A comparative review with experiments identifying optimal preprocessing, models, and transfer strategies for large-scale pixel-wise crop mapping using Landsat 8 data across five sites.
author Guan, K
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Year-wise cross-validation across ten ML algorithms on Harmonized Landsat-Sentinel imagery shows SVMs achieve mean F1 of 0.74 for almonds in California and 0.59 for corn in Iowa by early June in unseen validation years.
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From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping
A comparative review with experiments identifying optimal preprocessing, models, and transfer strategies for large-scale pixel-wise crop mapping using Landsat 8 data across five sites.
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Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
Year-wise cross-validation across ten ML algorithms on Harmonized Landsat-Sentinel imagery shows SVMs achieve mean F1 of 0.74 for almonds in California and 0.59 for corn in Iowa by early June in unseen validation years.