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
Satellite image processing is our core mission
Advance image processing
Atmospheric correction
Virtual constellation
Synthetic data
Atmospheric correction
Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations. Martins, V. S., Soares, J. V., Novo, E. M., Barbosa, C. C., Pinto, C. T., Arcanjo, J. S., & Kaleita, A. (2018).
Analysis Ready Data
Analysis Ready Data
Sentinel-3 Coastal Analysis Ready Data (S3CARD): An operational framework for coastal water applications
Caballero, C. B., Martins, V. S., Paulino, R. S., Lima, T. M., Butler, E., & Sparks, E. (2025).
Virtual constellation
AQUAVis: Landsat-Sentinel Virtual Constellation of Remote Sensing Reflectance (Rrs) Product for Coastal and Inland Waters.
Lima, T. M., Martins, V. S., Paulino, R. S., Caballero, C. B., Barbosa, C. C., & Ashapure, A. (2025).
Sampling strategy
Sampling strategy
Impact of sampling techniques on crop type mapping using multi-temporal composites from Harmonized Landsat-Sentinel images
Aires, U. R., Martins, V. S., Ferreira, L. B., Huang, Y., Heintzman, L., & Ouyang, Y. (2025).
Synthetic data generation
Generation of robust 10-m Sentinel-2/3 synthetic aquatic reflectance bands over inland waters.
Paulino, R. S., Martins, V. S., Novo, E. M., Barbosa, C. C., Maciel, D. A., Wanderley, R. L. D. N., ... & Lima, T. M. (2025).
Satellite validation
Satellite product validation
PACE (Plankton, Aerosol, Cloud, ocean Ecosystem): Preliminary analysis of the consistency of remote sensing reflectance product over aquatic systems. Paulino, R. S., Martins, V. S., Caballero, C. B., Lima, T. M., Liu, B., Ashapure, A., & Werdell, J. (2026).
Applications
Land cover
Land cover mapping
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution. Kaleita, A. L., Gelder, B. K., da Silveira, H. L., & Abe, C. A. (2020).
Agriculture
Agricultural mapping
FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery.
Ferreira, L. B., Martins, V. S., Aires, U. R., Wijewardane, N., Zhang, X., & Samiappan, S. (2025).
Burned area
Burned area mapping
Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope
Martins, V. S., Roy, D. P., Huang, H., Boschetti, L., Zhang, H. K., & Yan, L. (2022).
Ag Conservation
Agricultural conservation
Digital mapping of structural conservation practices in the Midwest US croplands: Implementation and preliminary analysis
Martins, V. S., Kaleita, A. L., & Gelder, B. K. (2021).
Aerosols
Aerosol optical depth
Validation of high‐resolution MAIAC aerosol product over South America.
Martins, V. S., Lyapustin, A., de Carvalho, L. A., Barbosa, C. C. F., & Novo, E. M. L. D. M. (2017).
Algal bloom
Coastal algal bloom mapping
The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions.
Caballero, C. B., Martins, V. S., Paulino, R. S., Butler, E., Sparks, E., Lima, T. M., & Novo, E. M. (2025).
Water vapor
Columnar water vapor
Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations. Martins, V. S., Lyapustin, A., Wang, Y., Giles, D. M., Smirnov, A., Slutsker, I., & Korkin, S. (2019).
Landsat Gallery
