Estimation of Surface Moisture Content using Sentinel-1 C-band SAR Data Through Machine Learning Models
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RESEARCH ARTICLE
Estimation of Surface Moisture Content using Sentinel-1 C-band SAR Data Through Machine Learning Models Subhadip Datta1 • Pulakesh Das1
•
Dipanwita Dutta1 • Rakesh Kr. Giri1
Received: 14 April 2020 / Accepted: 8 November 2020 Ó Indian Society of Remote Sensing 2020
Abstract Monitoring the spatio-temporal variation in soil moisture content (SMC) of the surface soil layer is essential for agriculture and water resource management activities, especially in regions where the socio-economic condition and livelihood depend upon agriculture and allied sectors. In the present study, we have compared different machine learning (ML) and linear regression models to estimate the SMC integrating field observed soil moisture and Sentinel-1 SAR data. Total 56 soil samples were collected from the surface soil layer (* 5 cm) in correspondence with the passing date of the Sentinel-1 sensor over the study area. The surface SMC was estimated for bare soil areas, which was extracted by applying the threshold values on vegetation and water index maps derived from the Sentinel-2 multispectral data. The univariate linear regression with the co-polarized VV band provided higher accuracy compared to the cross-polarized VH band. However, the multiple linear regression with VV and VH bands indicated similar accuracy as obtained by the VV band alone. The random forest model was observed as the best performing ML model for soil moisture estimation (R2 = 0.87 and 0.93 during modeling and validation, respectively; RMSE: * 0.03). The obtained results indicate well accurate surface soil moisture verified with in-situ information collected during the dry rabi crop season (January to March 2019). The maximum SMC was observed for March, followed by February and January, that corroborated with the total monthly precipitation and irrigation activities. The study highlights the potentiality of ML models and Sentinel-1 SAR data for soil moisture estimation, which is useful for policy-level implications and decision making in agriculture and water resource management activities. Keywords Soil moisture Sentinel-1 Random forest Regression
Introduction Soil moisture content (SMC) represents the water available in the pore spaces of surface (up to 10 cm) and subsurface (approx. 200 cm) soil layers, which plays a vital role in hydro-botanical studies. It partially controls the water and energy exchange between the land surface and atmosphere, facilitating water availability for plant photosynthesis. Monitoring SMC has multidimensional use in irrigation planning, assessing drought conditions, crop yield, ecology, primary productivity, groundwater analysis, atmospheric processes, rainfall prediction, climate change & Pulakesh Das [email protected] 1
Department of Remote Sensing and GIS, Vidyasagar University, Midnapore 721102, India
studies, etc. Estimating the spatio-temporal SMC from ground sampled data is time-consuming and labor-intensive (Kumar et al. 2016; Petropoulos et al. 2013). However, recent advancement in satellit
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