TY - JOUR
T1 - Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
AU - Deragon, Raphaël
AU - Saurette, Daniel D.
AU - Heung, Brandon
AU - Caron, Jean
N1 - Publisher Copyright:
© 2023 Authors R. Deragon, B. Heung, and J. Caron / the Crown, represented by Ontario Ministry of Agriculture, Food and Rural Affairs.
PY - 2023/3
Y1 - 2023/3
N2 - Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable pro-duction. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum. Estimations of the depth and thickness of these materials are critical for soil management. Therefore, five drained and cultivated peatlands were studied to estimate their maximum peat thickness (MPT)——a potential key soil property that can help identify management zones for their conservation. MPT can be defined as the depth to the mineral layer (DML) minus the coprogenous layer thickness (CLT). The objective of this study was to estimate DML, CLT, and MPT at a regional scale using environmental covariates derived from remote sensing. Three machine-learning models (Cubist, Random Forest, and k-Nearest Neighbor) were compared to produce maps of DML and CLT, which were combined to generate MPT at a spatial resolution of 10 m. The Cubist model performed the best for predicting both features of interest, yielding Lin’s concordance correlation coefficients of 0.43 and 0.07 for DML and CLT, respectively, using a spatial cross-validation procedure. Interpretation of the drivers of CLT was limited by the poor predictive power of the final model. More precise data on MPT are needed to support soil conservation practices, and more CLT field observations are required to obtain a higher prediction accuracy. Nonetheless, digital soil mapping using open-access geospatial data shows promise for understanding and managing cultivated peatlands.
AB - Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable pro-duction. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum. Estimations of the depth and thickness of these materials are critical for soil management. Therefore, five drained and cultivated peatlands were studied to estimate their maximum peat thickness (MPT)——a potential key soil property that can help identify management zones for their conservation. MPT can be defined as the depth to the mineral layer (DML) minus the coprogenous layer thickness (CLT). The objective of this study was to estimate DML, CLT, and MPT at a regional scale using environmental covariates derived from remote sensing. Three machine-learning models (Cubist, Random Forest, and k-Nearest Neighbor) were compared to produce maps of DML and CLT, which were combined to generate MPT at a spatial resolution of 10 m. The Cubist model performed the best for predicting both features of interest, yielding Lin’s concordance correlation coefficients of 0.43 and 0.07 for DML and CLT, respectively, using a spatial cross-validation procedure. Interpretation of the drivers of CLT was limited by the poor predictive power of the final model. More precise data on MPT are needed to support soil conservation practices, and more CLT field observations are required to obtain a higher prediction accuracy. Nonetheless, digital soil mapping using open-access geospatial data shows promise for understanding and managing cultivated peatlands.
KW - coprogenous soil
KW - machine learning
KW - organic soils
KW - peat thickness
KW - predictive digital soil mapping
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U2 - 10.1139/cjss-2022-0031
DO - 10.1139/cjss-2022-0031
M3 - Article
AN - SCOPUS:85139246423
SN - 0008-4271
VL - 103
SP - 103
EP - 120
JO - Canadian Journal of Soil Science
JF - Canadian Journal of Soil Science
IS - 1
ER -