EOFS

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AcronymDefinition
EOFSEmpirical Orthogonal Functions
EOFSEnvironmental Observation and Forecasting System
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Empirical Orthogonal Function. The empirical orthogonal function method decomposes the original data into the product of temporal function and spatial function [18, 33].
In the empirical orthogonal function method, the original data (X) is divided into the product of a temporal function (EOFs-V) and spatial function (PCs-Z) [34, 35]:
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].
In this analysis, the curves from empirical orthogonal function with decreasing trends are temporal patterns and represent the vegetation cover reduction.
The curves of empirical orthogonal function provide vegetation increasing/decreasing trend as prior information.
Second, here the curves of empirical orthogonal function provide vegetation decreasing prior information for temporal unmixing [16].
The first curve of empirical orthogonal function has primary eigenvalues, which contributes to approximately 89.25% of the variance (Figure 3(a)).
The amplitudes of the first ten curves of empirical orthogonal function could be quantified in the time domain (Figure 4).
The temporal curves of empirical orthogonal function provide prior information for the temporal unmixing model.
The blue part is vegetation decreasing area from empirical orthogonal function and temporal unmixing method.
According to decreasing vegetation pixels coincidence from the land use map and temporal unmixing analysis, the accuracy of empirical orthogonal function and temporal unmixing analysis is 83.14% (Table 1).
Khokhlov, "Using meteorological data for reconstruction of annual runoff series over an ungauged area: empirical orthogonal function approach to Moldova-Southwest Ukraine region," Atmospheric Research, vol.
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