Sensitivity of spaceborne radar to near-surface soil moisture in grasslands across southern Ireland

Barrett, Brian; Dwyer, Edward; Whelan, Pádraig M.

doi:10.1080/00750778.2012.758434

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…Cross-polarization is more sensitive to vegetation, as it captures vegetation response (backscattering) better than VV polarization. Barrett et al (2007), in a study to derive relative changes in soil moisture in the near-surface (0-5 cm) over an agricultural area in southern Ireland, observed that the growth of vegetation (grass) could cause attenuation of the radar signal (i.e., depolarisation effect due to volumetric scattering contribution). Pratola et al (2014) in a similar study, also carried out in southern Ireland, found that in winter, the distribution of soil moisture is rather homogenous over the SAR pixels, whereas a higher spatial variability was observed in summer.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

An assessment of Sentinel‐1 synthetic aperture radar, geophysical and topographical covariates for estimating topsoil particle‐size fractions

Deodoro,

Moral,

Fealy

et al. 2023

European J Soil Science

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Data derived from Synthetic Aperture Radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches – linear (with and without interaction terms) and tree‐based models, namely compositional linear regression model (LRM) and Random Forest (RF) – and both non‐geophysical (e.g. surface soil moisture, topographic etc) and geophysical‐based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel 1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave‐one‐out cross validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash‐Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt, and clay of 0.94, 0.62, and 0.46, respectively; for RF, the NSE values were 0.93, 0.59, and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar derived variables (e.g. VV, VH, RVI) alone were not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g. ESA Sentinel‐1) for predicting topsoil particle‐size, we intend to test surface scattering information derived from the dual‐polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.This article is protected by copyright. All rights reserved.

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Section: Theoretical Backgroundmentioning

confidence: 99%