Dr. Malakar is an associate professor in the Department of Earth, Environment and Physics. He is also serving as the Director of Aisiku STEM Center.
He has a wide-ranging research interest including societal applications of remote sensing data, land surface temperature, air pollution, application of machine learning to earth sciences etc. 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.
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.
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.
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.
We applied a new approach in generating NASA Land Surface Temperature and Emissivity products, and demonstrated that they were consistent across different satellite platforms (namely MODIS and VIIRS). This ensures well-characterized long-term LST&E data record for better monitoring and understanding trends in Earth system behavior.
Our study demonstrates the capability of using Landsat 8 TIRS and OLI sensors for the monitoring of SST and surface Chl-a concentrations at a high spatial resolution in nearshore waters and highlights the value of these sensors for assessing the environmental effects of wastewater discharge in a coastal environment.
