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Remote Sensing
Volume 17
Issue 8
10.3390/rs17081363
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Article

Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model

by
Yassine Bouslihim
1,
Abdelkrim Bouasria
2,
Budiman Minasny
3,
Fabio Castaldi
4,*,
Andree Mentho Nenkam
3,
Ali El Battay
5 and
Abdelghani Chehbouni
5
1
National Institute for Agricultural Research, Rabat 10000, Morocco
2
Faculty of Science, Chouaib Doukkali University, El Jadida 24000, Morocco
3
School of Life & Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW 2006, Australia
4
Institute of BioEconomy, National Research Council of Italy (CNR), Via Giovanni Caproni 8, 50145 Firenze, Italy
5
Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Ben Guerir 43150, Morocco
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(8), 1363; https://doi.org/10.3390/rs17081363
Submission received: 26 February 2025 / Revised: 8 April 2025 / Accepted: 9 April 2025 / Published: 11 April 2025
(This article belongs to the Special Issue Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval)
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Abstract

Accurate mapping of soil organic carbon (SOC) supports sustainable land management practices and carbon accounting initiatives for mitigating climate change impacts. This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using the PRISMA hyperspectral satellite imagery in the Doukkala plain of Morocco. The framework employs a two-layer structure of prediction models. The first layer consists of Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). These base models were configured using data smoothing, transformation, and spectral feature selection techniques, based on a 70/30% data split. The second layer utilizes a ridge regression model as a meta-learner to integrate predictions from the base models. Results indicated that RF and SVR performance improved primarily with feature selection, while PLSR was most influenced by data smoothing. The meta-learner approach outperformed individual base models, achieving an average relative improvement of 48.8% over single models, with an R2 of 0.65, an RMSE of 0.194%, and an RPIQ of 2.247. This study contributes to the development of methodologies for predicting and mapping soil properties using PRISMA hyperspectral data.
Keywords: hyperspectral satellite imagery; PRISMA; meta-learner model; digital soil mapping; soil organic carbon hyperspectral satellite imagery; PRISMA; meta-learner model; digital soil mapping; soil organic carbon
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MDPI and ACS Style

Bouslihim, Y.; Bouasria, A.; Minasny, B.; Castaldi, F.; Nenkam, A.M.; El Battay, A.; Chehbouni, A. Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model. Remote Sens. 2025, 17, 1363. https://doi.org/10.3390/rs17081363

AMA Style

Bouslihim Y, Bouasria A, Minasny B, Castaldi F, Nenkam AM, El Battay A, Chehbouni A. Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model. Remote Sensing. 2025; 17(8):1363. https://doi.org/10.3390/rs17081363

Chicago/Turabian Style

Bouslihim, Yassine, Abdelkrim Bouasria, Budiman Minasny, Fabio Castaldi, Andree Mentho Nenkam, Ali El Battay, and Abdelghani Chehbouni. 2025. "Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model" Remote Sensing 17, no. 8: 1363. https://doi.org/10.3390/rs17081363

APA Style

Bouslihim, Y., Bouasria, A., Minasny, B., Castaldi, F., Nenkam, A. M., El Battay, A., & Chehbouni, A. (2025). Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model. Remote Sensing, 17(8), 1363. https://doi.org/10.3390/rs17081363

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