Unmixing Model Research Articles

High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover (FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite (HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index (NDVI) was acquired by using the continuous correction (CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product (GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors (MEs) of forest, cropland, and grassland were −0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809, root-mean-square deviation (RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude (RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

An exploratory study on the use of different composite magnetic and colour fingerprints in aeolian sediment provenance fingerprinting

There is an urgent need for reliable and cost-effective sediment source tracing techniques for apportioning aeolian sediment (sand dune) sources for guiding the selection of best management practices for wind erosion control. Accordingly, the main aim of this study was to quantify the contributions of aeolian sources to sand dune target sediment samples collected in a case study in central Iran using a source fingerprinting procedure based on low-cost fingerprints comprising colour and magnetic tracers. Colour (RGB), magnetic susceptibility (χlf and χhf) and 13 colour and magnetic indices were measured on 54 aeolian sediment source samples and ten aeolian target sediment samples. Three different composite fingerprints for discriminating and apportioning the aeolian sediment sources were selected based on a combination of statistical tests comprising the Kruskal–Wallis H test (KW-H), discriminant function analysis (DFA), principal component & classification analysis (PCCA), and a general classification & regression tree (GC&RT) model. The Modified MixSIR Bayesian un-mixing model was used to apportion aeolian source contributions using the final composite fingerprints. The composite signatures all suggested that the salt flat plain was the dominant (average 63%, and standard deviation, SD, 5.9%) source of the aeolian target sediment samples, whilst agricultural land was second (average 63%, SD 5.6%,) most important. The root mean square difference between the apportionment results based on the three composite fingerprints ranged from 0.2% to 8.3%. Pairwise comparisons of the posterior distributions for the predicted source proportions generated using the three composite signatures showed that eight of 12 pairwise comparisons were not significantly different. Virtual mixture accuracy tests of the fingerprinting models using the three composite signatures suggested errors ranging between 2.2%−20.6% (with a mean of 9.9%), 1.4%−17.0% (mean value 8.3%), and 0.03%−1.0% (mean value 0.8%). The results support the use of low-cost colour and magnetic tracers by investigations into aeolian sediment provenance.

A novel hyperspectral unmixing model based on multilayer NMF with Hoyer’s projection

Hyperspectral remote sensing is an important earth observation method with wide application. But the low spatial resolution of hyperspectral images makes it difficult to distinguish the ground objects. The hyperspectral image unmixing is a task to estimate the spectral signatures and corresponding fractional abundances. However, the unmixing speed and efficiency are still limited by traditional structures. In this paper, a novel multilayer nonnegative matrix factorization framework is proposed with Hoyer’s projector, called HP-MLNMF. The well-known framework, multilayer nonnegative factorization (MLNMF), is completely restructured and enhanced by introducing the Hoyer’s projector to provide the iteration directivity of the structure in the unmixing process. Besides, a novel sparse constraint to spectral signatures suitable for this structure is found as l1/4-norm based on some experimental discussions. Moreover, the lp-norm is utilized to find the possible sparest solution for abundance terms. Finally, HP-MLNMF is compared with some representative and state-of-art methods on synthetic and real-world hyperspectral image datasets. Experiments indicate that our method performances well in most cases.

A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing

Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method.

Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks

Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers. Considering real-world situations, a sparse constraint is normally added to the linear unmixing model. However, the linear model does not take into account that the spectrum of mixed pixels is not simply linearly mixed. To fully study the mixing characteristics of ground object spectra before being imaged by the sensor, we propose a supervised unmixing architecture based on one-dimensional convolutional neural network (CNN) by considering the spectral information and the sparse characteristics in the mixed pixel. Since one-dimensional CNN only consider feature learning, we combine the traditional root mean square error (RMSE) and l_"1" regularization in its loss function to minimize training error. The performance of our proposed unmixing model is assessed by comparing the unmixing results with three traditional linear sparse unmixing algorithms, and the Fuzzy-ARTMAP neural network in a simulated dataset and three real datasets. The RMSE was used to verify the unmixing accuracy of the different methods. The results showed that the RMSE obtained by our proposed CNN-based method was the lowest among the methods on all three real datasets, proving the effectiveness and stability of CNN in unmixing tasks.

Double Regression‐Based Sparse Unmixing for Hyperspectral Images

Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression‐based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression‐based sparse unmixing via K‐means (DRSUMK−means) is proposed. The improved K‐means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l2,0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row‐hard‐threshold function to solve the l2,0 norm directly. Then, DRSUMK−means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSUMK−means.

Spectral-Spatial Constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of Hyperspectral Images

Hyperspectral spectral mixture analysis (SMA), which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been successfully used to tackle mixed-pixel problem in hyperspectral remote sensing applications. As an approach of decomposing a high-dimensional data matrix into the multiplication of two nonnegative matrices, nonnegative matrix factorization (NMF) has shown its advantages and been widely applied to SMA. Unfortunately, most of the NMF-based unmixing methods can easily lead to an unsuitable solution, due to inadequate mining of spatial and spectral information and the influence of outliers and noise. To overcome such limitations, a spatial constraint over abundance and a spectral constraint over endmembers are imposed over NMF-based unmixing model for spectral-spatial constrained unmixing. Specifically, a spatial neighborhood preserving constraint is proposed to preserve the local geometric structure of the hyperspectral data by assuming that pixels in a spatial neighborhood generally fall into a low-dimensional manifold, while a minimum spectral distance constraint is formulated to optimize endmember spectra as compact as possible. Furthermore, to handle non-Gaussian noises or outliers, an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm based loss function is ultimately adopted for the proposed spectral-spatial constrained nonnegative matrix factorization model and a projected gradient based optimization algorithm is designed for optimization. Experimental results over both synthetic and real-world datasets demonstrate that the proposed spatial and spectral constraints can certainly improve the performance of NMF-based unmixing and outperform state-of-the-art NMF-based unmixing algorithms.

Measurements of pressure broadening of N2 in the anisotropic tensor component of spontaneous Raman spectra

Spontaneous Raman scattering from nitrogen is well-suited for temperature measurements in combustion experiments, especially at high pressure which increases the Raman signal because of the higher density in the measurement volume. In this work we investigate high density effects on the anisotropic tensor component of ro-vibrational spontaneous Raman scattering, which must be understood to obtain accurate thermometry in high pressure gases using high-fidelity Raman simulations. We measure the collision broadening for the anisotropic component of spontaneous Raman scattering from room temperature nitrogen over the pressure range 10–70 atm for three gas compositions: pure nitrogen, air, and nitrogen in argon. Line broadening coefficients inferred from these measurements were found to be 14 ± 5% larger than the corresponding broadening coefficients of the isotropic Q branch. Air broadening coefficients were found to be very similar and about 2.5% smaller than nitrogen self-broadening coefficients. Argon broadening coefficients were 25% smaller at rotational quantum number 7 and about 50% smaller at rotational quantum number 21. Additionally, we found that our unmixed line model for the O and S branches gave good fits for all pressures, which indicates that line mixing effects are not significant in the O and S branches over this range of pressures. Using indirect experimental evidence we infer that line mixing effects in the anisotropic component of the Q branch were below the threshold set by our experimental spectral resolution at pressures up to 70 atm at room temperature. Assuming that the anisotropic Q branch lines mix like the isotropic lines results in a small systematic error in the inferred temperature at flame temperatures, with the error increasing slowly with pressure. The bias can be easily removed by modeling the anisotropic spectrum separately from the isotropic spectrum. Line mixing effects should be included in the model of the isotropic part of the Raman spectrum, but can probably be neglected in the anisotropic part of the spontaneous Raman spectrum of N 2 for pressures below 400 atm at flame temperatures.

Thermal emission measurements of ordinary chondrite mineral analogs in a simulated asteroid environment: 2. Representative mineral mixtures

We investigate a suite of synthetic mineral mixtures designed to act as bulk mineralogical analogs to H, L, and LL ordinary chondrite meteorites in order to probe how the thermal emission characteristics of such materials change between ambient and simulated asteroid environmental conditions. Due to the parent body link with certain S-type asteroids, studying these analog mixtures in an environment that is relevant to actual asteroid surfaces advances our understanding of the thermal emission properties of one of the most common regolith types among the main-belt and near-Earth asteroid populations. The observed changes in spectral emissivity features due to cold, vacuum conditions are not as large as previously observed for single mineral (silicate) samples. We interpret this difference to be the result of metallic and opaque components weakening near-surface thermal gradients in the mixtures. As such, we predict that near-surface thermal gradients on ordinary chondrite parent bodies (e.g., S-type asteroids) are likely much weaker than would be inferred from cold, vacuum measurements of individual mineral components. We tested whether the increased spectral contrast observed in fine-grained (<25 μm) samples measured in a cold, vacuum environment increases the efficacy of least squares linear unmixing methods. It is found that the accuracy of such models does not improve relative to measurements made under ambient conditions, thus linear unmixing models are not expected to yield accurate estimates of the modal mineralogy of airless planetary surfaces if they are dominated by fine-grained regolith. Mixtures with coarse particle sizes (125–250 μm) that were modeled using the coarse particle size endmembers yielded results that were largely independent of the environmental conditions, but with larger errors in spectral fits for samples measured in a simulated asteroid environment. At simulated asteroid environmental conditions, the bulk silicate composition and metal content play a more important role in determining the thermal state and brightness temperature of the sample than at ambient conditions. Modest changes in metal content (10–25 wt%) lead to large differences in the brightness temperature of a sample. Under simulated asteroid conditions, an ~10 K increase in maximum brightness temperature that tracks with increased iron content is observed at fine particle sizes (<25 μm) between each of the analog ordinary chondrite groups. Based on these results, it may be difficult to distinguish H, L, and LL compositions of suspected ordinary chondrite parent bodies using only Earth- or space-based thermal emission spectra. This is inferred from the spectral similarity of the analog mixtures and the absence of significant variation in spectral emissivity associated with reported differences in bulk metal content for ordinary chondrites.

Determining the source of placer gold in the Anaconda metamorphic core complex supradetachment basin using detrital zircon U-Pb geochronology, western Montana, USA

Abstract Despite the widespread occurrence and economic significance of gold placer deposits, modern provenance studies of placer sediments remain largely qualitative. This study applies detrital zircon (DZ) geochronology to determine the source of zircon in placer deposits. We then evaluate the provenance of the zircon to assess whether the gold might have been derived from the same sources, thereby providing a case study of the use of DZ geochronology applied to placers. We present a new set of DZ U-Pb ages (n = 1058) and Lu-Hf (n = 61) isotopic data from four placer deposit samples collected from the Pioneer District of western Montana (USA). Each of the four samples yielded similar age spectra, with a range of U-Pb ages between 3000 and 25 Ma. We interpret that ≥250 Ma zircons were recycled from the Mesoproterozoic Belt Supergroup, Paleozoic–Mesozoic sedimentary rocks, and the Upper Cretaceous–Paleocene Beaverhead Group. Our 237 DZ U-Pb ages ≤250 Ma reveal two prominent age-probability peaks centered at ca. 69 Ma and ca. 26 Ma, which we interpret to record first-cycle derivation from the Royal stock and nearby Dillon Volcanics, respectively. We evaluate these data using an inverse Monte Carlo DZ unmixing model that calculates relative contributions from plausible source units, determining a 12% contribution from the Royal stock and a 43% contribution from the Beaverhead Group. A current absence of the Beaverhead Group in the hypothesized source region suggests complete erosion of the unit into the placer-bearing basin. Detrital zircon geochronology, Hf isotopic data, and the unmixing modeling results offer the first zircon-based support for previous interpretations that the Late Cretaceous Royal stock precipitated gold along its contact with overlying Proterozoic–Mesozoic sedimentary strata. Subsequent exhumation and erosion of the lode source led to gold deposition in the Anaconda metamorphic core complex supradetachment basin during the late Oligocene–late Miocene. The worldwide occurrence of gold placer deposits with unknown source areas provides abundant opportunity to apply these techniques elsewhere.

Cloud‐native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel‐2

AbstractThe lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi‐automated workflow in the cloud that uses freely available Sentinel‐2 data from the European Space Agency (ESA) Copernicus programme to derive information on near‐shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end‐to‐end cloud‐based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud‐free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite‐derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub‐pixel–level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow‐water ecosystems which are of great significance to local communities and building resilience towards climate change.

The quantitative source apportionment of heavy metals in peri-urban agricultural soils with UNMIX and input fluxes analysis

The intensive human activities are the major cause of heavy metals pollution in suburban farmland, which poses threats to soil environment and safety of agricultural products. The combination of spatial analysis (SA), principal component analysis (PCA), input fluxes analysis (IFA) and UNMIX model have precisely analyzed the sources and relative contribution of heavy metals from each source. The results revealed that average contents of Cr, Ni, Cu, Zn, Pb and Cd were 37.82, 13.65, 17.93, 93.78, 48.84 and 0.24 mg kg −1 , respectively. The spatial distribution indicated that parent materials, industrial activities and traffic emissions had significant effects on contents of heavy metals in topsoil. According to UNMIX model, the contribution rates of soil parent material, industrial/ traffic emissions, and agricultural activities to heavy metals in soil were 49.15%, 35.00% and 15.85% respectively. Which indicated largest contribution of natural sources, but input of human activities including industry, transportation and agriculture could not be ignored. The input fluxes analysis (IFA) reveals that atmospheric deposition was main source of heavy metals in soil, followed by fertilizer and irrigation. This study has presented reliable methods for analysis of sources of heavy metals in suburban farmland soil. The combination of UNMIX and IFA can accurately identify and quantify pollution sources. • UNMIX and input fluxes analysis were combined for source apportionment of heavy metals. • Heavy metals in soil mainly come from natural sources, industrial and traffic emissions, and agricultural activities. • UNMIX can be successfully applied for sources apportionment of heavy metals in soil.

MULTITEMPORAL SPECTRAL ANALYSIS FOR ALGAE DETECTION IN AN EUTROPHIC LAKE USING SENTINEL 2 IMAGES

Abstract. Eutrophication is characterized by excessive plant and algal growth due to the increased of organic matter, carbon dioxide and nutrients in water body. Although eutrophication naturally occurs over centuries as lakes age, human activities have accelerated it processes and caused dramatic changes to the aquatic ecosystems including elevated algae blooms and risk for hypoxia as well as degradation in the quality of drinking water and fisheries. Monitoring eutrophic processes is therefore highly important to human health and to the aquatic environment. However, the spatial and seasonal distribution of the phenomena and its dynamic are difficult to be resolved using conventional methods as water sampling or sparse acquisition of remote sensing data. This research work proposes a methodology that takes advantage of the high temporal resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced temporal unmixing model to estimate the abundance of [Chl-a] and algae species in San Roque reservoir, Argentina, in the period August 2016 to August 2019. The spatial patterns and the temporal tendencies of these aquatic indicators, that have a direct link to Eutrophication, were analysed and evaluated using in situ data in order to assess their contribution to the local water management.

Nonlocal Tensor-Based Sparse Hyperspectral Unmixing

Sparse unmixing is an important technique for analyzing and processing hyperspectral images (HSIs). Simultaneously exploiting spatial correlation and sparsity improves substantially abundance estimation accuracy. In this article, we propose to exploit nonlocal spatial information in the HSI for the sparse unmixing problem. Specifically, we first group similar patches in the HSI, and then unmix each group by imposing simultaneous a low-rank constraint and joint sparsity in the corresponding third-order abundance tensor. To this end, we build an unmixing model with a mixed regularization term consisting of the sum of the weighted tensor trace norm and the weighted tensor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm of the abundance tensor. The proposed model is solved under the alternating direction method of multipliers framework. We term the developed algorithm as the nonlocal tensor-based sparse unmixing algorithm. The effectiveness of the proposed algorithm is illustrated in experiments with both simulated and real hyperspectral data sets.

Twin support vector machine-based hyperspectral unmixing and its uncertainty analysis

In consideration of within-class endmember variability, it is realistic to use multiple endmembers to model a pure class. We propose an advanced multi-endmember unmixing algorithm based on twin support vector machines (UTSVM), which derives the abundances based on the distances from the mixed pixels to each classification hyperplane. Unmixing uncertainty, an issue often neglected in multi-endmember unmixing, is also analyzed quantitatively for UTSVM. Two types of unmixing uncertainty, abundance overlap (i.e., different mixed pixels have the same abundances) and model overlap (i.e., one mixed pixel may be unmixed into different abundances), are introduced. Abundance overlap angle and abundance variability scale (AVS) are defined as two uncertainty indexes to measure abundance overlap and model overlap, respectively. The relationship between within-class endmember variability and unmixing uncertainty is discussed. When the unmixing uncertainty is high, we propose to use the mean value of abundances within AVS as the estimation of abundance to obtain the best compromised results. Experimental results show the feasibility and effectiveness of our study.

Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization

Remote sensing data from hyperspectral cameras suffer from limited spatial resolution, in which a single pixel of a hyperspectral image may contain information from several materials in the field of view. Blind hyperspectral image unmixing is the process of identifying the pure spectra of individual materials (i.e., endmembers) and their proportions (i.e., abundances) at each pixel. In this article, we propose a novel blind hyperspectral unmixing model based on the graph total variation (gTV) regularization, which can be solved efficiently by the alternating direction method of multipliers (ADMM). To further alleviate the computational cost, we apply the Nyström method to approximate a fully connected graph by a small subset of sampled points. Furthermore, we adopt the Merriman-Bence-Osher (MBO) scheme to solve the gTV-involved subproblem in ADMM by decomposing a gray-scale image into a bitwise form. A variety of numerical experiments on synthetic and real hyperspectral images are conducted, showcasing the potential of the proposed method in terms of identification accuracy and computational efficiency.

An Overview on Linear Unmixing of Hyperspectral Data

Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. The existence of a mixed pixel affects the accuracy of the ground object identification and classification and hinders the application and development of hyperspectral technology. For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well. Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing.

FingerPro: an R Package for Tracking the Provenance of Sediment

Soil loss by erosion processes is one of the largest challenges for food production and reservoir siltation around the world. Information on sediment, nutrients and pollutants is required for designing effective control strategies. The estimation of sediment sources is difficult to get using conventional techniques, but sediment fingerprinting is a potentially valuable tool. This procedure intends to develop methods that enable to identify the apportionment of sediment sources from sediment mixtures. We developed a new tool to quantify the provenance of sediments in an agroforest catchment. For the first time, the procedure for the selection of the best combination of tracers was included in the tool package. An unmixing model algorithm is applied to the sediment samples to estimate the contribution of each possible source. The operations are compiled in an R package named FingerPro, which unmixes sediment samples after selecting the optimum set of tracers. An example from a well-studied Mediterranean catchment is included in the package to test the model. The sediment source apportionments are compared with previous results of soil redistributions where 137Cs derived rates validate the unmixing results, highlighting the potential of sediment fingerprinting for quantifying the main sediment provenance. Fingerprinting techniques will allow us to better comprehend sediment transport to water ecosystems and reservoirs and its detrimental effect on the quality of the water and aquatic habitats. The FingerPro package provides further understanding of the unmixing procedure through the use of graphical and statistical tools, offering a broader and easier application of the technique.

An alternative approach to mapping pigments in paintings with hyperspectral reflectance image cubes using artificial intelligence

Spectral imaging modalities, including reflectance and X-ray fluorescence, play an important role in conservation science. In reflectance hyperspectral imaging, the data are classified into areas having similar spectra and turned into labeled pigment maps using spectral features and fusing with other information. Direct classification and labeling remain challenging because many paints are intimate pigment mixtures that require a non-linear unmixing model for a robust solution. Neural networks have been successful in modeling non-linear mixtures in remote sensing with large training datasets. For paintings, however, existing spectral databases are small and do not encompass the diversity encountered. Given that painting practices are relatively consistent within schools of artistic practices, we tested the suitability of using reflectance spectra from a subgroup of well-characterized paintings to build a large database to train a one-dimensional (spectral) convolutional neural network. The labeled pigment maps produced were found to be robust within similar styles of paintings.

Sediment sources, soil loss rates and sediment yields in a Karst plateau catchment in Southwest China

Intensive agricultural activities have accelerated soil erosion and rocky desertification in karst regions of southwest China. Knowledge of sediment sources and soil erosion rates can be used to target soil conservation measures and to improve calibration and validation of process-based soil erosion and sediment delivery models for scenario analyses. Due to the complexity of karst environments, however, catchment scale information on these components of sediment budgets has rarely been assembled, meaning there continues to be an evidence gap. Within this context, this study selected Chenqi catchment, given its appropriate research infrastructure, to investigate sediment sources and soil loss rates in a typical karst agroforestry landscape. We estimated the relative contributions from three sources: surface soil, subsurface soil and clastic rock, using a composite fingerprinting procedure combining 137Cs and magnetic susceptibility and a frequentist un-mixing model with Monte Carlo uncertainty analysis. Suspended sediment samples were taken at an hourly interval during seven rainfall events in 2017–2018 to characterize and quantify the sediment exported in both surface and underground drainage. The overall average median contributions (with 5th-95th percentile uncertainty ranges) from the sources to the suspended sediment samples from the surface drainage outlet were in the order: 62% (0−99%) subsurface soils, 25% (0−91%) surface soils and 13% (0−45%) clastic rock. For the sediment samples collected from the underground drainage catchment outlet, the corresponding estimates were in the order: 68% (0−97%) subsurface soils, 25% (0−53%) clastic rock and 7% (0−44%) surface soils. Plot scale soil loss rates were highest on cropland (0.70 Mg km−2) and pasture land (0.48 Mg km−2). The average (2017 and 2018) annual suspended sediment load exported through the surface outlet was 4.64 Mg km−2 compared with 1.20 Mg km−2 through the underground outlet. The broader implications of this study are that subsurface and clastic rock sources represent a significant component of the catchment sediment budget, meaning erosion control measures targeting hillslope surface soils alone may have limited impact on suspended sediment export at landscape scale.