Assessing the impact of porosity variations on the reflectance and transmittance of natural sands

Abstract

Porosity is a fundamental characteristic of naturally occurring sand-textured soils, commonly referred to as natural sands, found in a wide range of landscapes, from beaches to dune fields. As a primary determinant of the density and permeability of sediments, it represents a pivotal element in geophysical studies involving basin modeling and the optical dating of sand deposits formed in areas subjected to erosion like coasts and deltas. It is also of interest for geoaccoustics and geochemical research on sediment transport and water diffusion properties of these deposits, as well as for agricultural and ecological investigations on the germination of light-sensitive seeds and the photochemical transformation of substances (e.g., pesticides) that may be present in these soils. Despite the importance of these applications, however, the remote estimation of porosity and the quantification of its effects on the light penetration profiles of natural sands remain elusive tasks. In this work, we tackle one of the major obstacles in this interdisciplinary area of research, namely the relative scarcity of experimental information due to technical constraints associated with traditional laboratory procedures. More specifically, we systematically examine the impact of porosity variations on the reflectance and transmittance of natural sands (in the 400 to 1000 nm region of the light spectrum) through controlled in silico experiments supported by measured data. Our findings are expected to strengthen the knowledge basis required for advances in this area and contribute to the development of technologies aimed at the effective monitoring and prediction of environmentally triggered changes affecting these soils.

SCU-Net: Semantic Segmentation Network for Learning Channel Information on Remote Sensing Images

“…At the same time, the neural network can automatically obtain the information contained in the pictures and accurately predict the category of unknown data [ 2 ]. Therefore, academia began to use neural networks to obtain the deep features in high-resolution remote sensing images and use them for satellite remote sensing image classification [ 3 ].…”