Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model
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 algorithm...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/8/1363 |
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| author | Yassine Bouslihim Abdelkrim Bouasria Budiman Minasny Fabio Castaldi Andree Mentho Nenkam Ali El Battay Abdelghani Chehbouni |
| author_facet | Yassine Bouslihim Abdelkrim Bouasria Budiman Minasny Fabio Castaldi Andree Mentho Nenkam Ali El Battay Abdelghani Chehbouni |
| author_sort | Yassine Bouslihim |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-bcb1b6c3952b450587a8a79840145ebf |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bcb1b6c3952b450587a8a79840145ebf2025-08-20T02:18:04ZengMDPI AGRemote Sensing2072-42922025-04-01178136310.3390/rs17081363Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner ModelYassine Bouslihim0Abdelkrim Bouasria1Budiman Minasny2Fabio Castaldi3Andree Mentho Nenkam4Ali El Battay5Abdelghani Chehbouni6National Institute for Agricultural Research, Rabat 10000, MoroccoFaculty of Science, Chouaib Doukkali University, El Jadida 24000, MoroccoSchool of Life & Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW 2006, AustraliaInstitute of BioEconomy, National Research Council of Italy (CNR), Via Giovanni Caproni 8, 50145 Firenze, ItalySchool of Life & Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW 2006, AustraliaCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Ben Guerir 43150, MoroccoCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Ben Guerir 43150, MoroccoAccurate 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 R<sup>2</sup> 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.https://www.mdpi.com/2072-4292/17/8/1363hyperspectral satellite imageryPRISMAmeta-learner modeldigital soil mappingsoil organic carbon |
| spellingShingle | Yassine Bouslihim Abdelkrim Bouasria Budiman Minasny Fabio Castaldi Andree Mentho Nenkam Ali El Battay Abdelghani Chehbouni Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model Remote Sensing hyperspectral satellite imagery PRISMA meta-learner model digital soil mapping soil organic carbon |
| title | Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model |
| title_full | Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model |
| title_fullStr | Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model |
| title_full_unstemmed | Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model |
| title_short | Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model |
| title_sort | soil organic carbon prediction and mapping in morocco using prisma hyperspectral imagery and meta learner model |
| topic | hyperspectral satellite imagery PRISMA meta-learner model digital soil mapping soil organic carbon |
| url | https://www.mdpi.com/2072-4292/17/8/1363 |
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