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Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier

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Title Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier
 
Creator Schulthess, Urs
Rodrigues, Francelino
Taymans, Matthieu
Bellemans, Nicolas
Bontemps, Sophie
Ortiz Monasterio, Jose Iván
Gerard, Bruno G.
Defourny, Pierre
 
Subject crops
forests
machine learning
agriculture
remote sensing
 
Description 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.
 
Date 2023-02-01
2023-02-03T08:30:07Z
2023-02-03T08:30:07Z
 
Type Journal Article
 
Identifier Schulthess, U., Rodrigues, F., Taymans, M., Bellemans, N., Bontemps, S., Ortiz-Monasterio, I., Gérard, B. and Defourny, P. 2023. Optimal sample size and composition for crop classification with sen2-agri’s random forest classifier. Remote Sensing 15(3):608. https://hdl.handle.net/10883/22489
2072-4292
https://hdl.handle.net/10568/128426
https://hdl.handle.net/10883/22489
https://doi.org/10.3390/rs15030608
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Publisher MDPI
 
Source Remote Sensing