A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China

Brine shrimp are vital inhabitants of saltwater lakes, contributing significantly to economic and ecological systems. With increasing threats from environmental degradation and overharvesting, effective monitoring is urgently needed. Traditional field sampling methods are limited in scope and efficiency, necessitating a reliable remote sensing-based approach. However, Ebinur Lake’s complex spectral environment, characterized by poor water quality and diverse suspended particulates, poses challenges for satellite remote sensing accuracy. To overcome these issues, we developed a novel multi-rule extraction model based on Landsat data, leveraging the distinct short-wave infrared signatures of brine shrimp to enhance detection accuracy. We evaluated and validated this method using Landsat 8 and Sentinel-2 datasets, achieving a classification accuracy of 94.5% and a kappa coefficient of 0.88, surpassing existing methods. Additionally, our analysis of a decade of Landsat data in Ebinur Lake via Google Earth Engine revealed a correlation between brine shrimp distribution and lake surface area. Our model demonstrates high accuracy and scalability in mapping brine shrimp, making it a valuable tool for long-term, large-scale assessments in saline lakes. This capability holds significant potential for advancing fisheries research and informing conservation strategies.