Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis

TL;DR

This paper introduces a robust multivariate Gaussianization method for estimating information-theoretic measures in high-dimensional Earth system data, enabling diverse applications like data synthesis, uncertainty quantification, and analysis of environmental variables.

Contribution

It presents a novel, statistically guaranteed Gaussianization technique for high-dimensional probability density estimation and information measure computation in Earth data analysis.

Findings

Successfully Gaussianized radar and hyperspectral data

Quantified information content in soil-vegetation variables

Analyzed temporal and spatial information scales in remote sensing

Abstract

Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable. However, accurately estimating information content is challenging because spatio-temporal data is high-dimensional, heterogeneous and has non-linear characteristics. In this paper, we apply multivariate Gaussianization for probability density estimation which is robust to dimensionality, comes with statistical guarantees, and is easy to apply. In addition, this methodology allows us to estimate information-theoretic measures to characterize multivariate densities: information, entropy, total correlation, and mutual information. We demonstrate how information theory measures can be applied in various Earth system data analysis problems. First we show how the method can be used to jointly Gaussianize radar backscattering intensities, synthesize hyperspectral data, and quantify of information content in aerial optical images. We also quantify the information content of several variables describing the soil-vegetation status in agro-ecosystems, and investigate the temporal scales that maximize their shared information under extreme events such as droughts. Finally, we measure the relative information content of space and time dimensions in remote sensing products and model simulations involving long records of key variables such as precipitation, sensible heat and evaporation. Results confirm the validity of the method, for which we anticipate a wide use and adoption. Code and demos of the implemented algorithms and information-theory measures are provided.