A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery

Abstract

Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The Forced Invariance Approach has been proven able to effectively suppress the vegetation contribution to the mixed image pixel. It takes advantage of scene statistics and requires no specific a priori knowledge of the referenced spectra. However, the approach is still mainly limited to lithological mapping. In this case study, the objective was to test the performance of the Forced Invariance Approach to improve the estimation accuracy of soil salinity for an agricultural area located in the semi-arid region of Northwest China using airborne hyperspectral data. The ground truth data was obtained from an eco-hydrological wireless sensing network. The relationship between Normalized Difference Vegetation Index (NDVI) and soil salinity is discussed. The results demonstrate that the Forced Invariance Approach is able to improve the retrieval accuracy of soil salinity at a depth of 10 cm, as indicated by a higher value for the coefficient of determination (R 2 ). Consequently, the vegetation suppression method has the potential to improve quantitative estimation of soil properties with multivariate statistical methods.

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

“…It is based on information from red and near-infrared bands of the sensors without requiring any knowledge of lithological composition of the scene. Figure 5 illustrates the FIM [56,58]. The FIM was successfully applied in the geology field using hyper-and multispectral data [58].…”

“…Figure 5 illustrates the FIM [56,58]. The FIM was successfully applied in the geology field using hyper-and multispectral data [58]. The performance of the FIM approach was checked using two criteria: the false color image (by applying visual analysis) and the normalized difference vegetation index (NDVI).…”

Cartografía estructural y mineral mediante imagen multiespectral en el Alto Atlas Central de Marruecos

“…Lithological mapping is an important step in various mineral exploration studies as it forms the basis for the interpretation and validation of mining results. Lithological discrimination is done either in an automatic way using supervised machine learning algorithms such as SVM (Yu et al, 2012;Othman & Gloaguen 2014Liu et al, 2018) which is the most popular in lithological mapping using multispectral imagery or by traditional methods (Pournamdari et al, 2014;Kumar et al, 2015). In the case of our study, we chose to use satellite data to map lithological units in the Central High Atlas using the Principal Component Analysis method and the Optimal Index Factor method applied to multispectral data (Pournamdari et al, 2014;Tabeliouna et al, 2016;El Atillah et al, 2018).…”