Sanath Kumar Sathyachandran

• Kumar, S. S., Tolk, B., Dittmeier, R., Picotte, J. J., La Puma, I., Peterson, B., & Hatten, T. D. (2024). The Spatially Adaptable Filter for Error Reduction (SAFER) Process: Remote Sensing-Based LANDFIRE Disturbance Mapping Updates. Fire, 7(2), 51.
• Kumar, S. S., Prihodko, L., Lind, B. M., Anchang, J., Ji, W., Ross, C. W., ... & Hanan, N. P. (2020). Remotely sensed thermal decay rate: an index for vegetation monitoring. Scientific Reports, 10(1), 1-11.
• Kumar, S. S., & Roy, D. P. (2018). Global operational land imager Landsat-8 reflectance-based active fire detection algorithm. International Journal of Digital Earth, 11(2), 154-178
• Kumar, S. S., Roy, D. P., Boschetti, L., & Kremens, R. (2011). Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations. Journal of Geophysical Research: Atmospheres, 116(D19).
• Kumar, S. S., Barlage, M., Zheng, W., Wei H., Gayno, G., Yang, F. Using Observed Spatially Varying Leaf Area Index to Drive NOAA UFS Land Model. In AGU Fall Meeting 2024
• Anchang J, Kumar , S. S., Prihodko, L., Kahiu , N , N., Geli, H, M, E., Hanan N. P.,. Improving Pre-Fire Analysis in the US Southwest: Satellite Measured Thermal Decay Rate (Rak) as an Indicator of Canopy Moisture Variability, Stress, and Fuel Cure. In AGU Fall Meeting 2024
• Kumar, S. S., Tolk, B., La Puma, I.P., and Hatten, T. Vegetation Disturbance Mapping Accuracies of the LANDFIRE Program Over the CONUS for 2013 – 2017. In AGU Fall Meeting 2022.
• Tilley, J. S., Bower, K. A., Kumar, S. S., Kucera, P. A., & Askelson, M. A.. On the Utility of a Modest Physics-Based High-Resolution WRF Ensemble for Hurricane Prediction: Hurricane Ivan as an Example. In AGU Spring Meeting 2005.

• Developed a streamlined process to generate ocean-land coupled grids for operational use in the NOAA EMC Unified Forecast Systems (UFS). Added critical ability to ingest new satellite derived land surface parameters into the model (Kumar, S.S.,2024).
• Developed the Spatially Adaptable Filter for Error Reduction (SAFER) algorithm (Kumar et al., 2024). It is currently the operational algorithm deployed in the United States Geological Survey's HPC environment to generate the LANDFIRE disturbance (2017 onwards) product suite.
• Developed a new index for monitoring vegetation using satellite observed thermal data (Kumar et al., 2020).
• Developed the first globally applicable algorithm (Kumar et al., 2018) for active fire detection using Landsat class reflectance data.
• Developed statistical models to compensate for the observed underestimation of satellite derived fire radiative power (Kumar et al., 2011)

• Satellite based active fire characterization. In forum for contemporary Geographic Issues 2023 online webinar for University of North Dakota, Geography and Geographic Information Science. April 19 2023.
• A Random Forest-Based Commission Error Filter for LANDFIRE Disturbance Mapping. In Full Speed Ahead: Increasing frequency and reducing latency of national-scale maps. Pecora 2022, Denver, Colorado
• Remote sensing of wildfires: A regional and global analysis. In Long Term Ecological Research (LTER) symposium 20th September 2017 Wotton hall, NMSU, New Mexico.
• The Global Operational Land Imager (GOLI) Landsat 8 reflectance based active fire detection algorithm. In summer 2016 Landsat Science Team Meeting was held at the South Dakota State University, South Dakota.

• Special edition editor
• “Advances in Characterizing and Monitoring Land Cover/Use and Associated Ecosystem Changes Using Remote Sensing Data” Frontiers in Environmental Science
• Session convener (AGU & Pecora)
• Remote Sensing of Fire Processes and Biomass Burning (BB) Emissions (2021 & 2022)
• Advances in Characterizing and Monitoring Land Cover/Use and Associated Ecosystem Changes Using Remote Sensing Data (2021 & 2022)
• Technical Session 7-2: Fire Detection, Monitoring, and Remediation (2022)
• Technical Session 8-2: Floods, Weather Events and Other Hazards (2022)
• Public outreach
• Amateur astronomy and amateur radio popularization in my local communities
• Conducted several telescope making workshops in India
• Volunteer subject matter expert and mentor for NASA Space App challenge

Interdisciplinary scientist with diverse and deep research to product development experience. I have developed classical and machine learning based algorithms to derive environmental and biophysical variables, and remote sensing solutions to monitor and address environmental issues.