Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences

Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. I...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-05, Vol.16 (9), p.1493
Main Authors: Hoppe, Hauke, Dietrich, Peter, Marzahn, Philip, Weiß, Thomas, Nitzsche, Christian, Freiherr von Lukas, Uwe, Wengerek, Thomas, Borg, Erik
Format: Article
Language:English
Subjects:
Accuracy
Artificial satellites in remote sensing
Biodiversity
Classification
crop classification
Crop identification
Crops
Deep learning
Drought
Environmental impact
Flowers & plants
Geology
Growing season
Humidity
Hydrology
Identification and classification
Image classification
Image processing
Influence
Learning algorithms
Machine learning
Methods
Multilayer perceptrons
Phenology
Precipitation
Remote sensing
Sentinel-2
Soil erosion
spatial transferability
Stochasticity
Support vector machines
Vegetation
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Summary:Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. It proposes a new testing methodology that systematically analyzes the quality of the spatial transfer of trained models. In this study, the classification results of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and a Majority Voting of all models and their spatial transferability are assessed. The proposed testing methodology comprises 18 test scenarios to investigate phenological, temporal, spatial, and quantitative (quantitative regarding available training data) influences. Results show that the model accuracies tend to decrease with increasing time due to the differences in phenological phases in different regions, with a combined F1-score of 82% (XGBoost) when trained on a single day, 72% (XGBoost) when trained on the half-season, and 61% when trained over the entire growing season (Majority Voting).
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16091493