Andrew R. Michaelis

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Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP(More)
Application of remote sensing data to extrapolate evapotranspiration (ET) measured at eddy covariance flux towers is a potentially powerful method to estimate continental-scale ET. In support of this concept, we used meteorological and flux data from the AmeriFlux network and an inductive machine learning technique called support vector machine (SVM) to(More)
The generation of meteorological surfaces from point-source data is a difficult but necessary step required for modeling ecological and hydrological processes across landscapes. To date, procedures to acquire, transform, and display meteorological information geographically have been specifically tailored to individual studies. Here we offer a flexible,(More)
One of the characteristics of Earth Science data is their diversity, which results in a large number of similar algorithms tailored to a specific data set. Moreover, it is often difficult to connect several algorithms in a “pipeline” where output of one is the input of the other. The solution we propose in this paper is a flexible and an extensible(More)
Snow is important for water management, and an important component of the terrestrial biosphere and climate system. In this study, the snow models included in the Biome-BGC and Terrestrial Observation and Prediction System (TOPS) terrestrial biosphere models are compared against ground and satellite observations over the Columbia River Basin in the US and(More)
Quantum annealing is an experimental and potentially breakthrough computational technology for handling hard optimization problems, including problems of computer vision. We present a case study in training a production-scale classifier of tree cover in remote sensing imagery, using early-generation quantum annealing hardware built by D-wave Systems, Inc.(More)
In this study, we aim to generate global 30-m Leaf Area Index (LAI) from Landsat surface reflectance data using the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). Furthermore, canopy spectral invariants introduce an efficient way for incorporating(More)
The 30 meter spatial resolution of Landsat sensors makes it one of the most suitable datasets for bridging the gap between local measurements and large-scale studies of biophysical processes, yet its usage is limited by the availability of cloud-free surface observations. Methods have been proposed to combine Landsat data with measurements from sensors of(More)