@article{fdi:010090984,
  title = {{V}isible and infrared lab spectroscopy for soil texture classification : analysis of entire spectra v/s reduced spectra},
  author = {{T}ernikar, {C}. {R}. and {G}omez, {C}{\'e}cile and {N}agesh {K}umar, {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil, directly or indirectly, impacts seven of the {S}ustainable {D}evelopment {G}oals set by the {U}nited {N}ations. {S}oil texture is a fundamental physical property that controls other properties ranging from soil stability, erodibility, compaction to water holding capacity, nutrient availability and fertility. {W}hile {V}isible {N}ear-{I}nfrared and {S}hortwave-{I}nfrared ({VNIR}/{SWIR}) laboratory spectroscopy has been already used for soil texture classification, {M}id {I}nfrared ({MIR}) region and unison of the {VNIR}/{SWIR} and {MIR} region warrant quantification. {T}hus, 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. {T}his study utilized an {ICRAF}-{ISRIC} global soil spectral library consisting of 3643 {VNIR}/{SWIR}, {MIR} soil spectra and the {USDA} soil textural classes. {T}wo 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 {P}artial {I}nformation {C}orrelation ({PIC}) selected bands ({VNIR}/{SWIR}_{PIC}, {MIR}_{PIC}, {VNIR} + {MIR}_{PIC}). {T}he mean confusion matrix, overall accuracy and kappa were calculated to evaluate classifiers' performances over 100 iterations. {T}wo additional measures were proposed for partitioning inaccuracies between neighbouring and far classes: {C}orrect-{N}eighbouring-{F}ar classes distribution matrix and added neighbourhood accuracy. {T}his 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. {F}inally, the textural classes with poor classification performance had low areal and sample representation. {T}hese 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.},
  keywords = {},
  booktitle = {},
  journal = {{R}emote {S}ensing {A}pplications : {S}ociety and {E}nvironment},
  volume = {35},
  numero = {},
  pages = {101242 [21 ]},
  ISSN = {2352-9385},
  year = {2024},
  DOI = {10.1016/j.rsase.2024.101242},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090984},
}