Welcome to GCER Lab
Geospatial Computing for Earth observation Research (GCER) Lab is housed in the Department of Agricultural and Biological Engineering at Mississippi State University. Our research group utilizes satellite observations, cutting-edge deep learning algorithms, and high-performance computing to advance geospatial data analysis for agriculture and water resource management.
Mission
GCER lab's mission is to advance sustainable agriculture and water resources management by developing innovative AI-driven software that integrate satellite data, and high-performance computing. Our interdisciplinary research supports further insights on agricultural and environmental challenges from local to global scales.
1. Advance Earth Observation for agricultural and water resources monitoring
2. Develop AI-driven software for complex data classification
3. Support climate resilience and environmental protection
Our research translates satellite optical data into actionable science that supports agricultural and water resources management.
Our core vision is to use new algorithms to process and analyze satellite remote sensing data, producing reliable results of image classification, time series analysis, bio-optical modeling and more.
Agricultural management
Agricultural structural conservation practices (SCPs) are designed to preserve soil and water resources in agricultural fields. Some examples are i) grassed waterways, ii) contour buffer strips, iii) terraces, iv) filter strips, v) riparian buffers, and others. These practices are typically implemented in the most sensitive areas (e.g., highly erodible lands). The accurate mapping of structural conservation practices becomes crucial for the spatial overview of current practices and their function in the agricultural landscape.
Land cover mapping
Land cover is one of the critical descriptors of the Earth’s terrestrial surface, and influences the energy balance, carbon budget, and hydrological cycle as many physical characteristics change such as albedo, emissivity, photosynthetic capacity, and transpiration. In remote sensing, land cover classification refers to the process of assigning a pixel value to specific land cover classes (e.g., open water, forest, developed, shrub, grassland, cultivated crops).
Land cover
Agriculture
Coastal waters
Burned area
aerosols
Earth Observation | Advanced Image Processing | AI-driven analytics
Coastal waters: algal blooms
Algal blooms, defined as a rapid, large-scale accumulation of micro- or macroalgae in the upper water column, can occur across diverse coastal environments, with more than 5,000 species documented globally. Satellite ocean color remote sensing provides multi-spectral datasets for detecting and tracking algal bloom events, documenting when and where they occur, and making it possible to understand and monitor their spatial and temporal dynamics
Aerosol optical depth
Aerosols are suspended solid and liquid particles in the atmosphere derived from natural and anthropogenic sources. Common natural sources are desert dust, volcanoes, wildfire, sea salt, and biogenic compounds from vegetation, while anthropogenic sources include biomass burning from logging and agricultural areas, fossil fuel combustion, and industrial pollution. Many efforts have been made to understand the aerosol physical, chemical, and optical properties, as well as aerosol-cloud interaction and impacts on hydrologic cycle.
Change detection: burned area mapping
Wildland fires have substantial effects on terrestrial ecosystems and greenhouse gas emissions with a recent apparent surge of destructive fires causing social disruption and economic costs. Satellite data have been used for several decades to monitor fire, by detecting the locations of actively burning fires and by mapping the spatial extent of the area affected by fire, usually referred to as the “burned area”.
Ag Conservation
Agricultural mapping
The use of cropland maps is crucial for global food security as it enables the quantification of annual food production, yield forecasting, and production trends, including agricultural area expansions or reductions. Accurate crop type maps support further public and private initiatives that enhance agricultural productivity and sustainability, making crop maps indispensable information for addressing food availability and supply uncertainties.
water vapor
Columnar water vapor
Water vapor is the most abundant greenhouse gas in the Earth-atmosphere system and plays a significant role in the Earth's climate. In addition, atmospheric water vapor content is a key factor defining the local humidity, cloud formation, and hydrological cycle, especially in the precipitation regimes.
Landsat Gallery
