Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: a study case from northern Morocco. Issue 26 (13th December 2022)

• Record Type: Journal Article
• Authors: John, Kingsley; Bouslihim, Yassine; Bouasria, Abdelkrim; Razouk, Rachid; Hssaini, Lahcen; Isong, Isong Abraham; Ait M'barek, Samir; Ayito, Esther O.; Ambrose-Igho, Gare
• Is Part Of: Geocarto international. Volume 37:Issue 26(2023)
• Page Start: 11209
• Page End: 11222
• Publication Date: 2022-12-13
• Subjects: Predictive soil mapping -- sampling strategies -- soil nutrients -- soil management -- machine learning -- Mediterranean region; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2048091
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling (cLHS)] and sample ratios (10% = 147 and 25% = 368) to predict soil phosphorus and potassium contents, previously estimated using standard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better.