Modeling of soil organic matter using Sentinel-1 SAR and partial least squares (PLS) regression

The determination of soil properties, in addition to requiring great human effort, also involves a number of technical activities of high financial cost. Seeking to show that it is possible to reduce these high financial costs, this study presents a methodology that combines remote sensing technique...

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Bibliographic Details
Published in:Arabian journal of geosciences 2024, Vol.17 (1), Article 32
Main Authors: Duarte, Miqueias Lima, da Cunha e Silva, Darllan Collins, Barbosa, Ricardo Luís, Lourenço, Roberto Wagner
Format: Article
Language:English
Subjects:
Backscatter
Backscattering
Earth and Environmental Science
Earth science
Earth Sciences
Horizontal polarization
Land use
Least squares method
Mathematical models
Model accuracy
Modelling
Organic matter
Organic soils
Original Paper
Predictions
Regression analysis
Remote sensing
SAR (radar)
Soil moisture
Soil organic matter
Soil properties
Spatial analysis
Spatial data
Synthetic aperture radar
Vertical polarization
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Summary:The determination of soil properties, in addition to requiring great human effort, also involves a number of technical activities of high financial cost. Seeking to show that it is possible to reduce these high financial costs, this study presents a methodology that combines remote sensing techniques and mathematical modeling for estimating the concentration of soil organic matter (SOM). Technological advances have provided great improvements in equipment for capturing terrestrial information, allowing a greater availability of quality data for spatial analysis, which has brought about better estimates in the models. Thus, this study evaluated the capacity of the Sentinel-1 Synthetic Aperture Radar (SAR) satellite to determine the concentration of SOM in areas with different types of agricultural use in a hydrographic basin localted in the souutheastern region of the state of São Paulo, Brazil. The partial least squares regression method was used to build models for estimating the SOM, taking into account the SAR backscattering values and soil samples obtained in situ, which were collected at a depth of 0–10 cm. The results obtained indicated that the accuracy of the model adjusted based on backscattering values in the vertical/vertical (VV), vertical/horizontal (VH) polarizations, product of VHxVV and soil moisture presented a coefficient of determination of 0.502 with the SOM for independent data, at p
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-023-11844-w