  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.
