@article{fdi:010082091,
  title = {{M}apping of tank silt application using {S}entinel-2 images over the {B}erambadi catchment ({I}ndia)},
  author = {{G}omez, {C}{\'e}cile and {D}harumarajan, {S}. and {L}agacherie, {P}. and {R}iotte, {J}ean and {F}errant, {S}ylvain and {S}ekhar, {M}. and {R}uiz, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{M}apping soil properties is becoming more and more challenging due to the increase in anthropogenic modifica-tion of the landscape, calling for new methods to identify these changes. {A} striking example of anthropogenic modifications of soil properties is the widespread practice in {S}outh {I}ndia of applying large quantities of silt from dry river dams (or & ldquo;tanks & rdquo;) to agricultural fields. {W}hereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. {O}ver {S}outh-{I}ndian pedological context, this practice is characterized by an application of black-colored tank silt to red-colored soils such as {F}erralsols. {T}he objective of this work was to evaluate the usefulness of {S}entinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application. {W}e used data collected in a cultivated watershed in {S}outh {I}ndia including 217 soil surface samples characterized in terms of {M}unsell color. {W}e used two {S}entinel-2 images acquired on {F}ebruary and {A}pril 2017. {T}he surface soil color over each {S}entinel-2 image was classified into two soil types ("{B}lack" and "{R}ed" soils). {A} change of soil color from "{R}ed" in {F}ebruary 2017 to "{B}lack" in {A}pril 2017 was attributed to tank silt application. {S}oil color changes were analyzed accounting for possible surface soil moisture changes. {T}he pro-posed methodology was based on a well-balanced {C}alibration data created from the initial imbalanced {C}alibra-tion dataset thanks to the {S}ynthetic {M}inority {O}ver-sampling {T}echnique ({SMOTE}) methodology, coupled to the {C}ost-{S}ensitive {C}lassification {A}nd {R}egression {T}rees ({C}ost-{S}ensitive {CART}) algorithm. {T}o estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from "{R}ed" to "{B}lack" a bootstrap pro-cedure was used providing fifty two-class classifications for each {S}entinel-2 image. {T}he results showed that 1) the {CART} method allowed to classify the "{R}ed" and "{B}lack" soil with correct overall accuracy from both {S}entinel-2 images, 2) the tank silt application was identified over 202 fields and 3) the soil color changes were not related to a surface soil moisture change between both dates. {W}ith the actual availability of the {S}entinel-2 and the past availability of the {LANDSAT} satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.},
  keywords = {{T}ank silt application ; {V}ertisols ; {F}erralsols ; {S}oil color classification ; {CART} ; {SMOTE} ; {S}entinel-2 ; {I}ndia ; {INDE} ; {DECCAN} {PLATEAU} ; {BERAMBADI}},
  booktitle = {},
  journal = {{G}eoderma {R}egional},
  volume = {25},
  numero = {},
  pages = {e00389 [12 ]},
  ISSN = {2352-0094},
  year = {2021},
  DOI = {10.1016/j.geodrs.2021.e00389},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082091},
}