Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia
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ORIGINAL ARTICLE
Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia Hazem T. Abd El‑Hamid1 · Guan Hong2 Received: 27 March 2020 / Accepted: 24 May 2020 © The Author(s) 2020
Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soiladjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area. Keywords Spectral characteristics band · Salt index · Vegetation index · Soil salinization
Introduction Soil salinization is a major problem to agricultural development, especially in arid and semiarid areas. It causes deterioration of the ecological environment, decline in crop yield, and threat to the ecological environment and the biosphere (Weng and Gong 2006). Soil salinization generally has strong spatial heterogeneity, so remote sensing technology has important application value in monitoring of this problem. Traditional methods require more samples; therefore, the quantity of soil samples affects directly the accuracy of the prediction of soil salinization (Hereher et al. 2010). Remote sensing uses the electromagnetic spectrum of the soil to quickly acquire extensive, multi-band, multi-temporal soil information. It is difficult to achieve accurate data by * Hazem T. Abd El‑Hamid [email protected] 1
Marine Pollution Department, National Institute of Oceanography and Fisheries, Alexandria 21556, Egypt
Ningxia Technical College of Wine and Desertification Prevention, Yinchuan 750021, China
2
relying solely on communication and ground transportation. Modern applications of remote sensing technology, geographic information system (GIS), and new technologies such as numerical simulations and mathematical models provide a new means for the investigation, calculation, simulation, analysis, evaluation, and prevention of
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