Experimental studies of the influence of soil moisture and rainfall on the efficiency of the method for detecting oil pollution in the near-infrared range
Minh Bach Nguyen, Yu.V. Fedotov, N.V. Baryshnikov, M.L. Belov
Bauman Moscow State Technical University, Moscow, Russia
Keywords: optical remote sensing, Earth surface, detection of oil pollution, near-IR range
Abstract
The article is devoted to experimental study of hyperspectral method of detection of oil pollution on the earth surface in the near-IR range. The results of experimental measurements of spectral brightness coefficient of soil samples contaminated with various types of petroleum products in the spectral range 1.6-2.5 mm are given. The influence of soil moisture and rainfall on the spectra of reflection of soils (various types of sand and soil from forest and park areas) contaminated with petroleum products (Moscow and Samara oil processing plants, kerosene, gas condensate, various gasoline brands, motor oils, and diesel fuel) was studied. It was shown that spectral notches of about 1.73 and 2.3 mm (typical for soils contaminated with oil products) in most cases remain in the spectra of reflection under conditions of moderately wet soil, moderate “rain”, and even heavy “rain”. The results of the work of the created neural network show the probability of detecting oil pollution on the earth surface to be more than 99% under conditions of moderately moist soil and moderate “rain” and more than 88% under conditions of heavy “rain” and wet soil for 14 spectral channels with a resolution of 10 nm in the range 1.6-2.4 mm.
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