Makhan L Virdi

Makhan Virdi

Makhan L Virdi

R&D Associate Staff and ORNL MODIS Tools Lead


Dr. Makhan Virdi is a researcher at NASA’s Distributed Active Archive Center for biogeochemical dynamics at the Oak Ridge National Laboratory (ORNL). Dr. Virdi has a background in Water Resource Modeling, Soil Physics, Remote Sensing, Biogeochemical Modeling, and Environmental Sciences.

Dr. Virdi joined the Environmental Sciences Division in September 2013. He received a Ph.D. in Water Resources from the University of South Florida (USF Tampa) in 2013, and a BS (B.Tech) in Civil Engineering from Indian Institute of Technology (IIT), Roorkee, India. In India, Dr. Virdi worked at the Center for Remote Sensing (IIT Roorkee) on landslide hazards using satellite data. He also worked at the Microwave Remote Sensing laboratory (IIT Bombay) on IRS-P4 satellite data to determine snow/ice for the Himalayan region using the brightness temperature (Tb).

While at USF, he worked for the U.S. Geological Survey (USGS) from 2007 to 2010 studying surface-groundwater interactions by modeling long- term climatic effects on a karst lake. His graduate research included geospatial analysis, time-series analysis, hydrological modeling, variably-saturated flow modeling, and numerical simulations. His current research interests include analytics, management and visualization of geospatial and time-series data. Currently, he is developing subsetting and visualization tools for MODIS data from NASA’s Terra and Aqua satellites. It received the Doug D. Nebert National Spatial Data Infrastructure (NSDI) Champion of the Year Award (2016) awarded by the United States’ Federal Geographic Data Committee (FGDC). These subset data, tools, and visualizations are used by researchers from a variety of disciplines to study ecology, earth science, and global change.


  • Ph.D. Civil and Environmental Engineering, University of South Florida, Tampa, USA (2013)
  • M.S. Civil Engineering, University of South Florida, Tampa, USA (2008)
  • B.Tech. Civil Engineering, Indian Institue of Technology Roorkee, India (2004)


Doug D. Nebert National Spatial Data Infrastructure (NSDI) Champion of the Year Award for developing an innovative suite of tools for using MODIS data sets. 


MODIS Land Products Subsets

Moderate Resolution Imaging Spectroradiometer (MODIS) land data products from NASA’s Terra and Aqua satellites are used by researchers from a variety of disciplines to study ecology, earth science, and global change. This tool prepares and distributes user-defined subsets of selected MODIS Land Products at a scale useful for field researchers in simpler format. The popular, award-winning tool (MODIS Global Subsetting and Visualization Tool), including support for MODIS Collection 6 land products. It provides satellite data subsets for any location using the Global Tool. It also provides the Web Service to obtain subsets in real time. Pre-processes subsets are also available for 1000+ field sites (Flux towers) worldwide using the Fixed Sites Tool. Learn more at MODIS Subsets.

Landscape-scale Crop Assessment Tool (LCAT)

Landscape-scale Crop Assessment Tool (LCAT) is a geo-informatics technology that enables field researchers, scientists and agronomists to forecast crop yields, identify pattern, and identify regions with conditions to support the adoption of specific technologies. The landscape-scale crop assessment tool (LCAT) was initiated as a collaborative effort between the Cereal Systems Initiative for South Asia (CSISA ) and the Oak Ridge National Laboratory (ORNL). Learn more at LCAT Project.

Groundwater-Surface Water Interactions

Modeling the interaction of surface water and groundwater to study the effect of climate extremes on a lake. Effects on daily groundwater exchanges with a lake over 10-year study period. This period included record dry and wet years with major hurricanes, El Nino, La Nina periods. Conceptual improvements to FORTRAN packages (LAK and UZF) of the USGS finite difference groundwater model (MODFLOW). Details: Groundwater Journal

Algorithm for planning safe route in landslide prone areas

Programmed algorithms in Java/C++ for planning shortest/safest route in areas susceptible to landslides, like young mountains of Himalayas and Alps. Implemented Dijkstras algorithm for shortest route. Satellite imagery used to determine nodal heights and hazard factors for assigning cost matrix for moving from one pixel to pixel. It utilized various input data layers, viz. DEM, landuse, and landslide hazard zonation using remotely sensed imagery. Details: IJGIS


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