The Dunhuang murals are a precious treasure of China's cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive, circumventing issues of high costs, low efficiency. Building on this, the study constructs an inversion model for the Electrical Conductivity (EC) values of mural plaster subjected to phosphate erosion, through the integration of Fractional Order Differentiation (FOD), a novel three-band spectral index, and the Partial Least Squares Regression algorithm. The specific research contents include: (1) Initially, in preparation for the experiments, the materials used to create the samples underwent a rigorous desalting process, and phosphate solutions were prepared using deionized water to ensure uniform experimental conditions and the accuracy of the results. These meticulous preprocessing steps guaranteed that the measured EC values exhibited a clear correlation with the phosphate content. Subsequently, by employing qualitative experimental analysis techniques, this study was able to more accurately simulate the real-world scenarios of mural plaster affected by salt damage, enabling a deeper investigation into the mechanisms by which salts inflict microscopic damage to murals. (2) Explores the absorption mechanisms and characteristic spectral bands of the Electrical Conductivity (EC) values measured after the phosphate erosion of mural plaster. By integrating the optimal spectral indices, a univariate linear regression model is constructed, providing a basis for the rapid quantitative measurement of electrical conductivity in murals. (3) By comparing the accuracy of the Phosphate Simple Ratio (PSR) and Phosphate Normalized Difference Index (PNDI) spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study's findings include: (1) Surfaces of samples deteriorated by phosphate erosion formed numerous irregularly shaped crystal clusters, exhibiting uneven characteristics. (2) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (Q2) of 0.728. (3) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural electrical conductivity with a determination coefficient (Q2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

Soil heavy metal pollution caused by mining in mining areas seriously affects crop yield and causes human diseases. It is necessary to prevent soil heavy metal pollution from damaging health. Hyperspectral remote sensing can rapidly and dynamically acquire continuous spectra signals of ground objects, which provides a new idea for developing soil heavy metal content monitoring based on remote sensing. Aiming at the typical lead-zinc mining area in Laiyuan County, Hebei Province, soil samples from the mining area and surrounding areas are collected on-site, and the reflectance spectra of soil were obtained using SVC HR-1024i spectrometer (350 similar to 2 500 nm). Through the spectral data smoothing, first derivative (FD), multivariate scattering correction (MSC), standard normal variate transform (SNV), first derivative after multivariate scattering correction (MSC+FD), and first derivative after standard normal variatetrans form (SNV+FD), six kinds of spectral transformations were performed. The difference index (DI), ratioindex (RI), and normalized difference index (NDI) methods were used to extract the The optimal independent variables for different heavy metal elements were selected to increase the practical features of inversion modeling. Random forest algorithm and partial least squares regression method were used to establish prediction models for three heavy metals: cadmium (Cd), lead (Pb) and zinc (Zn) in soil. (Pb), and zinc (Zn). The R-2 of the optimal models reached 0.90, 0.91, and 0.84, respectively, which confirmed the validity of this research method. This study can provide a basis for the inversion modeling of soil heavy metal content in lead-zinc mining areas and a method reference for detecting soil heavy metal content in mining areas.

The Dunhuang murals are a precious treasure of China's cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive, circumventing issues of high costs, low efficiency. Building on this, our study developed a high-spectral feature inversion model for mural phosphate content using Fractional Order Differentiation (FOD), a novel three-band spectral index, and Partial Least Squares Regression (PLSR) algorithm. The specific research contents include: 1) Exploring the absorption mechanism of phosphates and their characteristic bands, combined with the optimal spectral index to construct a univariate linear regression model, providing a basis for rapid quantitative measurement of mural phosphate content. 2) By comparing the accuracy of the PSR and PNDI spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study's findings include:1) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (R-2) of 0.728. 2) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural phosphate content with a determination coefficient (R-2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

Aerosol particles of Black carbon in the snow cause a significant decrease in the albedo spectrum of the snow, which results in climatic radiation changes seriously, and will delay or advance the snow melting time, badly affecting the characteristics of surface runoff and processes of water cycle in the arid region. This problem is receiving increasing attention in ecological hydrology issues in the arid region. The data of field measurement were obtained by ASD spectrometer, Snow Folk and HR-1024 external field spectrum radiometer. SNICAR model was used to simulate the snow spectrum spectral characteristics under different parameters. Discussed the sensitivity of BC and snow particle size in different spectral ranges. The results showed that : In the snow spectral curve, the zenith angle changes from 0 degrees to 80 degrees, the albedo at 600 nm in the visible spectrum increases by 0. 045, and the albedo at 1 000, 1 200 and 1 300 nm in the near-infrared band increases by 0. 16, 0. 225 and 0. 249, respectively. The zenith angle is at 60 degrees, when snow particle size increases from 100 to 800 mu m, the albedo reduction can reach 0. 15, and snow particle size in the range of 100 similar to 300 mu m is significantly higher than the albedo in the range of 400 similar to 800 mu m. And the increase of the snow particle size can enhance the absorption effect of the light spectrum absorbing particles; Different BC concentrations have little effect on the spectral albedo in the near-infrared region, but are mainly concentrated in the visible light band. At 800 and 1 100 nm, the BC concentration of 5 mu g . g(-1) reduces the spectral albedo by 0. 13. The BC of 5 mu g . g(-1) can reduce the spectral albedo at 350 and 550 nm by 0. 25 and 0. 23. Compared with the different snow sizes, the decrease of BC concentration on the broad-band albedo of snow spectrum can be found in BC. In the case of the increase in the particle size of the snow, the light absorption effect of BC is increased, and at the higher concentration, the more the absorption increases; from the spectral index, the BC is sensitive in the visible light range of 350-740 nm, and the correlation coefficient is higher; The snow size is sensitive in the near-infrared band 1 100 similar to 1 500 nm, especially around 1 000 and 1 300 nm. The correlation between BC and snow particle size in the sensitive band of the snow spectral curve is high. Finally, the snow albedo simulated by the model is compared with the measured data. The R-2 is 0. 738, and the simulation effect is good. It can lay a data foundation for the study of the snow albedo in the arid region.