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Friday, May 2, 2025

CISESS Shines at Maryland Day

On 26 April, the University of Maryland in College Park welcomed the public to its springtime open house. CISESS succeeded in introducing some of the cool research conducted by its scientists, delighting the public along the way.

Friday, April 25, 2025

Evaluation of Satellite Data for Oyster Aquaculture Modeling

This study, coauthored by CISESS Scientist Ron Vogel, found that satellite data products and water quality data collected routinely by the Maryland Department of Natural Resources can replace on-site measurements taken at oyster farms as input to an oyster harvest model.

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Apr 24, 2025 09:21 AM

CISESS Announces 2025 Seed Grants

Deputy Director Hugo Berbery has just announced the CISESS Seed Grants to be funded in 2025. While four projects have been funded for the last four years, this year CISESS will have six Seed Grants for the first time. In addition, two prior seed grants, Hu Yang's Remote Sensing Lab and Guangyang Fan's 3D Virtual Reality Weather Maps, have now been given permanent funding.

Latent Heat Profile

Apr 23, 2025 02:31 PM

CISESS Seed Grant: Retrieving Latent Heat from Passive Microwave Satellite Observations

The goal of this CISESS Seed Grant Project is to demonstrate the feasibility of retrieving latent heating profiles of the atmospheric column using passive microwave satellite observations. This advance could potentially provide the foundation for unique cloud system analyses and new insights into cloud processes, potential for advances in numerical prediction, and a better understanding of energy and water budgets at global and regional scales.

PACE satellite

Apr 23, 2025 01:59 PM

CISESS Seed Grant: Machine Learning-based Hyperspectral Sensor Data Retrieval at the CISESS Remote Sensing Laboratory

The CISESS Seed Project takes hyperspectral data from NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). This data is processed with both supervised-learning and self-teaching machine learning to classify hyperspectral data to ocean composition and phytoplankton types. This approach will be validated using hyperspectral radiometer data from field and lab experiments using the CISESS Remote Sensing Lab (RSL) instruments.

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