eng Science and Education Publishing Journal of Geosciences and Geomatics 2373-6704 2024-05-19 12 2 44 54 10.12691/jgg-12-2-3 JGG20241223 article Lionelle Bitom-Mamdem 1 Achille Ibrahim ibrahimachille13@gmail.com 2 Boris Sounya 3 Primus Azinwi Tamfuh 3 4 Denis Tiki 5 Sabine Danala 6 Olivier Leumbe Leumbe 7 Desir¨¦ Tsozu¨¦ 8 Dieudonn¨¦ Bitom 8 8 Faculty of Science, University of Yaounde I, P.O.Box 812 Yaounde, Cameroon Faculty of Agronomy and Agricultural Sciences, University of Dschang, P.O.Box 222, Dschang, Cameroon Institute for Research and Agricultural Development (IRAD), P.O. Box 65 Ngaoundere, Cameroon Institute for Research and Agricultural Development, P.O. Box 2123 Nkolbisson,Yaounde, Cameroon Faculty of Science, University of Ngaoundere, P.O. Box 454 Ngaoundere, Cameroon National Institute of Cartography, P.O. Box 157 Yaounde, Cameroon Faculty of Science, University of Maroua, P.O. Box 814 Maroua, Cameroon Remote Sensing and Geographic Information System methods used to map soils over space and time have numerous advantages over conventional soil mapping techniques which are time consuming, labour intensive, expensive and cover limited areas. In Cameroon, most soil maps were established at small scale using conventional methods and soils units are poorly delineated making it difficult to properly manage soils for various purposes. This study aims to use spectral signatures and GIS techniques to update existing soil maps. The method is based on ETM+ image processing, field investigation and existing data from other maps to update an existing soil map of the Mayo Kani Division in Far North Cameroon. The methodology consisted of interpreting the relationship (colour, organic matter, iron content, texture, moisture content, vegetation, human activities) between soil and satellite images. The main results revealed that delineation of soil units using GIS permitted to establish a soil map and to update soil digital data. Thus, percentages of Vertisols, Ferruginous soils and Halomorphic soils decreased from 38.38, 26.87 and 10.64 to 36.88, 25.67 and 9.13 respectively. Meanwhile, the percentages of less evolved soils, Raw Mineral soils and Hydromorphic soils increased respectively from 14.17 to 16.17, 0.43 to 0.54 and 8.73 to 10.67, while percentage of Fersiallitic soils remained constant, at 0.73. These results reveal that Remote Sensing data and GIS constitute a valuable approach to update existing soil map and to draw digital soil maps. It is recommended that Remote Sensing data be combined with field data to obtain more precise maps. https://pubs.sciepub.com/jgg/12/2/3/jgg-12-2-3.pdf Digital soil mapping GIS Remote Sensing soil semi-arid area Far North Cameroon