Large-Scale Crop Mapping Based on Multisource Remote Sensing Intelligent Interpretation: A Spatiotemporal Data Cubes Approach

Abstract

Large-scale crop mapping not only requires high-quality remote sensing data, but also has high requirements for multisource data organization and interpretation. How to make full use of multisource remote sensing data and combine it with artificial intelligence algorithms has become a key solution for current crop mapping. To solve the above problems, this study proposed a spatiotemporal data cubes approach, which could organize multisource remote sensing data in a unified manner and inherit the deep learning methods. Based on the spatiotemporal data cubes, an innovative sample expansion mechanism was designed, which will effectively solve the problem of sample scarcity in ground surveys. The Attention-based Bidirectional Long Short-Term Memory network was used with a late-fusion strategy as a classifier on each cube cell to further enhance the incorporation of multisource information. In this study, experiments toward winter wheat and garlic were conducted in the Henan Province of China (approximately 167 000 km2). The approach was verified via comparing with random forest and early-fusion strategy, which demonstrated competitive performance, achieving a weighted-average F1 score of 0.888, with individual F1 scores of 0.919, 0.854, and 0.846 for winter wheat, garlic, and others classes, respectively. The analysis also examined the impact of data availability and found that the temporal distribution of observations was more important than the absolute quantity of available data. Overall, the spatiotemporal data cubes approach proposed in this article has the potential to be extended to a national, intercontinental, or even global scale, and can provide a reference for large-scale crop mapping.

Keywords

• Deep learning
• large-scale crop mapping
• multisource remote sensing
• spatiotemporal data cubes