Abstract
Abstract. Dynamic Time Warping (DTW) has been successfully used for crops mapping due to its capability to achieve good classification results when a reduced number of training samples and irregular satellite image time series is available. Despite its recognized advantages, DTW does not account for the duration and seasonality of crops and local differences when assessing the similarity between two temporal sequences. In this study, we implemented a Weighted Derivative modification of DTW (WDDTW) and compared it with DTW and Time Weighted Dynamic Time Warping (TWDTW) for crops mapping. We show that WDDTW outperformed DTW achieving an overall accuracy of 67 %, whereas DTW obtained an accuracy of 57%. Yet, TWDTW performed better than both methods obtaining an accuracy of 88%.
Highlights
The earth’s population exceeds 7 billion (Brelsford et al, 2018)
A three-step methodology was implemented in this study: (i) generation of NDVI-based temporal sequences; (ii) implementing Dynamic Time Warping (DTW), Time Weighted Dynamic Time Warping (TWDTW) and Weighted Derivative modification of DTW (WDDTW); (iii) evaluating the results using standard classification accuracy metrics
TWDTW outperformed both DTW and WDDTW, obtaining an overall accuracy of 88% as compared to 67% and 57% accuracy obtained by WDDTW and DTW respectively
Summary
The earth’s population exceeds 7 billion (Brelsford et al, 2018). This population growth puts pressure on the food supply systems across the globe. Food security related challenges are high on the agenda of the Sustainable Development Goals (SDG) (United-Nations, 2015), in particular SDG Goal 2-Zero Hunger. To be able to address these challenges, decision-makers require accurate and spatially explicit information on crops (Fritz et al 2015). Such information is of paramount importance for evidence-based action and policy formulation. The importance of remote sensing data for generating this information is widely recognized
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