@article{fdi:010075548,
  title = {{U}se of {S}entinel-2 time-series images for classification and uncertainty analysis of inherent biophysical property : case of soil texture mapping},
  author = {{G}omez, {C}{\'e}cile and {D}harumarajan, {S}. and {F}eret, {J}. {B}. and {L}agacherie, {P}. and {R}uiz, {L}aurent and {S}ekhar, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {S}entinel-2 mission of the {E}uropean {S}pace {A}gency ({ESA}) {C}opernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. {T}he objective of this work is to evaluate the ability of {S}entinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. {T}he tested inherent biophysical property was the soil texture. {S}oil texture classification was performed on each individual {S}entinel-2 image with a linear support vector machine. {T}wo sources of uncertainty were studied: uncertainties due to the {S}entinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. {T}he first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. {T}he second uncertainty analysis was achieved from each individual {S}entinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. {T}he {S}impson index was used to compute this diversity in the classification results. {T}his work was carried out in an {I}ndian cultivated region (84 km(2), part of {B}erambadi catchment, in the {K}arnataka state). {I}t used a time-series of six {S}entinel-2 images acquired from {F}ebruary to {A}pril 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. {T}he classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. {T}he uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. {T}hese results illustrate the potential of {S}entinel-2 for providing input for modeling environmental processes and crop management.},
  keywords = {time-series ; {S}entinel-2 ; soil texture ; classification ; uncertainty ; {S}impson index ; bootstrap ; {INDE} ;  ; {DECCAN} {PLATEAU}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {11},
  numero = {5},
  pages = {art. 565 [20 ]},
  ISSN = {2072-4292},
  year = {2019},
  DOI = {10.3390/rs11050565},
  URL = {https://www.documentation.ird.fr/hor/fdi:010075548},
}