Reflection Data Sets Research Articles

An in situ hyperspectral dataset for typical aquatic vegetation

ABSTRACT The heterogeneity of wetland habitats promotes aquatic vegetation diversity. The large number of plant species creates challenges in classifying wetland habitats. In-situ hyperspectral data directly relate vegetation species to the spectral response of the canopy, which serves as a foundation for interpreting and validating remote sensing data, estimating nutrient estimates, and biomass inversions. An in-situ hyperspectral reflectance dataset (spectral range: 350–2500 nm with 300 bands) of typical aquatic vegetation is described in this paper. The dataset includes 134 effective original spectral curves of 124 aquatic vegetation species, including 122 healthy canopy spectra, 7 spectra of withered leaves of aquatic plants, and 5 spectra of flowers. The first-order differential and continuum removal curves are obtained by calculating the original data. To provide a reliable reference for extracting plant spectral features, we describe the spectral curve differences of different plant families, genera, and species. Kruskal–Wallis tests and paired comparison validation were also used to determine the optimal wavelengths for distinguishing different plant types, families, and genera. We expect that this hyperspectral dataset can facilitate ecological remote sensing applications and thus can support remote sensing assessment of the carbon sink capacity of wetland vegetation from multiple perspectives.

Crustal Structure of the Hikurangi Subduction Zone Revealed by Four Decades of Onshore‐Offshore Seismic Data: Implications for the Dimensions and Slip Behavior of the Seismogenic Zone

AbstractFour decades of seismic reflection, onshore‐offshore and ocean‐bottom seismic data are integrated to constrain a high‐resolution 3‐D P‐wave velocity model of the Hikurangi subduction zone. Our model shows wavespeeds in the offshore forearc to be 0.5–1 km/s higher in south Hikurangi than in the central and northern segments (VP ≤ 4.5 km/s). Correlation with onshore geology and seismic reflection data sets suggest wavespeed variability in the overthrusting plate reflects the spatial distribution of Late Jurassic basement terranes. The crustal backstop is 25–35 km from the deformation front in south Hikurangi, but this distance abruptly increases to ∼105 km near Cape Turnagain. This change in backstop position coincides with the southern extent of shallow slow‐slip, most of which occurs updip of the backstop along the central and northern margin. These relationships suggest the crustal backstop may impact the down‐dip extent of shallow conditional stability on the megathrust and imply a high likelihood of near/trench‐breaching rupture in south Hikurangi. North of Cape Turnagain, the more landward position of the backstop, in conjunction with a possible reduction in the depth of the brittle ductile transition, reduces the down‐dip width of frictional locking between the southern (∼100 km) and central Hikurangi margin by up‐to 50%. Abrupt transitions in overthrusting plate structure are resolved near Cook Strait, Gisborne and across the northern Raukumara Peninsula, and appear related to tectonic inheritance and the evolution of the Hikurangi margin. Extremely low forearc wavespeeds resolved north of Gisborne played a key role in producing long durations of long‐period earthquake ground motions.

Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine

By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.

Multimodal HSI Combined with Multiblock Data Fusion: A New Tool for the Study of Time-Dependent Alteration Processes in Dyed Textiles.

The present study describes an innovative approach for the study of time-dependent alteration processes. It combines an advanced hyperspectral imaging (HSI) system, to collect visible reflectance and fluorescence spectral data sets sequentially, with a tailored multiblock data processing method. This enables the modeling of chemical degradation maps and the early, spatially resolved detection of dye alteration in textiles. A chemometric method based on data fusion and principal component analysis was employed to identify spectral features of dye degradation, combining and enhancing information from reflectance and fluorescence HSI data. The most significant spectral profiles extracted were used to develop an asymmetric Gaussian-based pixel-by-pixel fitting model applied to the HSI fluorescence data set, enabling the reconstruction of degradation maps for rapid and intuitive visualization. In particular, changes in intensities and horizontal shift of dye emission peaks were pixel-by-pixel evaluated and fitted for the reconstruction of the degradation maps. Artificially aged wool samples tinted with indigo carmine (IC) dye served as a case study. IC is extensively used in textiles, and it is notable for its light sensitive. The results show that this approach effectively identifies spatial variations and chemical changes in dyed wool fibers, offering potential for sustainable conservation of historical textiles and other types of time-dependent processes. Thus, by amplifying variation in spectral profiles induced over time by aging, even minimal changes at early stages can be easily detected and localized, offering powerful tools for future studies on food and drug shelf life and stability, as well as forensic trace analysis.

Detecting the Phaeocystis globosa bloom and characterizing its bloom condition in the northern Beibu Gulf using MODIS measurements

Phaeocystis globosa is the most common species making up harmful algal blooms. For better detect P. globosa bloom, a multispectral approach was developed based on extensive in-situ investigation and MODIS remote sensing reflectance (Rrs) dataset. A novel proxy RPG was created based on the feature of Rrs spectral shape and P. globosa bloom was identified when RPG was >1.6. Normalized Fluorescence Line Height (nFLH) was applied to discriminate the bloom events and nFLH of bloom waters was almost higher than 0.095 Wm-2μm-1sr-1. The RPG associated with nFLH exhibited the P. globosa bloom areas comparable to that in field investigation, which indicated this practical method was successful on the spatial and temporal distribution of P. globosa blooms. Several environmental factors derived from MODIS products and field survey were analyzed to characterize the bloom conditions. Redundancy analysis suggested that nutrients and temperature are vital for triggering P. globosa bloom.

HIGH-RESOLUTION SATELLITE ESTIMATION OF SNOW COVER FOR FLOOD ANALYSIS IN EAST KAZAKHSTAN REGION

The increasing frequency of extreme weather events linked to climate change has made flood forecasting an important issue, particularly in mountainous regions where snowmelt is a major driver of seasonal flooding. This study explores the application of snow cover estimation techniques to assess snowmelt dynamics and their potential impact on flood risks in the Ulba and Uba basins in East Kazakhstan. To achieve this, high-resolution multispectral satellite imagery from the Sentinel-2 Surface Reflectance dataset is used, focusing on images collected between March and October for the years 2021 to 2024. The images are processed in Google Earth engine platform with strict filtering based on spatial intersection with the basins and cloud cover pixels percentage, ensuring high-quality data for snow cover analysis. The study utilizes multiple remote sensing indices for snow cover estimation. The normalized difference snow index is calculated using the green and shortwave infrared bands to detect snow-covered pixels. Fractional snow-covered area is derived from the NDSI using the 'FRA6T' relationship, offering a more nuanced estimate of snow distribution across the basins. Additionally, a near-infrared to shortwave infrared ratio threshold is employed to minimize confusion between snow and water, improving the detection of snow cover, particularly in regions near water bodies or during melt periods. The resulting snow cover maps and fSCA estimates provide a detailed picture of snow distribution and melt dynamics, contributing to the assessment of snowmelt’s role in flood risk development. The obtained insights can assist in refining flood forecasting models, improving early warning systems, and supporting informed water resource management in vulnerable regions.

Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

Submarine canyon development controlled by slope failure and oceanographic process interactions

The southeastern Australian margin hosts a series of submarine canyons. Although the origin and evolution of canyons within the northwestern segment of the margin is relatively well studied, their quantitative morphology, interaction with longshore drift currents and slope failure remain poorly understood in the southeastern region. In this study, high-resolution bathymetry and 3D seismic reflection datasets revealed five main submarine canyons present in the central offshore Otway Basin. The canyons have V-shaped, evolving to U-shaped morphology downslope with sedimentary infill characterized by medium–high amplitudes, recognizable stratal pattern and localized chaotic seismic reflections. Analysis reveals these canyons were initiated by retrogressive slope failure on the continental slope, that once the shelf edge was reached and subsequently incised, development of the canyon heads was influenced by the shelf parallel Zeehan Current, active on the continental shelf of the region. The heads of the shelf-incised canyons preferentially migrate northwestward and were infilled by laterally accreting sedimentary packages characterized by southeast dipping seismic reflections. These indicate an early erosive phase followed by a period of deposition, a result of the prevalent eastward flowing Zeehan Current. These results have important implications for understanding of the mechanisms that control initiation and development of submarine canyons, and their morphology, both offshore southeastern Australia, and similar settings on continental margins worldwide.

Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities

Satellite-derived bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning SDB models, this study aims to assess the performance improvement of integrating the quasi-analytical algorithm (QAA)-derived IOPs using the Sentinel-2 and ICESat-2 datasets. In different water quality experiments, the results indicate that four SDB models (the Gaussian process regression, neural networks, random forests, and support vector regression) incorporating QAA-IOP parameters equal to or outperform those solely based on the remote sensing reflectance (Rrs) datasets, especially in turbid waters. By analyzing information gains in SDB, the most effective inputs are identified and prioritized under different water qualities. The SDB method incorporating QAA-IOP can achieve an accuracy of 0.85 m, 0.48 m, and 0.74 m in three areas (Wenchang, Laizhou Bay, and the Qilian Islands) with different water quality. Also, we find that incorporating an excessive number of redundant bands into machine learning models not only increases the demand of computing resources but also leads to worse accuracy in SDB. In conclusion, the integration of QAA-IOPs offers promising improvements in obtaining bathymetry and the optimal feature selection should be carefully considered in diverse aquatic environments.

Validation of the ERO2.0 code using W7-X and JET experiments and predictions for ITER operation

The paper provides an overview of recent modelling of global material erosion and deposition in the fusion devices Wendelstein 7-X (W7-X), JET and ITER using the Monte-Carlo code ERO2.0. For validating the modelling tool in a three-dimensional environment, W7-X simulations are performed to describe carbon erosion from the graphite test divertor units, which were equipped in operational phase OP 1.2 and analysed post-mortem. Synthetic spectroscopy of carbon line emission is compared with experimental results from the divertor spectrometer measurement system, showing a good agreement in the e-folding lengths in the radial intensity profiles of carbon. In the case of metallic wall materials, earlier modelling of the Be/W environment in JET and ITER is revisited and extended with an updated set of sputtering and reflection data, as well as including the mixing model for describing the Be/W dynamics in the divertor. Motivated by recent H/D/T isotope experiments in JET, limited and diverted configuration pulses are modelled, showing the expected trend of both Be and W erosion increasing with isotope mass. For the JET diverted configuration pulses, it is shown that Be migrates predominantly to the upper part of the inner divertor where it initially leads to strong W erosion. With longer exposure time, the growth of a Be deposited layer leads to a reduction of W erosion in that region. A similar trend is observed in simulations of the ITER baseline Q = 10 scenario, however with a more symmetric Be migration pattern leading to deposition also on the outer divertor.

Spectral library of gemological minerals from NE Brazil: A reference to aid the reflectance spectra-based discrimination of gem-quality materials

Spectral library of gemological minerals from NE Brazil: A reference to aid the reflectance spectra-based discrimination of gem-quality materials

A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation

A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation

Interpreting environments of deposition from facies analysis of outcrop versus seismic reflection data: A cautionary tale from the Mount Messenger Formation, Taranaki Basin (New Zealand)

Interpreting environments of deposition from facies analysis of outcrop versus seismic reflection data: A cautionary tale from the Mount Messenger Formation, Taranaki Basin (New Zealand)

Temporal and spatial characterization of a thermogenic, fault-controlled gas hydrate system, Woolsey Mound, Gulf of Mexico

Woolsey Mound, located at Mississippi Canyon Lease Block 118 (MC118), is the site of the Gulf of Mexico hydrate research consortium’s seafloor observatory, where gas hydrates outcrop at the seafloor. The presence of gas hydrates in the mound is confirmed directly by coring and indirectly by 3D seismic reflection data. Craters, pockmarks, chemosynthetic communities, and authigenic carbonates populate the seafloor at Woolsey Mound. Each crater is characterized by a network of shallow crestal faults that connect the hydrate mound to the underlying allochthonous salt body. We characterize the temporal and spatial evolution of gas hydrates at Woolsey Mound under natural perturbations using four collocated 3D seismic reflection data sets that span over 14 years. Data acquisition differences embedded in the data sets arising from variation in geometry, sample rate, and phase are minimized using the “cross-equalization” method. Our results indicate that hydrate formation and dissociation vary temporally and spatially in close connection to the shallow crestal faults. Evidence of gas hydrate dissociation is observed over a period of three years (2000–2003), where major dissociation occurred along the southern portion of the crestal fault in the southeast crater. The dissociation is less prominent in the southwest crater. Evidence of methane venting is observed between 2000 and 2010, which is mostly concentrated in the southeast crater. The residual amplitude anomalies observed between 2000 and 2014 in the mound are mostly positive, implying that the methane venting had increased significantly. The positive anomalies are correlated with the methane seepage recorded in 2011. Our results indicate the evolution of a fault-controlled gas hydrate system in the northern Gulf of Mexico, which would aid in assessing its impact on the seafloor.

Quantifying fault interpretation uncertainties and their impact on fault seal and seismic hazard analysis

Quantifying fault interpretation uncertainties and their impact on fault seal and seismic hazard analysis

Stacking pattern transition caused by submarine channel inception and internal levée development

ABSTRACTThe shift from highly amalgamated and laterally offset to aggradational and vertically stacked submarine channels is a pattern that has been widely recognized in seismic reflection datasets as well as in outcropping successions worldwide. However, the sedimentary and stratigraphic details of such an important part of channel evolution and its implications have not been discussed extensively. That gap is addressed here by characterizing a previously undocumented, seismic‐scale outcrop of the Tres Pasos Formation (Magallanes Basin, Chile) that records the transition from a laterally offset/low‐aggradational channel complex to a vertically stacked aggradational complex associated with the development of an internal levée that enhanced channel aggradation. The boundary between the two complexes is defined by a composite erosional surface (up to ca 35 m relief) with two adjacent element‐scale channelized incisions with contrasting sedimentary infills that provide unprecedented insight into this key phase of submarine channel evolution. Observations indicate that, in between these two depositional architecture styles, there is a significant phase of deep incision and bypass. The relief achieved via this deep incision of one or multiple simultaneously active conduits is interpreted to be necessary to set up the conditions for flow stripping and, subsequently, overbank deposition. Finally, the siltstone‐rich intra‐channel lithofacies association observed directly overlying part of the incision is interpreted to represent the stratigraphic expression of the earliest deposits of an active submarine channel preserved due to the abrupt abandonment of this pathway and the development of an adjacent conduit, which resulted in the former being covered with fine‐grained overbank deposits.

Research on Hyperspectral Surface Reflectance Dataset of Typical Ore Concentration Area in Hami Remote Sensing Test Field

Abstract. Surface reflectance data is the basic data source for the hyperspectral parametric remote sensing products and remote sensing quantitative application, which is widely used in various application fields such as natural resources and ecological environment monitoring. At present, multispectral data takes the leading role among the common land surface reflectance datasets and the reflectance data mainly involves the types of ground objects such as farmland, forest land, water body, soil, etc., while the datasets relatively less targets the types of rock and mineral surface objects, yet especially the reflectance datasets with the combination of time series and multi-scale satellite-earth are even more scarce. In order to better promote the application of hyperspectral surface reflectance and explore the advantages of joint application of satellite-earth multi-scale reflectance data, on the basis of field-measured rock and mineral target spectral, a comprehensive surface reflectance dataset was generated by using domestically produced hyperspectral satellite data as the data source in this study, mainly focusing on the typical ore concentration area in the Hami Remote Sensing test field in Xinjiang. The dataset includes multi-period hyperspectral satellite surface reflectance images, field measured rock and mineral spectral data, and multi-period sub-pixel spectral data collected based on ground spectral measured points, which can provide significant support for the research and development and accuracy verification as well as performance evaluation of algorithms such as surface reflectance inversion, mineral identification and ground object classification.

Seaway restriction, sea level drop and erosion in the Alboran Basin from a paleotopographic reconstruction for the Messinian Salinity Crisis

Seaway restriction, sea level drop and erosion in the Alboran Basin from a paleotopographic reconstruction for the Messinian Salinity Crisis

An enhanced large-scale benthic reflectance retrieval model for the remote sensing of submerged ecosystems in optically shallow waters

An enhanced large-scale benthic reflectance retrieval model for the remote sensing of submerged ecosystems in optically shallow waters

An applied framework to unlocking multi-angular UAV reflectance data: a case study for classification of plant parameters in maize (Zea mays)

AbstractOptical sensors, mounted on uncrewed aerial vehicles (UAVs), are typically pointed straight downward to simplify structure-from-motion and image processing. High horizontal and vertical image overlap during UAV missions effectively leads to each object being measured from a range of different view angles, resulting in a rich multi-angular reflectance dataset. We propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval. A standard (nadir) and a multi-angular, 10-band multispectral dataset was collected for maize using a UAV on two different days. Reflectance data was grouped by VZA and VAA (on average 2594 spectra/plot/day for the multi-angular data and 890 spectra/plot/day for nadir flights only, 13 spectra/plot/day for a standard orthomosaic), serving as predictor variables for leaf chlorophyll content (LCC), leaf area index (LAI), green leaf area index (GLAI), and nitrogen balanced index (NBI) classification. Results consistently showed higher accuracy using grouped VZA/VAA reflectance compared to the standard orthomosaic data. Pooling all reflectance values across viewing directions did not yield satisfactory results. Performing multiple flights to obtain a multi-angular dataset did not improve performance over a multi-angular dataset obtained from a single nadir flight, highlighting its sufficiency. Our openly shared code (https://github.com/ReneHeim/proj_on_uav) facilitates access to reflectance data from pre-defined VZA/VAA groups, benefiting cross-disciplinary and agriculture scientists in harnessing the potential of multi-angular datasets. Graphical abstract