Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series

Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly mapped. In this study, a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture, namely crop rotation mapping (CRM), were proposed to synergize the synthetic aperture radar (SAR) and optical time series in a rotational mapping task. The proposed end-to-end architecture had reasonable accuracies (i.e., accuracy > 0.85) in mapping crop rotation, which outperformed other state-of-the-art non-deep or deep-learning solutions. For some confusing rotation types, such as fallow-single rice and crayfish-single rice, CRM showed substantial improvements from traditional methods. Furthermore, the deeply synergistic SAR-optical, time-series data, with a corresponding attention mechanism, were effective in extracting crop rotation features, with an overall gain of accuracy of four points compared with ablation models. Therefore, our proposed method added wisdom to dynamic crop rotation mapping and yields important information for the agro-ecosystem management of the study area.

Automated summary

Crop rotations play a crucial role in agriculture management, impacting food security and agro-ecosystem sustainability. A novel CNN-LSTM architecture called CRM was introduced in this study to map crop rotation patterns using SAR and optical time series data. The proposed method outperformed other non-deep or deep-learning solutions and showed substantial improvements for confusing rotation types. The synergistic SAR-optical time-series data with an attention mechanism significantly enhanced the accuracy in extracting crop rotation features.