Welcome to the Global Aqua Remote Sensing (GARS) laboratory at the School of Environmental Science and Engineering, Southern University of Science and Technology. Our research is dedicated to observing global aquatic environments using advanced remote sensing techniques. We focus on exploring the spatial and temporal changes of these environments and their potential impacts. By combining remote sensing with numerical modeling and field surveys, our goal is to gain a comprehensive understanding of aquatic environment dynamics and their extensive effects on both ecosystems and human activities.
    Over the years, our team has developed a wealth of datasets pertaining to the remote sensing of aquatic environments. We are enthusiastic about collaborating and invite researchers and professionals interested in this field to explore and utilize these datasets. Our lab is committed to fostering collaborative efforts that can further our understanding and contribute to meaningful scientific and environmental advancements.
    We are also looking for passionate Postdocs, Ph.D., Master, and Undergraduate students to join the team!


Global Coastal Algal Bloom

Basic descriptions The Global Coastal Algal Bloom Dataset is derived from daily MODIS satellite images with a resolution of 1 km, totaling 0.76 million images globally, spanning the period from 2003 to 2020. It captures the occurrences of coastal blooms for

Global Lake Algal Bloom

Basic descriptions This dataset provides maximum bloom extent (MBE, in km2) and median bloom occurrence (BO, in %) for 21,878 freshwater lakes worldwide in the entire period (1980s-2010s) and three sub-periods (1980–1990s, 2000s and 2010s), which was generated using Landsat satellite

Global Lake Surface Water Temperature

Basic descriptions The GLAST dataset is a comprehensive, high-temporal-resolution global lake surface temperature dataset, covering 92,245 lakes worldwide (with 36% located in the Arctic region) from 1981 to 2099. This dataset is generated through lake-specific calibrated simulations using the FLake model,

Global Lake Area

Basic descriptions GLAKES is a comprehensive dataset that comprises the maximum extents of 3.4 million lakes with surface area >0.03 km² over the past four decades (from 1984 to 2019). Overall, the GLAKES dataset shows marked improvements over previous global lake