Lithium (Li) is growing in importance and demand in several industrial applications, such as portable electric devices, electric vehicles, and hybrid electric vehicles. Li-rich pegmatites are one of the main sources of Li production in the world, so low-cost and high-efficiency exploration using remote sensing has become an important means for promoting the discovery of lithium resources. Although imaging spectroscopy has great potential for Li-rich pegmatites identification and regional delineation, due to the limitations of resolution and data acquisition, the exploration of the Earth’s surface at various scales on a variety of platforms requires further research. The purpose of this study is to use a ground-based HySpex imaging hyperspectrometer to map pegmatite zones directly, developing a new approach for mineralized pegmatites exploration. To achieve this, we present the Minimum Wavelength Mapper (MWM), which combines the position and depth information of the deepest absorption feature, to give a per-pixel overview map of the Jingerquan Li-Be-Nb-Ta pegmatite deposit pit and heap. The results show that images between 2100 nm and 2450 nm provide useful information about mineral assemblages that display strong spectral features, such as Li-rich pegmatites, Li-poor pegmatites and alteration, and their spatial distribution at the surface in areas covered by the imagery. The wavelength position and the depth of Al-OH can provide an overview of the mineral assemblages and abundances, to rapidly classify possible regions of interest for further analysis.

Several techniques, such as chemical methods and inductively coupled plasma mass spectrometry (ICP-MS), are available to accurately determine element content. However, they are time-consuming, labor-intensive, or expensive. Portable X-ray fluorescence spectrometry (pXRF) can be applied in various scenarios, with significantly higher efficiency and cost-effectiveness than laboratory methods. However, it also has limitations such as lower detection capability, relatively high detection limits, and lower accuracy than laboratory methods. In this study, we focused on applying pXRF to determine the elemental content of sediment samples and investigate its use in mineral exploration. A variety of factors influencing the results of pXRF analysis were analyzed. Our results showed that pXRF could detect more than 30 elements in stream sediments. The reliability of pXRF’s measurements was affected by factors such as the kind of element, sediment particle size, sample grinding treatment, count time, averaged element content, standard deviation of content, and range of content variation. The combination of pXRF analysis and laboratory analysis of partial samples is adequate for establishing a multi-element content inference equation. With this equation, it is possible to effectively infer the content gradient of elements, which will provide valuable support for mineral resource exploration.

Rock geochemical methods are effective for geological surveys, but typical sampling and laboratory-based analytical methods are time-consuming and costly. However, using visible–near-infrared spectroscopy to estimate the metal element content of rock is an alternative method. This study discussed the potential of hyperspectral estimation of Cu and its significant associated elemental content. Ninety-five rock samples were collected from the Kalatage Yudai copper–nickel deposit in Hami, Xinjiang. The effects of different spectral resolutions, spectral preprocessing, band indices, and characteristic band selection on the estimation of the element contents of Fe, Cu, Co, and Ti were investigated. The results show that when the spectral resolution is 5 nm, good results are obtained for all four metal elements, Fe, Cu, Co, and Ti, with the coefficients of determination R² reaching 0.54, 0.59, 0.41, and 0.78, respectively. The best results are obtained for all transformed spectra with continuum removal, inverse transformation, continuum removal, and logarithmic transformation, respectively. In addition, the accuracy of the estimation models constructed by combining band indices and feature band selection was superior compared with full-band spectra for Fe (R² = 0.654, MAE = 1.27%, and RPD = 1.498), Cu (R² = 0.694, MAE = 20.509, and RPD = 1.711), Co (R² = 0.805, MAE = 2.573, and RPD = 2.199), and Ti (R² = 0.501, MAE = 0.04%, and RPD = 1.412). The results indicate that using band indices can provide a more accurate estimation of metal element content, providing a new technical method for the efficient acquisition of regional mineralization indicator element content distribution.

Remote sensing technology has significant technical advantages over traditional geological methods in geological mapping and mineral resource exploration, especially in high-altitude and steep topography areas. Geochemical sampling and geological mapping methods in these areas are difficult to use directly in mountainous regions such as West Kunlun. Therefore, in the face of Li-Be-Nb-Ta mineralization of the Dahongliutan rare-metal pegmatite deposit in West Kunlun, remote sensing has become an effective means to identify areas of interest for exploration in the early stage of the exploration campaigns. Several methods have been developed to detect pegmatites. Still, in this study, this methodology is based on spectral analysis to select bands of the ASTER and Landsat-8 OLI satellites, and methods, such as principal component analysis (PCA) and mixture tuned matched filtering (MTMF), to delineate the prospective areas of pegmatite. The results proved that PCA could map the hydrothermal alteration and structure information for pegmatites. To define new locations of interest for exploration, we introduced the spectra of spodumene-bearing pegmatites and tourmaline-bearing pegmatites as endmembers for the MTMF approach. The results indicate that the location of pegmatite areas on the ASTER and Landsat-8 OLI images overlaps with the ore deposits, and the location of potential ore-bearing pegmatites is delineated using remote sensing and geological sampling. Although this does not guarantee that all prospective areas have the mining value of ore-bearing pegmatites, it can provide basic data and technical references for early exploration of Li.

Quickly determining the metal content in plants and subsequently identifying geochemical anomalies can provide clues and guidance for predicting the location and scale of concealed ore bodies in vegetation-covered areas. Although visible, near-infrared and shortwave infrared (VNIR–SWIR) reflectance spectroscopy at wavelengths ranging from 400 to 2500 nm has been proven by many researchers to be a fast, accurate and nondestructive approach for estimating the contents of copper (Cu), lead (Pb), zinc (Zn) and other metal elements in plants, relatively few studies have been conducted on the estimation of lithium (Li) in plants. Therefore, the potential of applying VNIR–SWIR spectroscopy techniques for estimating the Li content in plants was explored in this study. The Jingerquan Li mining area in Hami, Xinjiang, China, was chosen. Three sampling lines were established near a pegmatite deposit and in a background region, canopy reflectance spectra were obtained for desert plants and Li contents were determined in the laboratory; then, quantitative relationships were established between nine different transformed spectra (including both integer and fractional orders) and the Li content was estimated using partial least squares regression (PLSR). The results showed that models constructed using high-order derivative spectra (with an order greater than or equal to 1) significantly outperformed those based on original and low-order derivative spectra (with an order less than 1). Notably, the model based on a 1.1-order derivative spectrum displayed the best performance. Furthermore, the performance of the model based on the two-layer wavelet coefficients of the 1.1-order derivative spectrum was further improved compared with that of the model based on only the 1.1-order derivative spectrum. The coefficient of determination ($R_{pre}^2$) and the ratio of performance to deviation (RPD) for the validation set increased from 0.6977 and 1.7656 to 0.7044 and 1.8446, respectively, and the root mean square error (RMSEpre) decreased from 2.5735 to 2.4633 mg/kg. These results indicate that quickly and accurately estimating the Li content in plants via the proposed spectroscopic analysis technique is feasible and effective; however, appropriate spectral preprocessing methods should be selected before hyperspectral estimation models are constructed. Overall, the developed hybrid spectral transformation approach, which combines wavelet coefficients and derivative spectra, displayed excellent application potential for estimating the Li content in plants.

Optical remote sensing has become an important tool for prospecting in the early stages of geological exploration for rare metal deposits. This analysis enables the identification and mapping of lithium (Li)-bearing minerals using multisource and multiscale remote sensing data. However, the mineral composition of granite pegmatite is complex, and the unique spectral characteristics caused by Li in the mineral structure are still uncertain, which also causes problems of low accuracy and a small range in remote sensing prospecting. Therefore, the purpose of this article is to investigate the scattering properties of granite pegmatite and lay the foundation for the subsequent analysis of grain size and mineral abundance inversion using the Hapke model. First, the relationship between particle size and bidirectional reflectance distribution function response is analyzed. Then, the relationship between the single scattering albedo (SSA) and the particle size and the endmember mineral abundances is extended to the qualitative assessment and quantitative inversion by HySpex imaging hyperspectroscopy. The following results are obtained: 1) a linear relationship between the SSA and the particle size in the characteristic zone is established to evaluate the ore-bearing property, and 2) the abundance of endmember minerals can be estimated by using the photometric parameters obtained from multiangle measurements as the input for the Hapke model. Therefore, the inversion model based on the “multiangle–multisource–multiscale” physicochemical characteristics and mineral abundance of granite pegmatite proposed in this article can provide a new reference for the exploration of Li deposits.