Rasmus Houborg

Senior Scientist
Office Phone Number: 

PhD., Department of Geography, University of Copenhagen, Denmark. 2006
MSc., Department of Geography, University of Copenhagen, Denmark and University of Edmonton, Canada. 2002
BSc., Department of Geography, University of Copenhagen, Denmark. 1999

Research Interests: 
Satellite remote sensing, CubeSats, UAVs, plant biophysical traits and function, machine-learning, precision agriculture

Dr. Rasmus Houborg is a Senior Scientist in the Geospatial Sciences Center of Excellence (GSCE) and an Assistant Professor with a tenure home in the Department of Geography. Before coming to GSCE, he spent 4 years (2007-2011) in the United States working first as a Post-doctoral scientist at the USDA-ARS Hydrology and Remote Sensing Laboratory and then as a Research Associate in the Hydrological Sciences Branch at the NASA Goddard Space Flight Center in Maryland. He also spent two years (2011-2013) at the European Commission Joint Research Centre in Italy, and five years (2013-2017) at the King Abdullah University of Science and Technology (KAUST) working as a Research Scientist in the Hydrology and Land Observation (HALO) group.

Dr. Houborg's research is focused on advancing the utility and integration of multi-scale and multi-sensor remote sensing data for land surface characterization, including novel interpretation of multi- and hyper-spectral signals and translation into meaningful biophysical quantities (e.g., leaf area index, photosynthetic pigment contents, water and carbon fluxes). Dr. Houborg attempts to optimize the observing potential (i.e., spatio-temporal enhancement) through synergistic utilization and interpretation of consistent data streams from a variety of complementary satellite sensors using data-driven (i.e., machine-learning) approaches. This includes directed research into realizing the full potential of new sensing platforms such as Unmanned Autonomous Vehicles (UAVs) and constellations of small nano-satellites (i.e., CubeSats), which offer exciting opportunities in the context of precision agriculture and near real-time detection of rapidly changing land surface conditions.

Google Scholar Researchgate Sensor timelines

Timelines of historical and planned multi- and hyperspectral optical and thermal satellite sensors relevant for remote sensing of vegetation at medium to very high spatial resolution (Houborg et al., 2015). Recently (2017-) constellations of CubeSats have emerged as a way forward to realize needed enhancements in spatial resolution, revisit time and large area data availability.

NDVI CubeSats

Ultra-high resolution timeseries of 'raw' (top panel) and 'corrected' (lower panel) Planet CubeSat NDVI in comparison with 8-day atmospherically corrected (6SV) Landsat 8 NDVI over an irrigated alfalfa site in Saudi Arabia. The Cubesat Enabled Spatio-temporal Enhancement Method (CESTEM; Houborg and McCabe, 2018) was used to correct for noise in the CubeSat timeseries data and produce Landsat 8 consistent estimates. Constellations of CubeSats offer an unprecedented capacity to observe rapidly changing land surface conditions.

LAI CubeSats

Ultra-high resolution timeseries of Planet CubeSat LAI in comparison with Landsat 8 LAI over an irrigated alfalfa site in Saudi Arabia. The Cubesat Enabled Spatio-temporal Enhancement Method (CESTEM; Houborg and McCabe, 2018) was used to translate the raw CubeSat RGB+NIR data into Landsat 8 consistent LAI estimates. The daily CubeSat observations capture rapid changes in LAI missed by the 8-day Landsat retrievals with a factor of 10 increase in spatial resolution.

======================================================================================================================= RECENT JOURNAL PUBLICATIONS

Houborg R. and McCabe M.F. (2018) “A Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data”, Remote Sensing of Environment (in press). Houborg R. and McCabe M.F. (2018) “A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning”, ISPRS Journal of Photogrammetry and Remote Sensing, 135, 173-188 (Link). McCabe, M.F., Aragon, B., Houborg, R., Mascaro, J. (2017), “CubeSats in hydrology: Ultrahigh-resolution insights into vegetation dynamics and terrestrial evaporation”, Water Resources Research, 53 (Link). Shah S. H., Houborg R., McCabe M.F. (2017) “Response of Chlorophyll, Carotenoid and SPAD-502 Measurement to Salinity and Nutrient Stress in Wheat (Triticum aestivum L.)”, Agronomy, 7, 61 (Link). Rosas, J., Houborg, R., McCabe, M.F. (2017), “Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems”, Remote Sensing, 9(10), 988 (Link). Houborg R. and McCabe M.F. (2017) “Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances”, Remote Sensing of Environment, 194, 127-145 (Link). McCabe M.F., Rodell M., Alsdorf D.E., Miralles D.G., Uijlenhoet R., Wagner W., Lucieer A., Houborg R., Verhoest N., Franz T.E., Shi J., Gao H. and Wood E.F. (2017) “The Future of Earth Observation in Hydrology”, Hydrol. Earth Syst. Sci., 21, 3879-3914 (Link). Lopez O., Houborg R., McCabe M.F. (2017) “Evaluating the hydrological consistency of evaporation products using satellite-based gravity and rainfall data”, Hydrol. Earth Syst. Sci., 21, 323-343 (Link). Houborg R. and McCabe M.F. (2016) “High-Resolution NDVI from Planet’s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture”, Remote Sens., 8, 768 (Link). Houborg R. and McCabe M.F. (2016) “Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems”, Remote Sensing of Environment, 186, 105-120 (Link). Houborg R., McCabe M.F., Gao F. (2016) “A Spatio-Temporal Enhancement Method for coarse resolution LAI (STEM-LAI)”, International Journal of Applied Earth Observation and Geoinformation, 15-29 (Link). Houborg R., Fisher J., Skidmore A.K. (2015) “Advances in remote sensing of vegetation function and traits”, International Journal of Applied Earth Observation and Geoinformation, 43, 1-6 (Link). Houborg R., McCabe M.F., Cescatti A., Gitelson A.A. (2015) “Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems”, International Journal of Applied Earth Observation and Geoinformation, 43, 160-176 (Link). Houborg R., McCabe M., Cescatti A., Gao F., Schull M., Gitelson A. (2015) “Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)”, Remote Sensing of Environment, 159, 203-221 (Link). Schull M.A., Anderson M.C., Houborg R., Gitelson A., Kustas W.P. (2015) “Thermal-based modeling of coupled carbon, water and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations”, Biogeosciences, 12, 1511-1523 (Link). Gao F., Anderson M.C., Kustas W.P., Houborg R. (2014) “Retrieving leaf area index from landsat using MODIS LAI products and field measurements”, IEEE Geoscience and Remote Sensing Letters, 11 (4), art. no. 6595584, pp. 773-777 (Link). Anderson M.C., Kustas W.P., Hain C.R., Cammalleri C., Gao F., Yilmaz M.T., Mladenova I.E., Otkin J., Schull M., and Houborg R. (2013) “Mapping surface fluxes and moisture conditions from field to global scales using ALEXI/DisALEXI”, In Remote Sensing of Land Surface Turbulent Fluxes and Soil Surface Moisture Content: State of the Art, CPC Press, Taylor & Francis. Migliavacca M., Dosio A., Camia A., Houborg R., Houston-Durrant T., Kaiser J.W., Khabarov N., Krasovskii A.A., Marcolla B., Miguel-Ayanz J.S., Ward D.S., Cescatti A. (2013), “Modeling biomass burning and related carbon emissions during the 21st century in Europe”, J. Geophys. Res. Biogeosci., 118, 1732-1747 (Link). Boegh E., Houborg R., Bienkowski J., Braban C.F., Dalgaard T., van Dijk N., Dragosits U., Holmes E., Magliulo V., Schelde K., Di Tommasi P., Vitale L., Theobald M.R., Cellier P., and Sutton M.A. (2013) “ Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes ”, Biogeosciences, 10, 6279-6307 (Link). Houborg R., Cescatti A., Migliavacca M. and Kustas W.P. (2013) “Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP”, Agricultural and Forest Meteorology, 177, 10-23 (Link). Migliavacca M., Dosio A., Kloster S., Ward D.S., Camia A., Houborg R., Durrant T.H., Khabarow N., Krasovskii A.A., San Miguel-Ayanz J., and Cescatti A. (2013) “Modeling burned area in Europa with the Community Land Model”, Journal of Geophysical Research, 118, 265-279 (Link). Houborg R., Rodell M., Li B., Reichle R., and Zaitchik B.F. (2012) “Drought indicators based on model assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations”, Water Resources Research, 48, W07525 (Link). Houborg R., Anderson M.C., Daughtry C.S.T., Kustas W.P., and Rodell M. (2011) “Using leaf chlorophyll to parameterize light-use-efficiency within a thermal-based carbon, water and energy exchange model”, Remote Sensing of Environment, 115, 1694-1705, (Link).


Last modified: 
Jan 15, 2019