%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Ternikar, C. R.
%A Gomez, Cécile
%A Nagesh Kumar, D.
%T Visible and infrared lab spectroscopy for soil texture classification : analysis of entire spectra v/s reduced spectra
%D 2024
%L fdi:010090984
%G ENG
%J Remote Sensing Applications : Society and Environment
%@ 2352-9385
%M ISI:001325479200001
%P 101242 [21 ]
%R 10.1016/j.rsase.2024.101242
%U https://www.documentation.ird.fr/hor/fdi:010090984
%> https://www.documentation.ird.fr/intranet/publi/2024-08/010090984.pdf
%V 35
%W Horizon (IRD)
%X 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/SWIR_PIC, MIR_PIC, VNIR + MIR_PIC). 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.
%$ 068ANASOL