Temporal Optimisation of Satellite Image-Based Crop Mapping: A Comparison of Deep Time Series and Semi-Supervised Time Warping Strategies

Abstract

This study presents a novel approach to crop mapping using remotely sensed satellite images. It addresses the significant classification modelling challenges, including (1) the requirements for extensive labelled data and (2) the complex optimisation problem for selection of appropriate temporal windows in the absence of prior knowledge of cultivation calendars. We compare the lightweight Dynamic Time Warping (DTW) classification method with the heavily supervised Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) using high-resolution multispectral optical satellite imagery (3 m/pixel). Our approach integrates effective practical preprocessing steps, including data augmentation and a data-driven optimisation strategy for the temporal window, even in the presence of numerous crop classes. Our findings demonstrate that DTW, despite its lower data demands, can match the performance of CNN-LSTM through our effective preprocessing steps while significantly improving runtime. These results demonstrate that both CNN-LSTM and DTW can achieve deployment-level accuracy and underscore the potential of DTW as a viable alternative to more resource-intensive models. The results also prove the effectiveness of temporal windowing for improving runtime and accuracy of a crop classification study, even with no prior knowledge of planting timeframes.