Towards Global Crop Maps with Transfer Learning
This work addresses the need for large-scale crop mapping to support food security decisions, but it is incremental as it builds on existing transfer learning methods in remote sensing.
The paper tackled the problem of scarce annotated data for crop mapping by using transfer learning with a deep learning model trained on Sentinel-1 VH time-series for paddy rice detection in South Korea, then fine-tuning it for paddy rice in France and Spain and barley in the Netherlands, achieving excellent performance for same-crop transfers and promising results for different crops.
The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage. During the previous decade and because of this surge of big Earth observations, deep learning methods have dominated the remote sensing and crop mapping literature. Nevertheless, deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. To address this problem, transfer learning methods can be used to exploit available annotations and enable crop mapping for other regions, crop types and years of inspection. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH time-series. We then fine-tune the model for i) paddy rice detection in France and Spain and ii) barley detection in the Netherlands. Additionally, we propose a modification in the pre-trained weights in order to incorporate extra input features (Sentinel-1 VV). Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type.