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Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Richard L. McCormick·Prasad Thenkabail·Itiya Aneece·Pardhasaradhi Teluguntla·Adam Oliphant·Daniel Foley
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Abstract

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

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Photogrammetric Engineering and Remote SensingArticle
90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 36.73462649015923° to 37.169191590622404° latitude; -119.72184856336573° to -118.95495213764198° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Richard L. McCormick, Prasad Thenkabail, Itiya Aneece, Pardhasaradhi Teluguntla, Adam Oliphant, Daniel Foley. 2025. Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data. https://doi.org/10.14358/pers.24-00072r3

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