Multi-Year Vector Dynamic Time Warping Based Crop Mapping
This addresses the challenge of predicting crop maps across years for agricultural monitoring, offering a robust solution with high accuracy, though it is incremental as it builds on dynamic time warping techniques.
The paper tackled the problem of cross-year crop mapping, where existing methods degrade in accuracy, by proposing Vector Dynamic Time Warping (VDTW) to handle temporal and spectral variations, achieving 99.74% overall accuracy in cross-year tests with fewer training samples compared to state-of-the-art methods.
Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests of our approach with Landsat 8 time-series imagery from years 2013 to 2016 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, we tested VDTW corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data. The VDTW method achieved 99.85% and 99.74% overall accuracies for the same and cross years, respectively with fewer training samples compared to other state-of-the-art approaches, i.e. spectral angle mapper (SAM), dynamic time warping (DTW), time-weighted DTW (TWDTW), random forest (RF), support vector machine (SVM) and deep long short-term memory (LSTM) methods. The proposed method could be expanded for other crop types and/or geographical areas.