Accurate and detailed hydrological monitoring is essential for water resources management and field-scale decision making. Current satellite missions continue to improve the availability of information on hydrological processes, such as soil moisture, surface temperature, etc. However, their utility is limited by the large footprint of the sensors, which limits the direct application of these satellite data for field scale decision making.
Researchers at Princeton University have invented a merging framework that uses hyper-resolution land surface modeling to efficiently assimilate and downscale coarse resolution remotely sensed hydrological processes to a 30-m spatial resolution over continental domains. It can be used to develop very high resolution remotely sensed derived datasets (i.e. soil moisture, evapotranspiration, etc.), or it can be applied to improve current monitoring and forecast systems of these variables.
As a proof of concept, the researchers have evaluated this methodology for assimilating and downscaling microwave-based soil moisture retrievals from the Soil Moisture Active Passive Mission (SMAP). The downscaled dataset was evaluated against ~650 probes observations in the United States, and showed that the merged product captures well the spatial and temporal variability of the soil moisture dynamics, with overall improvement over SMAP estimates at the field and watershed scales.
• Improved estimate of agricultural yields and water demand at field scale
• Track impact of human activities such as irrigation and groundwater pumping
• Monitor spatial distribution of biological species and epidemic diseases
• Improve the forecast of droughts, wildfires, flooding, and landslides
• Use of semi-distributed modeling and HRUs allows for fine-scale estimates with high computational performance
• Low cost
• Global Coverage
• Frequent pass time
• Detailed information at very high resolution
Stage of Development
The researchers have tested this framework for assimilating and downscaling remotely sensed soil moisture retrievals from the Soil Moisture Active Passive mission. The merged soil moisture estimates were evaluated against ~650 independent ground in-situ observations. The evaluation was performed in terms of temporal Pearson correlation, bias ratio, coefficient of variation ratio, root mean squared error, and unbiased root mean squared error. The results show that the present framework can accurately represent the soil moisture spatial and temporal dynamics, with overall improvement over the current satellite estimates.
Noemi Vergopolan da Rocha is a Ph.D. candidate in the Civil and Environmental Engineering Department at Princeton University pursuing a certificate in Scientific Computing and Information Technology, and a certificate in Statistics and Machine Learning. She works on computational hydrology, and her research relies on combining high-resolution remote sensing observation from satellites with hydrological and machine learning models to improve hydrological monitoring and forecasting of extreme events, such as floods, droughts, irrigation water demands, and water scarcity at fine spatial scales.
Eric F. Wood is Professor Emeritus of Civil and Environmental Engineering Department of Princeton University. He received his Sc.D. in Civil Engineering from MIT in 1974, and his B.A.Sc. in Civil Engineering from University of British Columbia, Canada in 1970. Dr. Wood’s primary research interests are in hydroclimatology with emphasis on land-atmosphere interactions and hydrologic impacts from climate change using remote sensing, hydrological models and in-situ data. Among his honors, Dr. Wood received a Doctor Honoris Causa from Ghent University (Gent, Belgium), received the European Geosciences Union’s Alfred Wegener Medal and John Dalton Medal, American Meteorological Society’s Jules G. Charney Award, and the American Geophysical Union’s Robert E. Horton Medal. He has been elected to the U.S. National Academy of Engineering and is a Fellow of the Royal Society of Canada.
Intellectual Property Status
Patent protection is pending. Princeton is currently seeking commercial partners for the further development and commercialization of this opportunity.
Prabhpreet S. Gill
Princeton University Office of Technology Licensing • (609) 258-3653• firstname.lastname@example.org