Identification of soil type in Pakistan using remote sensing and machine learning
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Identification of soil type in Pakistan using remote sensing and machine learning. / Haq, Yasin Ul; Shahbaz, Muhammad; Asif, H. M.Shahzad; Al-Laith, Ali; Alsabban, Wesam; Aziz, Muhammad Haris.
In: PeerJ Computer Science, Vol. 8, e1109, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Identification of soil type in Pakistan using remote sensing and machine learning
AU - Haq, Yasin Ul
AU - Shahbaz, Muhammad
AU - Asif, H. M.Shahzad
AU - Al-Laith, Ali
AU - Alsabban, Wesam
AU - Aziz, Muhammad Haris
N1 - Publisher Copyright: © 2022 Ul Haq et al.
PY - 2022
Y1 - 2022
N2 - Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.
AB - Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.
KW - Digital soil mapping
KW - Random forest
KW - Remote sensing
KW - Soil type
KW - Spectral signatures
U2 - 10.7717/PEERJ-CS.1109
DO - 10.7717/PEERJ-CS.1109
M3 - Journal article
AN - SCOPUS:85140585064
VL - 8
JO - PeerJ Computer Science
JF - PeerJ Computer Science
SN - 2376-5992
M1 - e1109
ER -
ID: 343042982