Dr. Sachidananda Mishra works as Satellite Oceanographer at the National Centers for Coastal Ocean Science (NCCOS), NOAA. Dr. Mishra received his Ph.D. in Earth and Atmospheric Science from Mississippi State University, USA, and his M.S. (Research) in Ocean Engineering from the Indian Institute of Technology (IIT) Madras, India. His research interest lies in the aquatic remote sensing domain. He uses satellite remote sensing data, geospatial technologies, and physics-driven machine learning models to detect, monitor, and forecast cyanobacterial harmful algal blooms (cyanoHABs) in lakes, estuaries, and coastal oceans and understand their driving factors. His prior research focused on biophysical parameter estimation from high-resolution airborne hyperspectral data using machine learning and developing semi/quasi-analytical algorithms to quantify water quality parameters such as chlorophyll-a, phycocyanin, dissolved organic matter, and suspended sediment concentration from ocean color measurements in optically complex waters. He is a member of the American Geophysical Union (AGU) and The Oceanography Society.
Research Keywords & Expertise
Climate Change
Remote Sensing
Environmental Remote S...
optical oceanography
Remote sensing of coas...
Remote sensing and bio...
Bio-optical algorithm ...
Ocean optics and satel...
Physics-guided machine...
CyanoHABs
Fingerprints
50%
Remote Sensing
50%
Remote sensing of coastal and open ocean waters
50%
Remote sensing and bio-geochemistry of harmful algal bloom
15%
CyanoHABs
Short Biography
Dr. Sachidananda Mishra works as Satellite Oceanographer at the National Centers for Coastal Ocean Science (NCCOS), NOAA. Dr. Mishra received his Ph.D. in Earth and Atmospheric Science from Mississippi State University, USA, and his M.S. (Research) in Ocean Engineering from the Indian Institute of Technology (IIT) Madras, India. His research interest lies in the aquatic remote sensing domain. He uses satellite remote sensing data, geospatial technologies, and physics-driven machine learning models to detect, monitor, and forecast cyanobacterial harmful algal blooms (cyanoHABs) in lakes, estuaries, and coastal oceans and understand their driving factors. His prior research focused on biophysical parameter estimation from high-resolution airborne hyperspectral data using machine learning and developing semi/quasi-analytical algorithms to quantify water quality parameters such as chlorophyll-a, phycocyanin, dissolved organic matter, and suspended sediment concentration from ocean color measurements in optically complex waters. He is a member of the American Geophysical Union (AGU) and The Oceanography Society.