roughness value was taken from the literature [43,58] which was measured using a one-dimensional pin-profiler in December 2019. ...
... where A is 10 −2.37 (cos 3 θ/sin 3 θ), B is 0.046 tan θ, and C is (k·s·sinθ) 1.1 λ 0.7 . The unknown surface roughness parameter (s) has been assigned as 1.8 cm, which is an average value of surface roughness (0.61-5.45 cm) based on in-situ measurements of 78 sample points over the Kosi fan in North Bihar [43,58]. The average roughness value was taken from the literature [43,58] which was measured using a one-dimensional pin-profiler in December 2019. ...
Article
Full-text available
Surface Soil Moisture (SSM) is a key factor for understanding the physical process between the land surface and atmosphere. With the advancement of Synthetic Aperture Radar (SAR) technology and backscattering models, retrieval of SSM over the land surface at higher spatial resolution became effective and accurate. This study examines the potential of C-band Sentinel-1 SAR data to derive SSM in a dry season (February 2020) over bare soil and vegetated agricultural fields in the Kosi River Basin (KRB) in North Bihar. Field campaigns were conducted simultaneously with Sentinel–1A acquisition date, and measurements comprised 54 in-situ sampling plots for the top of the soil (0–7.6 cm depth) using time-domain reflectometry (TDR–300). The modified Dubois model was employed to estimate relative soil permittivity from the backscatter values (σ°) of VV polarization. With the help of Topp’s model, volumetric SSM (m3/m3) was derived for all areas with normalized difference vegetation index (NDVI) less than 0.4 that majorly covered bare land or sparse vegetation. The key findings demonstrated that model-derived SSM was well correlated with the in-situ SSM with the coefficient of determination (R2) of 0.77 and root mean square error (RMSE) of 0.06 m3/m3. The spatial distribution of SSM ranged from 0.05 to 0.5 m3/m3 over the KRB, and the highest moisture was found in the Kosi Megafan. The modified Dubois model was effective in providing SSM from Sentinel–1A data in bare soil and agricultural fields and, thus, supporting use in hydrological, meteorological and crop planning applications.
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