Crop type mapping with temporal sample migration

Accurate and timely crop maps are crucial for monitoring agricultural production. Current supervised classification methods based on remote sensing rely heavily on ground-truth samples collected at a high cost, and the years without sampling highly limit classification accuracy. To address such a challenge, we proposed a time-migration method based on historical training samples collected in 2017, 2018 and 2020 to conduct supervised crop classification mapping in the target year (2021) with no ground samples. We chose Hailun City, Heilongjiang Province of northeastern China, as the study site; the major crops included corn, soybean, and rice. We reconstructed time series of Sentinel-2 data and selected spectro-temporal features to identify standard crop phenological curves. We calculated the similarity between reference and image spectra and designed label-matching rules to identify training samples through the dynamic time warping algorithm. We then used the historical samples to map the crop types of the target year. The results showed that the migration accuracy reached 95% for major crop. Using these samples as training data with a random forest to classify the target year, the overall accuracy reached 94.13%. The new sample time-migration method proposed in this study can efficiently migrate historical samples, greatly reducing the cost of ground-truth sampling.