A Moroccan soil spectral library use framework for improving soil property prediction: evaluating a geostatistical approach

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dc.contributor.authorAsrat, Tadesse Gashaw
dc.contributor.authorBreure, Timo
dc.contributor.authorSakrabani, Ruben
dc.contributor.authorCorstanje, Ron
dc.contributor.authorHassall, Kirsty L.
dc.contributor.authorHamma, Abdellah
dc.contributor.authorKebede, Fassil
dc.contributor.authorHaefele, Stephan M.
dc.date.accessioned2024-12-20T13:18:16Z
dc.date.available2024-12-20T13:18:16Z
dc.date.freetoread2024-12-20
dc.date.issued2024-12-01
dc.date.pubOnline2024-11-24
dc.description.abstractA soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. However, achieving this higher accuracy comes with associated costs, complexity, and resource requirements, thus limiting widespread adoption. Furthermore, there is a lack of comprehensive frameworks for developing and utilizing soil spectral libraries (SSLs) to make predictions for specific samples. While calibration samples are necessary, there is the need to optimize SSL development through strategically determining the quantity, location, and timing of these samples based on the quality of the information in the library. This research aimed to develop a spatially optimized SSL and propose a use-framework tailored for predicting soil properties for a specific farmland context. Consequently, the Moroccan SSL (MSSL) was established utilizing a stratified spatially balanced sampling design, using six environmental covariates and FAO soil units. Subsequently, various criteria for calibration sample selection were explored, including a spatial autocorrelation of spectra principal component (PC) scores (spatial calibration sample selection), spectra similarity memory-based learner (MBL), and selection based on environmental covariate clustering. Twelve soil properties were used to evaluate these calibration sample selections to predict soil properties using the near infrared (NIR) and mid infrared (MIR) ranges. Among the methods assessed, we observed distinct precision improvements resulting from spatial sample selection and MBL compared to the use of the entire MSSL. Notably, the Lin's Concordance Correlation Coefficient (CCC) values using the spatial calibration sample selection was improved for Olsen extractable phosphorus (OlsenP) by 41.3% and Mehlich III extractable phosphorus (P_M3) by 8.5% for the MIR spectra and for CEC by 25.6%, pH by 13.0% and total nitrogen (Tot_N) by 10.6% for the NIR spectra in reference to use of the entire MSSL. Utilizing the spatial autocorrelation of the spectra PC scores proved beneficial in identifying appropriate calibration samples for a new sample location, thereby enhancing prediction performance comparable to, or surpassing that of the use of the entire MSSL. This study signifies notable advancement in crafting targeted models tailored for specific samples within a vast and diverse SSL.
dc.description.journalNameGeoderma
dc.description.sponsorshipThe authors want to thank Mohammed VI Polytechnic University (UM6P) and OCP group, for the technical and financial support, respectively.
dc.identifier.citationAsrat TG, Breure T, Sakrabani R, et al., (2024) A Moroccan soil spectral library use framework for improving soil property prediction: evaluating a geostatistical approach. Geoderma, Volume 452, December 2024, Article number 117116en_UK
dc.identifier.eissn1872-6259
dc.identifier.elementsID560136
dc.identifier.issn0016-7061
dc.identifier.paperNo117116
dc.identifier.urihttps://doi.org/10.1016/j.geoderma.2024.117116
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23288
dc.identifier.volumeNo452
dc.languageEnglish
dc.language.isoen
dc.publisherElsevieren_UK
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0016706124003458?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSoil propertiesen_UK
dc.subjectIR spectroscopyen_UK
dc.subjectSpatial non-stationarityen_UK
dc.subjectGeostatisticsen_UK
dc.subjectMBLen_UK
dc.subjectCovariate clusteringen_UK
dc.subjectMoroccoen_UK
dc.subject41 Environmental Sciencesen_UK
dc.subject4106 Soil Sciencesen_UK
dc.subjectAgronomy & Agricultureen_UK
dc.titleA Moroccan soil spectral library use framework for improving soil property prediction: evaluating a geostatistical approachen_UK
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-11-18

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