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Research Highlights

These slides are for recent articles, announcements, meetings, and important outside links.

Hi-Res Soil Moisture from GNSS data

This CISESS Seed Project will use reflectivity and signal-to-noise observations from GNSS satellites like CYGNSS to develop high-resolution soil moisture products.

Hyperspectral Data to Find Phytoplankton

This CISESS Seed Project uses both supervised-learning and self-teaching machine learning to classify hyperspectral data to ocean composition and phytoplankton types in collaboration with the CISESS Remote Sensing Lab.

Wildfire Smoke & Early Health Warnings

This CISESS Seed Grant Project is to develop a wildfire smoke early-warning system using Earth observations, modelled data, and ground measurements to enable timely deployment of clinical support resources to high-risk areas in Maryland.

Latent Heat Profiles

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.

Daily High-Res Vegetation Maps

This CISESS Seed Grant Project focuses on balancing the spatial resolution of LEO satellites and the temporal resolution of GEO satellites to produce daily high-resolution vegetation datasets (10–30m).

A Stream Water Temperature Model

This CISESS Seed Grant Project will develop a Basic Model Interface (BMI) for stream water temperature modules, enabling seamless integration with NOAA’s Next Generation Water Resources Modeling Framework (NextGen).

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Published Date: 1/1/0001

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