A review of Google Earth Engine application in mapping aquaculture ponds

Abstract Google Earth Engine (GEE) can effectively monitor aquaculture ponds, but it is underutilized. This paper aims to review the application of GEE in mapping ponds around world using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. A total 16 journal articles have been identified since 2019 from Scopus Science Direct databases. Most studies were conducted China United States Sentinel-2, Sentinel-1 Landsat 8 images. Random Forest Decision Tree are commonly used machine learning classifiers GEE-based studies. In general, some reported that extract spatial distribution with great overall accuracies, which more than 90%. Difficult detect small misclassification due similar spectral reflectance among limitations previous Future research directions include incorporation pond extraction techniques different types satellite images GEE.