Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-05-15T01:37:53.658Z Has data issue: false hasContentIssue false

SSA research on electromagnetic scattering in arid zone

Published online by Cambridge University Press:  21 April 2023

Fengyun Jiang*
Affiliation:
Yichun University, Physical Science and Technology College, Yichun, Jiangxi, 336000, China
*
Correspondence author: Fengyun Jiang; E-mail: fengyunjiang0930@126.com

Abstract

This paper investigates the electromagnetic scattering characteristics in arid zone and proposes methods for monitoring and improving ecological agriculture, vegetation landscape and desertification in arid zone, based on the modified small slope approximation (SSA) electromagnetic scattering model. The dielectric properties of dryland soils are studied in the paper, and the variation curves of soil dielectric constant with humidity and electromagnetic frequency are plotted. In order to reproduce the geomorphological features of the dryland areas, a two-dimensional geometric model of the ground environment is established using Monte Carlo methods combined with Gaussian spectral functions. An algorithm model based on the modified SSA is developed, and the algorithm was validated and analyzed to verify its reliability. In the simulations, the southeastern region of Ejina Banner, Inner Mongolia was taken as a typical arid region, and the simulation parameters are obtained from the actual measurement data of the region. The study finds that the electromagnetic scattering characteristics of the arid zone are influenced by soil water content, ground roughness and incident wave electromagnetic frequency, and there is regularity. The results of this paper can be effectively combined with remote sensing technology to contribute to many researches on soil moisture inversion and ecological condition of vegetation, which are important in many aspects such as irrigation control, ecological research, vorticity covariance, and climatology of slope stability.

Type
Research Paper
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press in association with the European Microwave Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Guo, QQ, Qu, JJ, Wang, GH, Xiao, JH and Pang, YJ (2015) Progress of wetland researches in arid and semi-arid regions in China. Arid Zone Research 32, 213220.Google Scholar
Guan, XD, Cheng, SJ, Guo, RX and Ji, MX (2014) Review of researches on numerical simulation of soil moisture over the arid and semi-arid region. Journal of Arid Meteorology 32, 135141.Google Scholar
Khanal, S, Klopfenstein, A, Kushal, KC, Ramarao, V, Fulton, J, Douridas, N and Shearer, SA (2021) Assessing the impact of agricultural field traffic on corn grain yield using remote sensing and machine learning. Soil & Tillage Research 208, 104880.CrossRefGoogle Scholar
Zhao, XW, Pan, S, Sun, ZC, Guo, HD, Zhang, L and Feng, K (2021) Advances of satellite remote sensing technology in earthquake prediction. Natural Hazards Review 22, 03120001.CrossRefGoogle Scholar
Nhamo, L, Ebrahim, GY, Mabhaudhi, T, Mpandeli, S, Magombeyi, M, Chitakira, M, Magidi, J and Sibanda, M (2019) An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing. Physics and Chemistry of the Earth 115, 102810.CrossRefGoogle Scholar
Sarala, D and Jacob, S (2014) Digital image processing – a remote sensing perspective. International Journal of Innovative Research and Development 3, 295300.Google Scholar
Li, JX, Zhang, M, Jiang, WQ and Wei, PB (2020) Improved FBAM and GO/PO method for EM scattering analyses of ship targets in a marine environment. Sensors (Basel, Switzerland) 20, 4735.CrossRefGoogle Scholar
Ross, G (2010) Electromagnetic scattering and its applications. Optica Acta: International Journal of Optics 29, 725.CrossRefGoogle Scholar
Rowell, RL and Stein, RS (1965) Electromagnetic scattering. Science (New York, N.Y.) 149, 1399.CrossRefGoogle ScholarPubMed
Tian, AM (2019) Simulation Method of EM Scattering from Layered Rough Surface and Its Application in Snow. Xidian University.Google Scholar
Comite, D and Pierdicca, N (2019) Monostatic and bistatic scattering modeling of the anisotropic rough soil. IEEE Transactions on Geoscience and Remote Sensing 57, 25432556.CrossRefGoogle Scholar
Johnson, JT and Ouellette, JD (2014) Polarization features in bistatic scattering from rough surfaces. IEEE Transactions on Geoscience and Remote Sensing 52, 16161626.CrossRefGoogle Scholar
Han, GH (2013) Soil Surface Moisture Inversion Research on Salt-Affected Soils by Polarimetric Radar in Arid Areas. Xingjiang University.Google Scholar
Xie, W (2019) Inversion of Soil Moisture in Arid Area Based on C- and L-Band SAR Images. Chang'an University.Google Scholar
Wang, R, Guo, LX and Wang, AQ (2010) Investigation of electromagnetic scattering interaction between the buried target and the rough surface in different types of soil. Acta Physica Sinica 59, 31793186.CrossRefGoogle Scholar
Caflisch, RE (1998) Monte Carlo and quasi-Monte Carlo methods. Acta Numerica 7, 149.CrossRefGoogle Scholar
Bourlier, C, Bergine, G and Saillard, J (2001) Theoretical study on two-dimensional Gaussian rough sea surface emission and reflection in the infrared frequencies with shadowing effect. IEEE Transactions on Geoscience and Remote Sensing 39, 379392.CrossRefGoogle Scholar