Surface Reflectance Spectra Research Articles

Quantifying Qiyi Glacier Surface Dirtiness Using UAV and Sentinel-2 Imagery

The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R2 ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately.

Characterization of human lightness discrimination thresholds for independent spectral variations.

The lightness of an object is an intrinsic property that depends on its surface reflectance spectrum. The visual system estimates an object's lightness from the light reflected off its surface. However, the reflected light also depends on object extrinsic properties of the scene, such as the light source. For stable perception, the visual system needs to discount the variations due to the object extrinsic properties. We characterize this perceptual stability for variation in two spectral properties of the scene: the reflectance spectra of background objects and the intensity of light sources. We measure human observers' thresholds of discriminating computer-generated images of 3D scenes based on the lightness of a spherical target object in the scene. We measured change in discrimination thresholds as we varied the reflectance spectra of the objects and the intensity of the light sources in the scene, both individually and simultaneously. For small amounts of extrinsic variations, the discrimination thresholds remained nearly constant indicating that the thresholds were dominated by observers' intrinsic representation of lightness. As extrinsic variation increased, it started affecting observers' lightness judgment and the thresholds increased. We estimated that the effects of extrinsic variations were comparable to observers' intrinsic variation in the representation of object lightness. Moreover, for simultaneous variation of these spectral properties, the increase in threshold squared compared to the no-variation condition was a linear sum of the corresponding increase in threshold squared for the individual properties, indicating that the variations from these independent sources combine linearly.

Spectroscopic Phenological Characterization of Mangrove Communities

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban.

Mesocosm Experiment to Evaluate Relations between Chlorophyll-a Concentration and Water Surface Reflectance in an Anthropogenic Reservoir

This paper presents the results of a mesocosm experiment for the evaluation of remote sensing chlorophyll-a (chl-a) concentration estimations in an anthropogenic water reservoir. The chl-a presence in the water causes changes in the water surface reflectance spectrum, especially in the green and red part, but many factors could affect the remote measurements of chl-a content. The in situ mesocosm method of the experiment was used for investigating the spectral reflectance of the inland water surface in a wide range of chl-a concentrations. Eight specially designed measurement boxes were placed into the water. In half of the boxes, the devices to support the development of the submerged water plant were installed. During the experiment, simultaneously, spectral data from the water surface were gathered and physical–chemical analyses of water were carried out. The obtained results confirm the usefulness of the mesocosm experiment for the remote sensing chl-a concentration algorithms being developed. The concentration of dissolved organic carbon was identified as a key factor that interfered with remote chl-a estimations in the analyzed reservoir.

A new spectrally-invariant approach to the remote sensing of inhomogeneous clouds

Current operational satellite retrievals of cloud optical and microphysical properties go back to the Nakajima-King technique developed at the end of the 1980 s. This technique is based on library calculations for plane-parallel homogeneous clouds. It often works well for overcast skies but leads to substantial errors for inhomogeneous and broken cloud fields where the plane-parallel-geometry assumption is no longer valid. The basic concept of a new technique was first introduced in nuclear physics to quantify the critical conditions of reactors. Based on the eigenvalues of the radiative transfer equation, their approach provides a powerful means to parameterize the structure of 3D media. This parameterization was later successfully applied to relate surface reflectance spectra to 3D canopy structure and is known as the spectrally-invariant approximation. The proposed approach adapts this technique to the remote sensing of cloud properties such as droplet single scattering albedo and the average number of scatterings (which are the fundamental parameters in radiative transfer theory), with an emphasis on quantifying the associated errors and uncertainties. This retrieval is free from the plane-parallel homogeneous cloud assumption.

Fraunhofer line-based wavelength-calibration method without calibration targets for planetary lander instruments

High-accuracy wavelength calibration is critical for qualitative and quantitative spectroscopic measurements. Many spectrometers employed in planetary-exploration missions have onboard calibration sources, including standard lamps and calibration targets. However, such calibration sources are not always available because planetary missions, particularly landing missions, usually have limitations in size and mass. Thus, a wavelength calibration method without requiring hardware addition can be highly beneficial. In this study, we demonstrate a method for wavelength calibration using solar Fraunhofer lines observed in the reflectance spectra of planetary surfaces. Using a Raman spectrometer prototype developed for a Phobos rover, we measured the spectrum of the sunlight reflected from a spectral standard, manufactured to provide similar reflectance spectra to the surface of Phobos. We identified 35 Fraunhofer absorption lines in the wavelength range between 530 and 700 nm and utilized these features for the wavelength calibration of the spectrometer. This approach using Fraunhofer lines achieved good results (better than +0.04/−0.06 nm), comparable to the results achieved using a conventional Ne lamp. The wavelength accuracy corresponds to a wavenumber accuracy better than ±1.5 cm−1 in the 0–4000 cm−1 Raman shift (Stokes shift) range with a 532 nm excitation laser. This result enabled the estimation of the magnesium number (Mg#) of olivine, achieving a value more precise than 1.5% based on the Raman peak positions. In addition, we examined the number of solar Fraunhofer lines detectable at different wavelength resolutions by binning the solar spectrum acquired in this study. We found that more than 10 Fraunhofer lines could be detected as prominent absorption lines when the wavelength resolution is higher than 1 nm/pix (30 cm−1/pix at 1000 cm−1). This result suggests that the target-free wavelength-calibration method using solar Fraunhofer lines can be applied to other spectrometers simply by observing sunlit planetary surfaces.

Spatially constrained atmosphere and surface retrieval for imaging spectroscopy

Imaging spectrometers provide important Earth surface data for terrestrial and aquatic ecology, hydrology, geology through retrieved surface reflectance spectra. Surface reflectance is generally recovered from measured radiance through model inversions of the surface and atmospheric system. Most retrieval approaches treat all pixels independently and ignore spatial correlations in the atmosphere for neighboring pixels, resulting in surface-related biases in the retrieved surface and atmospheric parameters. In this work, we present a mathematical framework to more accurately retrieve surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. This framework retrieves the full spatial–spectral reflectance data cube in a computationally efficient manner and is to our knowledge the first approach capable of accurately finding this global solution for large imaging spectroscopy datasets. We implement this mathematical framework in an example proposed algorithm which we call spatially constrained optimal estimation (SCOE). We show that SCOE improves the overall surface reflectance retrieval error, reduces surface-related biases in the retrieved surface reflectance and atmospheric parameters, and is more robust to noise than a traditional pixel-by-pixel approach and an operational empirical line approach in simulated and experimental results. This reduction in surface reflectance error will impact science data products across application areas, making imaging spectroscopy algorithms more robust for global acquisitions.

An inner warp discovered in the disk around HD 110058 using VLT/SPHERE and HST/STIS

Context. An edge-on debris disk was detected in 2015 around the young, nearby A0V star HD 110058. The disk showed features resembling those seen in the disk of β Pictoris that could indicate the presence of a perturbing planetary-mass companion in the system. Aims. We investigated new and archival scattered light images of the disk in order to characterise its morphology and spectrum. In particular, we analysed the disk’s warp to constrain the properties of possible planetary perturbers. Methods. Using data from two VLT/SPHERE observations taken with the Integral Field Spectrograph (IFS) and near InfraRed Dual-band Imager and Spectrograph (IRDIS), we obtained high-contrast images of the edge-on disk. Additionally, we used archival data from HST/STIS with a poorer inner-working angle but a higher sensitivity to detect the outer parts of the disk. We measured the morphology of the disk by analysing vertical profiles along the length of the disk to extract the centroid spine position and vertical height. We extracted the surface brightness and reflectance spectrum of the disk. Results. We detect the disk between 20 au (with SPHERE) and 150 au (with STIS), at a position angle of 159.6° ± 0.6°. Analysis of the spine shows an asymmetry between the two sides of the disk, with a 3.4° ± 0.9° warp between ~20au and 60 au. The disk is marginally vertically resolved in scattered light, with a vertical aspect ratio of 9.3 ± 0.7% at 45 au. The extracted reflectance spectrum is featureless, flat between 0.95 µm and 1.1 µm, and red from 1.1 µm to 1.65 µm. The outer parts of the disk are also asymmetric with a tilt between the two sides compatible with a disk made of forward-scattering particles and seen not perfectly edge-on, suggesting an inclination of <84°. Conclusions. The presence of an undetected planetary-mass companion on an inclined orbit with respect to the disk could explain the warp. The misalignment of the inner parts of the disk with respect to the outer disk suggests a warp that has not yet propagated to the outer parts of the disk, favouring the scenario of an inner perturber as the origin of the warp.

Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models

Monitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.

Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera.

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors' mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry-Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained 0.29-0.36 fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between 0.01 and 0.05 on average and the spectral angle between 1.5 and 3.2 degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker.

Natural light-trapping nanostructures on thermally-grown cupric oxide

Thermal oxidation of copper produces a layer of copper oxides. We present results showing that the surface of this thermal oxide naturally has an involuted morphology that can trap a very significant amount of incident radiation (∼70–90%). This phenomenon is of particular interest at near-infrared and visible wavelengths where the oxide shows band gap absorption. We provide details on the growth process and surface characterization data, including SEM micrographs, surface reflection spectra, as well as diffuse reflection measurements. These reveal the intricate surface morphology and its effect on light trapping and scattering from the oxide surface.

Suberin Fatty Acid Hydrolysates from Outer Birch Bark for Hydrophobic Coating on Aspen Wood Surface.

Bark extracts are sustainable sources of biopolymers and have great potential to replace fossil-based polymers in wood coating applications. The present study investigated the applicability of suberin fatty acids hydrolysate (SFA) extracted from the outer bark of silver birch (Betula pendula Roth.) for coating of aspen wood (Populus tremula L.). The SFA combined with maleic anhydride (MA) and octadecyltrichlorosilane (OTS) as a curing agent was prepared in ethanol and used in surface coating. The water contact angle, surface reflectance spectra, FTIR, and SEM-EDS were used to characterize the physical and chemical properties of the coated wood samples. Further, the long-term stability of the SFA coating was analyzed via artificial aging. The wood surface became hydrophobic, as the contact angle for the water droplet (WCA) was over ~120°, and was stable for all of the prepared combinations of SFA, MA, and OTS.

A non-motorized spectro-goniometric system to measure the bi-directional reflectance spectra of particulate surfaces in the visible and near-infrared.

Reflectance spectroscopy is a powerful tool for remotely identifying the compositional and physical properties of surface materials. Due to the anisotropic scattering nature of most surfaces, the spectral features, including the absolute reflectance value, spectral slope, and band depth, are influenced by illumination and viewing configurations. Therefore, it is important to understand how spectral features vary with illumination and observation geometries for various particulate surfaces through laboratory measurements. Here, we describe a non-motorized spectro-goniometric system capable of measuring the bi-directional reflectance of particulate surfaces in the upper hemisphere in the wavelength range from 350 to 2150nm. The incident and the viewing zenith angles can be varied from 0° to 55° and from 0° to 70°, respectively. The relative viewing azimuth angle can be varied from 0° to 360°. Measurements on Labsphere Spectralon agree well with measurements done with other instruments. We also present measurement results on two typical planetary analog materials, the JSC-1A Martian soil simulant and the JSC-1A lunar regolith simulant.

Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data

Identifying objects or pixels of interest that are few in numbers and sparsely populated in imagery is referred to as target detection. Traditionally, the inverse modeling (IM) approach, usually a slow and computationally intensive process, is used for detecting targets using surface reflectance spectra. For the emerging online methods in remote sensing, modeling the at-sensor radiance of target material, i.e., a forward modeling (FM) approach, can be used. Compared to the IM approach, FM is better suited to online methods due to its potential for adaptation to regional atmospheric modeling. Spectral knowledge transfer of a target from a known to an unknown atmospheric condition is the primary outcome of an efficient target detection framework. However, such an endeavor requires an exhaustive assessment of the target detection process under different atmospheric models and associated uncertainties. The objective of this work is to assess the quantitative impact of atmospheric parameters on the detectability of engineered targets. Specifically, the impact of critical atmospheric parameters such as aerosol optical thickness (AOT), standard atmospheric profiles, and aerosol models are considered. For this effect, we designed a multi-platform image acquisition setup that acquired targets concurrently using a ground-based terrestrial hyperspectral imager (THI), an airborne hyperspectral imager (AVIRIS-NG), and a space-borne multispectral imager (Sentinel-2). We used a point-based spectroradiometer and pixel-based THI to collect the in-situ reference target reflectance spectra and generated a radiance spectral library by simulating TOA radiance spectra using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model. We have considered two cases of target radiance simulations, i.e., (i) corresponding to a grid of different AOT values for a predefined atmospheric and aerosol profile, and (ii) corresponding to varying combinations of atmospheric and aerosol profiles at a given AOT. The detection has been carried out using multiple target detection algorithms. Results indicate that the spectral knowledge-based targets can be detected in remote sensing data under different atmospheric model scenarios using the FM approach. A detection rate of about 75% and 50% have been consistently obtained for remote sensing data from airborne and space-borne platforms with a false alarm (FA) rate of 10-2 to 10-3 respectively. Change in the AOT across atmospheric models has resulted in decision-changing implications in the target detection modeling. The selection of the wrong atmospheric profile can potentially aggravate the number of FAs produced by a particular detection algorithm.

Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

Snow and ice melt processes are a key in Earth's energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth's ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/gice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach's potential and suitability for upcoming orbital imaging spectroscopy missions.

Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

The Rocklea Dome 3D Mineral Mapping Test Data Set

Abstract. The integration of surface and subsurface geoscience data is critical for efficient and effective mineral exploration and mining. Publicly accessible data sets to evaluate the various geoscience analytical tools and their effectiveness for characterisation of mineral assemblages and lithologies or discrimination of ore from waste are however scarce. The open-access Rocklea Dome 3D Mineral Mapping Test Data Set (Laukamp, 2020; https://doi.org/10.25919/5ed83bf55be6a) provides an opportunity for evaluating proximal and remote sensing data, validated and calibrated by independent geochemical and mineralogical analyses, for exploration of channel iron deposits (CIDs) through cover. We present hyperspectral airborne, surface, and drill core reflectance spectra collected in the visible–near-infrared and shortwave infrared wavelength ranges (VNIR–SWIR; 350 to 2500 nm), as well as whole-rock geochemistry obtained by means of X-ray fluorescence analysis and loss-on-ignition measurements of drill core samples. The integration of surface with subsurface hyperspectral data collected in the frame of previously published Rocklea Dome 3D Mineral Mapping case studies demonstrated that about 30 % of exploration drill holes were sunk into barren ground and could have been of better use, located elsewhere, if airborne hyperspectral imagery had been consulted for drill hole planning. The remote mapping of transported Tertiary detritals (i.e. potential hosts of channel iron ore resources) versus weathered in situ Archaean bedrock (i.e. barren ground) has significant implications for other areas where “cover” (i.e. regolith and/or sediments covering bedrock hosting mineral deposits) hinders mineral exploration. Hyperspectral remote sensing represents a cost-effective method for regolith landform mapping required for planning drilling programmes. In the Rocklea Dome area, vegetation unmixing methods applied to airborne hyperspectral data, integrated with subsurface data, resulted in seamless mapping of ore zones from the weathered surface to the base of the CID – a concept that can be applied to other mineral exploration and mineral deposit studies. Furthermore, the associated, independent calibration data allowed the quantification of iron oxide phases and associated mineralogy from hyperspectral data. Using the Rocklea Dome data set, novel geostatistical clustering methods were applied to the drill core data sets for ore body domaining that introduced scientific rigour to a traditionally subjective procedure, resulting in reproducible objective domains that are critical for the mining process. Beyond the previously published case studies, the Rocklea Dome 3D Mineral Mapping Test Data Set has the potential to develop new methods for advanced resource characterisation and develop new applications that aid exploration for mineral deposits through cover. The white mica and chlorite abundance maps derived from airborne hyperspectral, presented here for the first time, highlight the additional applications of remote sensing for geological mapping and could help to evaluate newly launched hyper- and multispectral spaceborne systems for geoscience and mineral exploration.

Nanocomposite fabricated by low energy silver ions implanted into dielectrics and sensitive to the refractive index of overlay

Based on 30 keV silver ions at a fluence of 6 × 1016 ions·cm−2 implanted into SiO2, Al2O3 and YAG, we found that the nanocomposite layers embedded with silver nanoparticles are sensitive to the refractive index of overlays on the implanted surface. By dropping liquids with different refractive indices on the surface of nanocomposite layer, the reflection spectra of the rear surface would shift. The sample with YAG as the substrate has better performance by comparing with the other two samples. The prepared structures have the advantages of simple structure and stable performance, which has potential applications in localized surface plasmon resonance sensors.

Improved method of hydrous mineral detection by latitudinal distribution of 0.7-μm surface reflectance absorption on the asteroid Ryugu

Global multiband images of the C-type asteroid (162173) Ryugu were obtained by the optical navigation camera telescope (ONC-T) onboard Hayabusa2. The 0.7-μm absorption depth of the surface reflectance spectrum, which indicates the presence of hydrous minerals, was not clearly seen on Ryugu using flat field correction data obtained in the preflight measurement. The flat field correction data were obtained in the preflight calibration test only at room temperatures (24‐–28 °C), whereas most observations around Ryugu were performed at a charge-coupled device (CCD) temperature of approximately ‐−30 °C. To obtain higher accuracy measurements, we used a new flat field correction method using the Ryugu surface reflection data. We confirmed that the flat-field patterns are different in high and low temperature conditions. The 0.7-μm absorption map generated by the new method shows that the 0.7-μm absorption near the equator (5°N–5°S) is stronger than that from 30°N to 30°S. We found that the excess of the absorption depth at low latitudes was 0.072%, corresponding to 2.7σ. The spectral analysis also shows that the Ryugu surface at low latitudes is bluer than that at high latitudes and bluer materials tend to show stronger 0.7-μm absorption than redder materials, suggesting that this region has been subjected to less space weathering and less solar heating.

Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space

In the coming years, Earth Observation missions like the FLuorescence EXplorer (FLEX) will acquire the radiance signal from the visible to the near-infrared at a very high spectral resolution, enabling exciting prospects for new insights in satellite-based photosynthetic studies. In this context, the process of de-coupling atmospheric and vegetation-related spectral signatures will become essential to guarantee a reliable estimation of the vegetation photosynthetic activity from space. Dynamic changes related to the vegetation photosynthetic status result in subtle contributions to the top of atmosphere radiance signal, e.g. due to the emission of the solar-induced chlorophyll fluorescence (~ 650–800 nm) or due to changes in surface reflectance spectra (500–600 nm) indicating variations in the vegetation photoprotection and light use efficiency. Conversely, atmospheric effects (molecular and aerosol absorption and scattering) dominate the spectral interval of interest for vegetation studies. This article presents a comprehensive overview of the atmospheric radiative effects caused by aerosols, ozone (O3), water vapor (H2O), oxygen (O2), and atmospheric pressure and temperature changes within the visible and near-infrared spectral interval, and paying special attention to the co-occurring vegetation-related spectral changes associated with the fluorescence emission and the activation of the photoprotection mechanisms. Since the largest uncertainties in the atmospheric correction process are associated with the characterization of the aerosol radiative effects, this work largely concentrates on the satellite retrieval-related implications under different aerosol absorbing and scattering scenarios on a global scale. Through a simulation exercise, it is evaluated to what extent aerosol climatology could influence the accuracy of satellite-derived surface apparent reflectance spectra impacting; therefore, any vegetation-related satellite product on a seasonal and global scale.