Mapping rice-crayfish co-culture (RCC) fields with Sentinel-1 and -2 time series in China's primary crayfish production region Jianghan Plain

Crayfish is a high-risk invasive species with devastating impacts on freshwater ecosystems. Meanwhile, nicknamed “little lobster”, it is a popular food in many countries including China. The crayfish production in China increased from 1.13 to 2.39 million tons in 2017–2020, accounting for 97% global production. This phenomenal increase is attributed to the expansion of the rice-crayfish co-culture (RCC) farming mode whose area increased by 123% from 0.57 to 1.26 million ha in 2017–2020. However, the fast expansion of RCC is undertaken in an uncontrolled and unregulated manner, referred by some researchers as a “blind expansion”. It raises wide concerns on ecological risks (crayfish can escape in high-magnitude floods), endangerment of riverbanks (crayfish burrows), food security (reduced rice production), excessive water consumption, and greenhouse gas (methane) emission. It is thus urgent to accurately map the spatial distributions of RCC fields using satellite remote sensing data, so as to assess the ecological and environmental impacts and risks, and to better regulate the expansion. However, there are currently no practically-scalable approaches to reliably map RCC fields in large areas. In particular, there lack the knowledge on the relationship between satellite observations and on-ground biophysical processes in RCC fields. In this study, we conducted field surveys in RCC fields, and in particular, the daily water levels in RCC fields were measured for the complete year of 2020. The comparison of annual water-level time series and satellite-NDVI time series, combined with the RCC farming information collected in surveys, reveals how satellite observations vary in correspondences to on-ground biophysical processes in RCC fields; and importantly, it provides information on how RCC fields can be efficiently distinguished from other land covers using satellite data. Based on that, we propose an approach to map RCC fields from annual Sentinel-2 optical-wavelength and Sentinel-1 Synthetic Aperture Radar (SAR) time series, utilizing the annual water-occurrence frequency (AWF) and characteristic phenological features derived from the satellite data. This method was demonstrated in Jianghan Plain, the primary crayfish production region in China with an area of approximately 37,000 km². A total of 273,365 ha (2733.65 km²) RCC field area in year 2020 was mapped, which accounted for 24.6% of the whole plain's cropland area (approximately 11,100 km²), meaning a significant proportion of the rice paddies were converted to RCC fields. The RCC mapping accuracies were validated using the samples collected in field surveys and also from Google Earth images, and was compared with the state-of-practice RCC mapping method using bi-seasonal optical-wavelength satellite images. The proposed method obtained 93.8% overall accuracy and 0.91 kappa coefficient, and outperformed the compared bi-seasonal method. The proposed method is scalable for large-area and multi-annual applications. Future research regarding improvements and applications is recommended.