The synthesis of statistical fractal models is justified for Spectral Data Processing. Many spectral sensor channels allow the use of methods for identifying distributions of spectral intensities and Rényi entropy, justifying the applicability of statistical fractal methods (multifractals) for modeling and subsequent interpretation of data, particularly plant responses to environmental changes. The derivation of multifractal models based on the Renyi information entropy is presented. The use of two multifractal measures of variability is substantiated. Multifractal models of hyperspectral characteristics of plants growing over oil deposits are presented. Examples of parameter computation of multifractal models based on real plant spectrograms are considered. Based on these results, measures of Rényi spectrum variability and multifractal spectra are determined, forming an indicative sign of oil deposit boundaries; prospective areas for oil development are forecasted. The discussed methods of computer multifractal modeling significantly expand the range of tasks addressable by established methods of spectral analysis based on spectral libraries. It has been observed that when studying complex self-organizing systems with fractal structures and power-law distributions, the application of Gibbs-Shannon entropy does not align well with observed phenomena. Describing systems with such properties is more appropriate using methods based on Rényi entropy.