Ternikar, CR and Gomez, C and Nagesh Kumar, D (2024) Visible and infrared lab spectroscopy for soil texture classification: Analysis of entire spectra v/s reduced spectra. In: Remote Sensing Applications: Society and Environment, 35 .
![]() |
PDF
Rem_Sen_App_Soc_Env_35_2024_ 01242.pdf - Published Version Restricted to Registered users only Download (5MB) | Request a copy |
Abstract
Soil, directly or indirectly, impacts seven of the Sustainable Development Goals set by the United Nations. Soil texture is a fundamental physical property that controls other properties ranging from soil stability, erodibility, compaction to water holding capacity, nutrient availability and fertility. While Visible Near-Infrared and Shortwave-Infrared (VNIR/SWIR) laboratory spectroscopy has been already used for soil texture classification, Mid Infrared (MIR) region and unison of the VNIR/SWIR and MIR region warrant quantification. Thus, the objectives of this work were: (1) to investigate the critical spectral regions for soil texture classification, (2) to compare the use of entire spectral bands v/s subset of spectral bands obtained via the feature selection method, and (3) to quantify the degree of misclassification in the neighbouring classes for a given textural class. This study utilized an ICRAF�ISRIC global soil spectral library consisting of 3643 VNIR/SWIR, MIR soil spectra and the USDA soil textural classes. Two classifiers were used for soil texture classification using the three databases (VNIR/SWIR, MIR, VNIR + MIR) with entire spectral bands and three databases with only the Partial Information Correlation (PIC) selected bands (VNIR/SWIRPIC, MIRPIC, VNIR + MIRPIC). The mean confusion matrix, overall accuracy and kappa were calculated to evaluate classifiers' performances over 100 iterations. Two additional measures were proposed for partitioning inaccuracies between neighbouring and far classes: Correct-Neighbouring-Far classes distribution matrix and added neighbourhood accuracy. This work highlighted that: (1) the combined VNIR/SWIR + MIR region provided the best texture classification, followed by MIR and then VNIR/SWIR region, (2) the use of PIC selected bands provided lower classification performances when compared to using all bands but a massive reduction in the number of bands allowing to reduce model complexity, (3) the misclassifications were predominantly in the neighbouring classes rather than far classes, for a given texture class. Finally, the textural classes with poor classification performance had low areal and sample representation. These insights may launch a number of soil texture classification studies worldwide based on global soil spectral databases, further helping map soils and the associated ecosystem services it provides. © 2024 Elsevier B.V.
Item Type: | Journal Article |
---|---|
Publication: | Remote Sensing Applications: Society and Environment |
Publisher: | Elsevier B.V. |
Additional Information: | The copyright for this article belongs to Elsevier B.V. |
Department/Centre: | Others |
Date Deposited: | 28 Jul 2024 16:54 |
Last Modified: | 28 Jul 2024 16:54 |
URI: | http://eprints.iisc.ac.in/id/eprint/85172 |
Actions (login required)
![]() |
View Item |