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    An assessment of Sentinel 1 SAR, geophysical and topographical covariates for estimating topsoil particle size fractions

    Deodoro, Sandra Cristina and Moral, Rafael Andrade and Fealy, Reamonn and McCarthy, Tim and Fealy, Rowan (2023) An assessment of Sentinel 1 SAR, geophysical and topographical covariates for estimating topsoil particle size fractions. European Journal of Soil Science, 74. ISSN 1351-0754

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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.

    Item Type: Article
    Additional Information: We thank Teagasc (Ireland), GSI (Ireland), ESDAC (Europe)and ISRIC Foundation (The Netherlands), for providing ormaking available the required topsoil data to run the modelprediction. We also would like to thank Estev ̃ao Batistado Prado (Department of Mathematics and Statistics—Lan-caster University-UK) and Antonia Alessandra Lemos dosSantos (Department of Mathematics and Statistics—Hamil-ton Institute Maynooth University) for their assistance withR-codes. Open access funding provided by IReL
    Keywords: clay; compositional; linear regression; log-ratio transformations; random forest; sand; Sentinel 1; silt;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 17527
    Identification Number:
    Depositing User: Corinne Voces
    Date Deposited: 02 Oct 2023 09:05
    Journal or Publication Title: European Journal of Soil Science
    Publisher: Wiley Online
    Refereed: Yes
    Funders: This work is funded under the scholarship programme‘Johnand Pat Hume Doctoral Awards Scheme 2020—WISHAWARD’, from Maynooth University, Co. Kildare, Ireland.
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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