Space-borne Hyperspectral Imagery Research Articles

Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery

Widespread soil contamination endangers public health and undermines global attempts to achieve the United Nations Sustainable Development Goals. Due to the lack of relevant studies and low precision of spaceborne spectroscopy, estimating soil heavy metal concentrations is challenging. In this study, we developed a coupled retrieval to qualify the heavy metal nickel (Ni) concentration in agricultural soil from spaceborne hyperspectral imagery. The retrieval couples spectral feature extraction from multi-scale discrete wavelet transform (DWT) and dimension reduction (DR), optimal band combination algorithm to five machine learning retrieval models using tree-based ensemble learning, neural network-based, and kernel-based. The comparison between the retrievals and Ni measurements shows that the DWT combined with t-distributed stochastic neighbor embedding (tSNE) coupled extreme gradient boosting (XGboost) retrieval model exhibited the best prediction for the validation dataset. Moreover, due to the integration of six statistical indicators of model performance and the fitted slope of the regression line, the retrieval framework can produce more robust and accurate predictions than those that rely on correlation coefficients. The demonstrated potential of spaceborne hyperspectral remote sensing to provide accurate quantitative measurements of soil heavy metal concentrations will serve as a reference for agricultural plot applications worldwide.

Comparing Gaofen-5, Ground, and Huanjing-1A Spectra for the Monitoring of Soil Salinity with the BP Neural Network Improved by Particle Swarm Optimization

Most of the world’s saline soils are found in arid or semiarid areas, where salinization is becoming serious. Ground laboratory hyperspectral data (analytical spectral devices, ASD) as well as spaceborne hyperspectral data, including Gaofen-5 (GF-5) and Huanjing-1A (HJ-1A), provide convenient salinity monitoring. However, the difference among ASD, GF-5, and HJ-1A spectra in salinity monitoring remains unclear. So, we used ASD, GF-5, and HJ-1A spectra as data sources in Gaotai County of Hexi Corridor, which has been affected by salinization. For a more comprehensive comparison of the three spectra datum, four kinds of band screening methods, including Pearson correlation coefficient (PCC), principal component analysis (PCA), successive projections algorithm (SPA), and random forest (RF) were used to reduce the dimension of hyperspectral data. Particle swarm optimization (PSO) was used to improve the random initialization of weights and thresholds of the back propagation neural network (BPNN) model. The results showed that root mean square error (RMSE) and determination of the coefficients (R2) of models based on ASD and HJ-1A spectra were basically similar. ASD spectra (RMSE = 4 mS·cm−1, R2 = 0.82) and HJ-1A (RMSE = 2.98 mS·cm−1, R2 = 0.93) performed better than GF-5 spectra (RMSE = 6.45 mS·cm−1, R2 = 0.67) in some cases. The good modelling result of HJ-1A and GF-5 data confirmed that spaceborne hyperspectral imagery has great potential in salinity mapping. Then, we used HJ-1A and GF-5 hyperspectral imagery to map soil salinity. The results of GF-5 and HJ-1A showed that extremely and highly saline soil mainly occurred in grassland and the southern part of arable land in Gaotai County. Other lands mainly featured non-saline and slightly saline soil. This can provide a reference for salinity monitoring research.

Tree Species Classification Based on Fusion Images by GF-5 and Sentinel-2A

Forest ecosystem detection and assessment usually requires accurate spatial distribution information of forest tree species. Remote sensing technology has been confirmed as the most important method for tree species acquisition, and space-borne hyperspectral imagery, with the advantages of high spectral resolution, provides a better possibility for tree species classification. However, the present in-orbit hyperspectral imager has proved to be too low in spatial resolution to meet the accuracy needs of tree species classification. In this study, we firstly explored and evaluated the effectiveness of the Gram-Schmidt (GS) Harmonic analysis fusion (HAF) method for image fusion of GaoFen-5 (GF-5) and Sentinel-2A. Then, the Integrated Forest Z-Score (IFZ) was used to extract forest information from the fused image. Next, the spectral and textural features of the fused image, and topographic features extracted from DEM were selected according to random forest importance ranking (Mean Decreasing Gini (MDG) and Mean Decreasing Accuracy (MDA)), and imported into the random forest classifier to complete tree species classification. The results showed that: comparing some evaluation factors such as information entropy, average gradient and standard deviation of the fused images, the GS fusion image was proven to have a higher degree of spatial integration and spectral fidelity. The random forest importance ranking showed that WBI, Aspect, NDNI, ARI2, FRI were more important for tree species classification. Both the classification accuracy and kappa coefficients of the fused images were significantly greatly improved when compared to those of original GF-5 images. The overall classification accuracy ranged from 61.17% to 86.93% for different feature combination scenarios, and accuracy of the selected method based on MDA achieved higher results (OA = 86.93%, Kappa = 0.85). This study demonstrated the feasibility of fusion of GF-5 and Sentinel-2A images for tree species classification, which further provides good reference for application of in-orbit hyperspectral images.

An assessment of image classifiers for generating machine-learning training samples for mapping the invasive Campuloclinium macrocephalum (Less.) DC (pompom weed) using DESIS hyperspectral imagery

Machine-learning algorithms may require large numbers of reference samples to train depending on the spatial and spectral heterogeneity of the mapping area. Acquiring these reference samples using traditional field data collection methods is a challenge due to time constraints, logistical limitations, and terrain inaccessibility. The aim of study was to assess how parametric, nonparametric, and spectral matching image classifiers can be used to generate a large number of accurate training samples from minimal ground control points to train machine-learning algorithms for mapping the invasive pompom weed using 30 m DESIS hyperspectral data. Three image classifiers, namely, maximum likelihood classifier (MLC), support vector machine (SVM) and spectral angle mapper (SAM) were selected to represent each of the three types of image classifiers under investigation in this study. Results show that the SAM, MLC and SVM classifiers had pixel-based classification accuracies of 87%, 73% and 67% for the pompom-containing pixels class, respectively. Furthermore, an independent field verification for the SAM classification was conducted yielding a 92% overall mapping accuracy for the pompom-containing pixels class. A total of 4000 pompom-containing and 8000 non-pompom-containing training samples were generated from an SAM classification that was trained using only 20 endmembers. Overall, this study presents a potential solution strategy that has significant implications for generating large numbers of reference training samples for mapping invasive alien plants from new generation spaceborne hyperspectral imagery using machine-learning algorithms.

Assessment of heavy metal pollution in agricultural soil around a gold mining area in Yitong County, China, based on satellite hyperspectral imagery

Soil is one of the essential natural resources that is at risk from heavy metal pollution. The traditional sampling method for soil heavy metal monitoring and assessment cannot meet the requirements for large-scale areas. The purpose of this study was to estimate the soil heavy metal concentrations based on Gaofen 5 (GF5) satellite hyperspectral imagery for the assessment of the heavy metal pollution in the study area and to analyze the scale effect under different resolutions. A total of 96 topsoil samples were collected in this work, and these samples were analyzed for the arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) contents. To solve the problem of the insignificant features caused by the complex imaging conditions of spaceborne hyperspectral satellite imagery, the binary weight symbiotic organisms search algorithm (BWSOS) was developed. After feature selection based on the BWSOS method, the heavy metal contents are inverted by the use of support vector machine regression. The experimental results show that the BWSOS feature selection method shows a good performance, with the Rp2 values for As, Cd, Cr, Cu, Ni, Pb, and Zn being 0.67, 0.68, 0.73, 0.71, 0.66, 0.65, and 0.71, respectively. Based on the estimated heavy metal concentration maps, the geoaccumulation index (Igeo), the pollution index, and the potential ecological risk index were calculated to assess the heavy metal pollution status in the study area. The results showed that only As contamination is present at a significant level, but with a low level of potential risk for the whole study area. A comparison with the results obtained using HyMap airborne hyperspectral imagery showed that the GF5 satellite hyperspectral imagery can obtain consistent results for heavy metal pollution assessment. The airborne hyperspectral imagery can provide more fine details, whereas the spaceborne hyperspectral imagery is more suitable for large-scale pollution assessment at a low cost.

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

Hyperspectral analysis of lithologies in the Arctic in areas with abundant lichen cover

Lithological mapping using remote sensing depends, in part, on the identification of rock types by their spectral characteristics. Chemical and physical properties of minerals and rocks determine their diagnostic spectral features throughout the electromagnetic spectrum. Shifts in the position and changes in the shape and depth of these features can be explained by variations in chemical composition of minerals. Detection of such variations is vital for discriminating minerals with similar chemical composition. Compared with multispectral image data, airborne or spaceborne hyperspectral imagery offers higher spectral resolution, which makes it possible to estimate the mineral composition of the rocks under study without direct contact. Arctic environments provide challenging ground for geological mapping and mineral exploration. Inaccessibility commonly complicates ground surveys, and the presence of ice, vegetation and rock-encrusting lichens hinders remote sensing surveys. This study addresses the following objectives: 1. Modelling the impact of lichen on the spectra of the rock substrate; 2. Identification of a robust lichen index for the deconvolution of lichen and rock mixtures and 3. Multiscale hyperspectral analysis of lithologies in areas with abundant lichens.

The use of hyperspectral remote sensing for mineral exploration: a review

The hyperspectral remote sensing technology has been available to the research community for more than three decades. Since in its first steps the hyperspectral technology was also promoted as a tool for mineral exploration. Numerous mineral exploration applications of hyperspectral remote sensing have been reported. This paper provides an up-to-date and focused review of the applications of the hyperspectral remote sensing to mineral exploration. The ore deposits are grouped based on major processes of formation (magmatic, hydrothermal, sedimentary, supergene). The review shows that the hyperspectral remote sensing technology has found application to the study and exploration of a number of ore deposits including kimberlites (host-rocks of diamonds), carbonatites (host-rock of rare earth elements deposits), porphyry deposits, epithermal gold and silver deposits, skarn deposits, volcanic-hosted massive sulfide (VHMS) deposits, orogenic gold deposits, Carlin-type gold deposits, SEDEX Pb-Zn-Ag deposits. On the other hand, the possibilities of the hyperspectral technology remain still underexplored for the study and exploration of chromite deposits, Ni-sulfide deposits in mafic and ultramafic rocks, rare-metal pegmatites, greisen and related deposits, Mississippi Valley-type (MVT) Pb-Zn deposits, Kupferschiefer deposits and uranium deposits in sedimentary basins, iron ores in banded-iron formations, laterites and bauxites. A special attention has been paid in this review to the applications in mineral exploration of the emerging airborne hyperspectral thermal infrared technology. In addition, the possibilities and limitations of spaceborne hyperspectral imagery of moderate spatial resolution for detailed characterization and detection of mineralized systems are discussed for each major deposit type. The spatial resolution of the hyperspectral data is noted to be a key factor on the success of a hyperspectral exploration project. By providing a full up-to-date picture of the applications and contribution of the hyperspectral imagery to the exploration and characterization of ore deposits this review paper should be useful to the interested geological remote sensing researcher and practitioner, and to the mineral exploration manager as well.

Research on the shortwave infrared hyperspectral imaging technology based on Integrated Stepwise filter

Space-borne hyperspectral imagery is an important tool for earth sciences and industrial applications. Higher spatial and spectral resolutions have been sought persistently, although this results in more power, larger volume and weight during a space-borne spectral imager design. For miniaturization of hyperspectral imager and optimization of spectral splitting methods, several methods are compared in this paper. Spectral time delay integration (TDI) method with high transmittance Integrated Stepwise Filter (ISF) is proposed.With the method, an ISF imaging spectrometer with TDI could achieve higher system sensitivity than the traditional prism/grating imaging spectrometer. In addition, the ISF imaging spectrometer performs well in suppressing infrared background radiation produced by instrument. A compact shortwave infrared (SWIR) hyperspectral imager prototype based on HgCdTe covering the spectral range of 2.0–2.5μm with 6 TDI stages was designed and integrated. To investigate the performance of ISF spectrometer, a method to derive the optimal blocking band curve of the ISF is introduced, along with known error characteristics. To assess spectral performance of the ISF system, a new spectral calibration based on blackbody radiation with temperature scanning is proposed. The results of the imaging experiment showed the merits of ISF. ISF has great application prospects in the field of high sensitivity and high resolution space-borne hyperspectral imagery.

Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects

A statistical relationship between canopy mass-based foliar nitrogen concentration (%N) and canopy bidirectional reflectance factor (BRF) has been repeatedly demonstrated. However, the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen. The canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF) has recently been suggested for estimating %N as it suppresses the canopy structural effects on BRF. However, estimation of %N using the scattering coefficient has not yet been investigated for longer spectral wavelengths (>855nm). We retrieved the canopy scattering coefficient for wavelengths between 400 and 2500nm from airborne hyperspectral imagery, and then applied a continuous wavelet analysis (CWA) to the scattering coefficient in order to estimate %N. Predictions of %N were also made using partial least squares regression (PLSR). We found that %N can be accurately retrieved using CWA (R2=0.65, RMSE=0.33) when four wavelet features are combined, with CWA yielding a more accurate estimation than PLSR (R2=0.47, RMSE=0.41). We also found that the wavelet features most sensitive to %N variation in the visible region relate to chlorophyll absorption, while wavelet features in the shortwave infrared regions relate to protein and dry matter absorption. Our results confirm that %N can be retrieved using the scattering coefficient after correcting for canopy structural effect. With the aid of high-fidelity airborne or upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling of ecosystem processes as well as ecosystem-climate feedbacks.

Short-wave infrared signature and detection of aicraft in flight based on space-borne hyperspectral imagery

Infrared signatures of aircraft are the basis for detection and monitoring. In past years, most of the studies focused on the aircraft’s infrared signature in the mid-wave spectral region and long-wave spectral region for missile guidance or aircraft survivability studies. For the security of civil aviation, methods and instruments that can detect and monitor aircrafts from space are expected to be developed in the coming years. A short-wave infrared hyperspectral imager aboard the Tiangong-1 spacecraft acquired some civil aircraft’s spectral data. The differences between the aircraft and the background in their spectral signatures are analyzed and discussed. Less absorption in the vapor absorption bands and a reflection spike is discovered at the 1.84 μm spectral band. The result shows that 1.84 μm and other vapor absorption bands can make contributions to aircraft detection in the daytime.

Using Space-borne Hyperspectral Imagery on Mapping Nitrogen Fertilizer Excess to the Environment

Using Space-borne Hyperspectral Imagery on Mapping Nitrogen Fertilizer Excess to the Environment

Hyperion image analysis and linear spectral unmixing to evaluate the grades of iron ores in parts of Noamundi, Eastern India

This paper reports the results of a study to differentiate iron ores in terms of their grades, using the hyperspectral (EO-1 Hyperion) image data, covering a mineralized belt in the Noamundi area, eastern India. The study involves hyperspectral data collection, pre-processing (reduction of atmospheric and solar flux effects), generation of spectral curves from the image for the iron ore deposits, extraction of key spectral parameters and linear spectral unmixing for mapping iron ore abundance. Spectral curves for iron ore deposits extracted from the Hyperion image pixels exhibit strong absorption at 850–900nm and 2150–2250nm wavelengths, which is typical of iron ores. The strength of the absorption features in the continuum removed spectra varies spatially in the image around the mining areas, indicating differences in composition/grade of the iron ores. Spectral parameters such as the depth, width, area and wavelength position of the absorption features, derived from image spectra in the 850–900nm and 2150–2250nm regions, correlate well with the concentration of iron-oxide and alumina (gangue) in the ore samples obtained from the mine face. Well defined correlations are evident between the concentration of iron oxide and (i) the depth of NIR absorption feature (R2=0.883); (ii) the width of NIR absorption feature (R2=0.912); and (iii) the area of the NIR absorption feature and (R2=0.882). Further, the linear spectral unmixing resulted in an iron ore abundance map which, in conjunction with the image- and laboratory-spectra, helped in assessing the grades of iron ores in the study area. Thus, this study demonstrates the feasibility of discriminating grades of iron ores based on spectral information derived from spaceborne hyperspectral imagery.

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy). Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution). Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification). The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.

Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

The retrievals of atmospheric water vapor column and surface reflectance from air- or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time consuming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a methodology to compile the database for a specified retrieval accuracy in water vapor and surface reflectance for a given set of input surface reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considerations are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and surface reflectance, respectively. It should be pointed out that this is not intended as a general claim of retrieval accuracy achievable under typical remote sensing conditions, but these figures apply only to the theoretical conditions of the calculation, i.e., assuming the same conditions for forward simulation and retrieval. Nevertheless, these figures are indispensable for the design of a database, which is an important step for the atmospheric correction of imaging spectrometer data and the sole topic of this paper.