George, EB and Gomez, C and Kumar, ND (2024) Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data. In: Remote Sensing, 16 (6).
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Abstract
The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction ((Formula presented.) ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model�s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4 for pixels with bare soil fractional cover >0.7 to 52.7 for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions. © 2024 by the authors.
| Item Type: | Journal Article |
|---|---|
| Publication: | Remote Sensing |
| Publisher: | Multidisciplinary Digital Publishing Institute (MDPI) |
| Additional Information: | The copyright for this article belongs to authors. |
| Keywords: | Forecasting; Hyperspectral imaging; Photomapping; Regression analysis; Remote sensing; Soil surveys; Soils; Uncertainty analysis, 'current; Airborne Visible Infrared/Imaging Spectrometers; Bare soils; Clay content; Composite maps; Digital soil mappings; Fractional cover; Regression modelling; Soil property; Uncertainty, Pixels |
| Department/Centre: | Division of Mechanical Sciences > Divecha Centre for Climate Change Division of Mechanical Sciences > Civil Engineering Others |
| Date Deposited: | 19 Jul 2024 10:02 |
| Last Modified: | 19 Jul 2024 10:02 |
| URI: | http://eprints.iisc.ac.in/id/eprint/84827 |
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