A synergistic approach for soil moisture estimation using modified Dubois model with dual-polarized SAR and optical satellite data

Thanabalan, P.; Vidhya, R.

doi:10.1117/12.2223751

Cited by 2 publications

(1 citation statement)

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“…Sentinel-1A satellite sensor operates in C-band at frequency 5.405 GHz and acquire information about the earth's surface in selectable single (HH or VV) and dual polarisation (HH + HV, VV + VH) [40]. This data is freely available and is widely used in various applications, including soil moisture estimation [34,38,[41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

et al. 2020

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Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.

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