PhenoCropNet: A Phenology-Aware-Based SAR Crop Mapping Network for Cloudy and Rainy Areas

Crop mapping in a cloudy area is always a challenge due to the lack of time-series clear optical satellite imagery. Making use of time-series synthetic aperture radar (SAR) imagery that is immune to cloud contamination is essential and promising for seamless and large-area crop mapping. However, existing deep learning (DL)-based crop classification methods give the extracted phenological features equal weights, without considering the different contributions of phenological features of the different crop growth periods. In this article, a phenology-based crop mapping network (PhenoCropNet) is proposed to extract the discriminative features from the two levels, including the key phenological dates in the phenological periods and key phenological periods in the whole growth stages. PhenoCropNet includes a phenological calendar information injection (PAI) module that divides the satellite imagery time series (SITS) into multiple sequences according to the phenological calendar information, and a hierarchical attention network structure that uses the two-level bidirectional gated recurrent unit-based self-attention (BiGRUA) modules to automatically extract the features containing the most important phenological information of key phenological dates and key phenological periods. The proposed PhenoCropNet was verified in Hubei province in China, around 185 933 km2, a typical cloudy area in China, for rapid winter crop mapping based on temporal Sentinel-1 SAR imagery. The mapping result shows that the $F1$ -score of PhenoCropNet for winter crop mapping could achieve 0.90, showing great potential in large-scale and seamless crop mapping. The code is available on request: https://github.com/LL0912/PhenoCropNet.