Shrimp Ponds Mapping Using Sentinel-1 and Sentinel-2 Data: A Pilot Study in Tumbes, Peru

Accurate mapping of aquaculture ponds is essential for monitoring production systems and supporting the sustainable management of coastal and inland environments. However, aquaculture pond detection from satellite imagery remains challenging because aquaculture ponds often share spectral and structural similarities with natural water bodies, wetlands, and seasonally flooded agricultural areas. This study introduces a method for identifying aquaculture ponds by characterizing management-driven hydrological cycles associated with aquaculture production using Sentinel-1 and Sentinel-2 time series data. Water indices and water surface extent dynamics were used to characterize key stages of aquaculture management, including pond drying and preparation, water filling, grow-out, and harvesting. A hybrid 1D-CNN–MLP model was then developed to classify ponds into aquaculture, non-aquaculture, and inactive pond categories. Independent validation showed that the proposed model achieved overall accuracies of 0.927, 0.936, and 0.954 for 2023, 2024, and 2025, respectively, with F1-scores for aquaculture ponds ranging from 0.842 to 0.980. Compared with existing land-cover products (including Dynamic World and ESRI Land Cover), the proposed method advances from water-surface detection to the identification of aquaculture use, demonstrating its potential to support operational aquaculture monitoring and sustainable aquaculture management.