Design‐based empirical orthogonal function model for environmental monitoring data analysis

Abstract

An empirical orthogonal function (EOF) model is proposed as a prediction method for data collected over space and time. EOF models are widely used in a number of disciplines, including Meteorology and Oceanography. The appealing feature of this model is the advantage of not requiring any assumption for the covariance matrix structure. However, there is a need to account for the errors associated with the spatial and temporal features of the data. This is accomplished by incorporating information from the sampling design, used to establish the network, into the model. The theoretical developments and numerical solutions are presented in the first section of the paper. An application of the model to real data and the results of validation analyses are also presented.

High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau

“…It is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables (Hannachi et al, 2007;Munoz et al, 2008). REOF analysis is a useful method for statistical analysis of large data sets and has been widely used in meteorology and climate research.…”

Detection of Decreasing Vegetation Cover Based on Empirical Orthogonal Function and Temporal Unmixing Analysis

“…Moreover, the empirical orthogonal function method aims to reduce the dimensionality with a minimum loss of information while maintaining the majority of the variation affected by independent processes and capturing the essential features [36,37].…”

A practical approach to spatio-temporal analysis

“…is the weight for s i (Munoz, Lesser, and Ramsey, 2008). We employ the inverse distance weighting function with p ≤ d for ddimensional space (Shepard (1968)…”

“…For more discussion on the use of EOF method, see Munoz, Lesser, and Ramsey (2008). Under the normality assumption with known variance components, the variance of the prediction error is given by…”