Remote Sensing of Water and Environment
We have built a unique indoor remote sensing facility over the flumes at the Saint Anthony Falls Laboratory (SAFL) to study the impacts of optically complex waters on reflectance signals of water and environmental pollution. In this remote sensing facility, we can study the effects of bedload, suspended sediments, algal biomass, waves, and foams as well as plastic debris on the reflectance signal. The system is equipped with a high-resolution ASD spectroradiometer, a source of light, and a DSLR camera that images the subgrid fraction of debris within the field of view.
Precipitation Microwave Passive Retrieval
(image credit, NASA)
Estimation of precipitation from space is one of the most important and challenging problems in earth remote sensing. It turns out that the upwelling earth radiation in microwave bands contains spectral signatures of precipitation profile at top of the atmosphere allowing us to measure global precipitation variability from space. In the past few years, our team has developed a modern passive retrieval algorithm to obtain improved estimates of precipitation from space using passive radiometric measurements provided by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. This algorithm relies on manifold learning via sparse approximation and promises improved retrieval of extreme and light precipitation, especially over land and at the vicinity of coastlines. Read more ...
Downscaling and Super-Resolution
Data Assimlation - Theory to Application
In remote sensing of natural processes, details of interest might not be fully captured due technical and cost constraints. Increasing the resolution of remote sensing imageries is of particular importance to obtain improved estimates of small-scale but important features of earth processes (e.g., precipitation extremes, tornadoes). Unlike the past efforts, via stochastic random generators for downscaling of geophysical fields, our team has developed downscaling methods, which rely on sparse approximation techniques. Applying our sparse downscaling algorithms, evidence suggests that the proposed sparse downscaling techniques can effectively recover high-resolution natural signals of interest (e.g., precipitation) while properly preserving their spatial coherency. Read more ...
Assimilation of observations into numerical models of environmental systems allows us to improve the estimates of their initial conditions and extend their forecast skills. Our team has contributed in developing modern theoretical frameworks in variational data assimilation in transform domains. The objective is to design data assimilation techniques that remain effective under limited number of sub-sampled observations (Ebtehaj et al., 2014). We have also been involved in large-scale assimilation of remotely sensed precipitation and soil moisture data into the land surface-atmosphere models. The research along this line aims to develop large-scale systems to assimilate satellite observations into coupled numerical models of land-atmospheric (see Lin et al., 2015) and crop models to optimize agricultural water management. Read more...
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