ACADEMICS

Nabin Malakar

Assistant Professor

Dr. Malakar has a wide ranging research interests including identification of relevant variables in physical phenomena, to improve the understanding of atmospheric processes, as well as algorithm development for societal applications of remote sensing data. Before joining the WSU, he worked in the NASA Jet Propulsion Laboratory/Caltech and developed global Land Surface Temperature and Emissivity products for thermal infrared sensors onboard MODIS Terra, MODIS Aqua, Landsat, and VIIRS satellites.

Education

2005
Tribhuvan University, Nepal
Physics
MS
2008
University at Albany, State University of New York
Physics
MS
2011
University at Albany, State University of New York
Physics
Ph.D.
Skills Physics, Remote Sensing, Earth Science, Artificial Intelligence

Achievements

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Benevolent Association Research Grant, SUNY, Albany
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Maximizing Utility of Remote Sensing Data for Water Quality Monitoring and Resources Management in California’s Water Systems
PI: Cristine Lee, NASA JPL/Caltech
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Travel Grant, CUNY Postdoctoral Travel Award, 2014, CCNY, NY.
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Publications

Service Projects

Editor

Special Issue of Journal Sustainability, MDPI, 2016

NASA-NSPIRES

NASA-NSPIRES Review panel member.

Robotics

Junior first LEGO League, University at Albany, SUNY.

STARS

Lakeview Community Foundation, STARS program.

Research

NASA Land Surface Temperature and Emissivity (PDF link)

NASA Land Surface Temperature and Emissivity (PDF link)

The MOD21 product was developed at JPL and is produced by the ASTER Temperature Emissivity Separation (TES) algorithm that was adapted to work with MODIS bands 29, 31 and 32. The MOD21 product addresses the problem of algorithm inconsistency between sensors, which makes intercomparisons difficult to interpret, introduces uncertainties when resampling data, and limits their usefulness in models and as Earth system data records which require consistent and accurate emissivities over all land cover types at a range of spatial, spectral and temporal scales.
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Estimating the global abundance of ground level presence of particulate matter (PM2. 5)

Estimating the global abundance of ground level presence of particulate matter (PM2. 5)

We used a suite of remote sensing and meteorological data products together with ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machine learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present.
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ASTER Global Emissivity Dataset v3

ASTER Global Emissivity Dataset v3

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using MODIS MOD07 atmospheric profiles and the MODTRAN 5.2 radiative transfer model.
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Courses

General Physics -I

General Physics -I


Environmental Science

Environmental Science