Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier
| cg.authorship.types | CGIAR and developing country institute | en |
| cg.authorship.types | CGIAR and advanced research institute | en |
| cg.contributor.affiliation | International Maize and Wheat Improvement Center | en |
| cg.contributor.affiliation | Lincoln University | en |
| cg.contributor.affiliation | Catholique Universite de Louvain | en |
| cg.contributor.affiliation | Université Mohammed VI Polytechnique | en |
| cg.contributor.crp | Maize | |
| cg.contributor.crp | Wheat | |
| cg.contributor.donor | CGIAR Trust Fund | en |
| cg.contributor.initiative | Digital Innovation | |
| cg.coverage.country | Mexico | |
| cg.coverage.iso3166-alpha2 | MX | |
| cg.coverage.region | Latin America | |
| cg.coverage.region | Central America | |
| cg.creator.identifier | Urs Schulthess: 0000-0002-9642-9762 | |
| cg.creator.identifier | Francelino Rodrigues: 0000-0001-7273-2217 | |
| cg.creator.identifier | Ivan: 0000-0002-2572-3219 | |
| cg.creator.identifier | Bruno Gerard: 0000-0002-1079-7493 | |
| cg.howPublished | Formally Published | en |
| cg.identifier.doi | https://doi.org/10.3390/rs15030608 | en |
| cg.isijournal | ISI Journal | en |
| cg.issn | 2072-4292 | en |
| cg.issue | 3 | en |
| cg.journal | Remote Sensing | en |
| cg.reviewStatus | Peer Review | en |
| cg.subject.actionArea | Systems Transformation | |
| cg.subject.actionArea | Resilient Agrifood Systems | |
| cg.subject.impactArea | Poverty reduction, livelihoods and jobs | |
| cg.subject.impactArea | Nutrition, health and food security | |
| cg.volume | 15 | en |
| dc.contributor.author | Schulthess, Urs | en |
| dc.contributor.author | Rodrigues, Francelino | en |
| dc.contributor.author | Taymans, Matthieu | en |
| dc.contributor.author | Bellemans, Nicolas | en |
| dc.contributor.author | Bontemps, Sophie | en |
| dc.contributor.author | Ortíz Monasterio, Jose Iván | en |
| dc.contributor.author | Gerard, Bruno G. | en |
| dc.contributor.author | Defourny, Pierre | en |
| dc.date.accessioned | 2023-02-03T08:30:07Z | en |
| dc.date.available | 2023-02-03T08:30:07Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/128426 | |
| dc.title | Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier | en |
| dcterms.abstract | Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to provide practitioners with recommendations for the best sample size and composition. The study area was located in the Yaqui Valley in Mexico. Using polygons of more than 6000 labeled crop fields, we prepared data sets for training, in which the nine crops had an equal or proportional representation, called Equal or Ratio, respectively. Increasing the size of the training set improved the overall accuracy (OA). Gains became marginal once the total number of fields approximated 500 or 40 to 45 fields per crop type. Equal achieved slightly higher OAs than Ratio for a given number of fields. However, recall and F-scores of the individual crops tended to be higher for Ratio than for Equal. The high number of wheat fields in the Ratio scenarios, ranging from 275 to 2128, produced a more accurate classification of wheat than the maximal 80 fields of Equal. This resulted in a higher recall for wheat in the Ratio than in the Equal scenarios, which in turn limited the errors of commission of the non-wheat crops. Thus, a proportional representation of the crops in the training data is preferable and yields better accuracies, even for the minority crops. | en |
| dcterms.accessRights | Open Access | |
| dcterms.audience | Scientists | en |
| dcterms.available | 2023-01-19 | |
| dcterms.bibliographicCitation | Schulthess, U., Rodrigues, F., Taymans, M., Bellemans, N., Bontemps, S., Ortiz-Monasterio, I., Gérard, B., & Defourny, P. (2023). Optimal Sample Size and Composition for Crop Classification with Sen2-Agri’s Random Forest Classifier. Remote Sensing, 15(3), 608. https://doi.org/10.3390/rs15030608 | en |
| dcterms.issued | 2023-02-01 | |
| dcterms.language | en | |
| dcterms.license | CC-BY-4.0 | |
| dcterms.publisher | MDPI | en |
| dcterms.subject | crops | en |
| dcterms.subject | forests | en |
| dcterms.subject | machine learning | en |
| dcterms.subject | agriculture | en |
| dcterms.subject | remote sensing | en |
| dcterms.type | Journal Article |