Endmember Spectra Research Articles

Towards automatic classification of diffuse reflectance image cubes from paintings collected with hyperspectral cameras

Knowledge of pigments and their distribution in paintings can inform conservators and scholars to better understand, and conserve these objects. In recent years, a variety of macro-scale non-contact imaging modalities have been applied to paintings to create classification/material maps. One modality, reflectance imaging spectroscopy (RIS) captures the diffuse reflectance spectral signatures of the artists’ materials present in the objects which can be used for classification (by defining regions of the image cube that have the same spectral reflectance signature). In Cultural Heritage Science the ENVI spectral hourglass wizard (ENVI-SHW) has been used to create classification maps from RIS. This method is slow and requires an expert user input during the processing in order to find the reflectance spectra that define the spectral classes (spectral endmembers or exemplars). The pigments are then deduced from spectral features in endmember spectra and from other non-invasive analytical chemical analysis of sites defined by the RIS maps. This paper explores the potential of more automated algorithms, such as maximum distance (MaxD), that are based on the theory of convex hulls for doing these classifications as well as spectral matching algorithms to an optimal spectral library. While all of these algorithms were developed for Remote Sensing community, MaxD and the library matching examine all the spectra in the reflectance image cube, require little user input, and are reproducible and fast. The results from the analysis of reflectance image cubes (400 to 950 nm) of three paintings with MaxD and a spectral library matching algorithm are presented and compared to those obtained with the ENVI-SHW algorithm. The three paintings analyzed are the central panels from illuminated leafs, two from the Laudario of Sant’Agnese (c. 1340) painted by the Master of the Dominican Effigies, namely The Nativity with the Annunciation to the Shepherds and, Christ and the Virgin Enthroned with Forty Saints, and one cutting from a leaf from a Choir book, Saint Francis Receiving the Stigmata, painted by Cosmè Tura (c. 1470s). Specifically, the results include the spectral endmembers recovered from each method as well as the associated classification maps. The findings from this study show that the MaxD algorithm was able to find the majority of the reflectance spectral endmembers with little user input.

Identification and characterization of hydrothermally altered minerals using surface and space-based reflectance spectroscopy, in parts of south-eastern Rajasthan, India

Imaging spectroscopy has evolved as one of the most significant advancements due to contiguous spectral coverage and higher spectral resolution which enable mineral identification and mineral exploration. Many phyllosilicate and carbonate minerals show specific spectral absorption feature in the wavelength range of visible-to-near-infra-red region of electromagnetic spectrum. These spectral features enable delineation of different mineral assemblages which in turn help in mineral prospecting using hyperspectral imaging spectra. The present study is focussed on evaluation and application of EO-1 Hyperion (hyperspectral) data as an Earth Observation tool for mineral detection and mapping in parts of Udaipur district in south-eastern Rajasthan. Hyperion reflectance imagery of this area was analysed using spectral angle mapper after pre-processing, atmospheric correction and geometric correction. Five endmembers, viz. dolomite, montmorillonite, chlorite, phlogopite and serpentine, were derived from both atmospherically corrected image and from rock samples in the laboratory using ASD field spectroradiometer covering spectral range of 0.4–2.5 µm. The reflectance spectra of endmembers derived from satellite image were initially compared with USGS mineral spectral library, and then after comparing with laboratory-based spectra with respect to absorption features, target minerals were identified which shows more than 70% match with the USGS and laboratory spectra. These minerals were also cross-checked with the reported litho-sequence of the area. Minerals derived from laboratory and image spectra are indicative of hydrothermally altered outer thermal aureole which is also corroborated by litho-structural association of the area.

Sub-pixel Mapping of Copper- and Iron-Bearing Metamorphic Rocks Using ASTER Data: A Case Study of Toutak and Surian Complexes, NE Shiraz, Iran

Metamorphic rock units of Toutak and Surian complexes located in the Sanandaj–Sirjan metamorphic zone, NE Shiraz, Iran, were mapped using the matched filtering (MF) method on the visible near-infrared (VNIR) and shortwave infrared (SWIR) bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) sensor. These complexes host magnetite and Cu–Zn–Ag volcanogenic massive sulfide (VMS) deposits. Mixed pixels of lithologic–mineralogic indicators of these deposits were detected to identify the prospecting areas. The spectra of end-members were extracted from imagery using sequential maximum angle convex cone (SMACC) model and were compared to the field and laboratory spectra to examine their characteristics. The key minerals including chlorite + epidote (greenschist), muscovite + biotite (mica schist), calcite + dolomite (marble), and muscovite + oligoclase (gneiss) were identified and were input to the algorithm for estimating the sub-pixel fractions of desired minerals. A generalized map of lithologies was then produced by combining the fraction maps of greenschist, mica schist, marble, and gneiss using a GIS. Finally, this lithologic association was applied to define the prospecting areas. The accuracy of results was assessed using R2 coefficient, field observations, and XRD analysis. The R2 coefficient was 0.74 for greenschist, 0.63 for mica schist, 0.65 for marble, and 0.53 for gneiss. The results also showed good agreement with field evidence, and thus, they can be used for finding potential targets for copper and iron deposits.

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.

Impact of water characteristics on the discrimination of benthic cover in and around coral reefs from imaging spectrometer data

Coral reefs are the foundation of productive ecosystems in the global, tropical oceans and are under threat from a variety of local to global scale stressors. Satellite imagery provides a tool to identify and understand the processes that control coral reef degradation, however due to the dynamic nature of seawater constituents, current spaceborne multispectral sensors cannot reliably discriminate between the many coral reef benthic classes necessary to detect change. Hyperspectral imagers may provide sufficient spectral resolution to estimate water column properties and differentiate benthic classes, however, the effects of depth, seawater constituents, and classification algorithm on the accuracy of benthic classifications have not been systematically assessed. Here, we simulate the ability of a spaceborne hyperspectral imager to accurately map fractional cover of coral reef benthic classes under a variety of conditions. Benthic reflectance is simulated by combining pure reflectance spectra of coral, algae, and sand and projecting these mixed spectra through a fully crossed set of water columns. We then use a semi-analytical optimization procedure to estimate the water column properties and multiple endmember spectral mixture analysis to estimate the fractional cover of the benthic classes using many independent endmember spectra. We compare our estimated benthic class fractions to the original, actual fractions used to produce the mixed coral reef spectra to quantify several measures of error. We found that multiple endmember spectral mixture analysis decreases fractional retrieval error, which is also reduced when the first derivative of the mixed and endmember spectra is used prior to unmixing. The estimation of fractional benthic class cover is most accurate for depths ≤3 m for most water conditions. Depths ≥5 m should be classified only if chlorophyll and sediment concentration are <0.1 mg m−3 and <0.1 g m−3, respectively. Our results indicate that the fractional cover of coral and algae should be at least 25% for accurate benthic class estimates (mean relative error < 50%), however there will be many ways to leverage the repeat measurements of a hyperspectral satellite sensor, such as a stable depth retrievals and benthic cover estimates, to produce more accurate and useful fractional cover data. We show how this simulation analysis can be used to generate maps of predicted benthic cover fractional retrieval uncertainty across a coral reef system using aerial hyperspectral imagery acquired over Hawaii, USA, although reef-specific, within pixel variations in depth and benthic class complexity should be considered.

Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms

Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed. First, the noisy endmember spectra are extracted by using the vertex component analysis algorithm. Then the singular spectrum analysis is used to denoise the endmember spectrum. When compared with the hyperspectral data as a whole, the amounts of endmember spectral data are known to be small. If only denoising endmember spectral data were to be performed, then the denoising time will be greatly improved, and image information can be effectively preserved. The method has high precision and fast speed for unmixing the noisy hyperspectral image. The advantages of this method will be more apparent when dealing with large amounts of hyperspectral data. In this article, different noise images are experimented with using this method, and strong experimental results are obtained.

Decomposition of mixed pixels in MODIS data using Bernstein basis functions

The decomposition of mixed pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) images is essential for the application of MODIS data in many fields. Many existing methods for unmixing mixed pixels use principal component analysis to reduce the dimensionality of the image data and require the extraction of endmember spectra. We propose the pixel spectral unmixing index (PSUI) method for unmixing mixed pixels in MODIS images. In this method, a set of third-order Bernstein basis functions is applied to reduce the dimensionality of the image data and characterize the spectral curves of the mixed pixels in a MODIS image, and then the derived PSUIs (i.e., the coefficients of the basis functions) are calibrated by means of the abundance values of the ground features from the Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) classification images corresponding to the date and region of the MODIS image. The proposed method was tested on MODIS and ETM+/OLI images, and it obtained satisfying unmixing results. We compared the PSUI method with conventional methods, including the pixel purity index, the N-finder algorithm, the sequential maximum angle convex cone, and vertex component analysis and found that the PSUI method outperformed the other four methods.

Identification of iron-bearing minerals based on HySpex hyperspectral remote sensing data

Our research group built a super-low altitude detection platform with power delta wings and mounted it with a HySpex hyperspectral sensor. This platform has great potential for extracting alteration information as it can provide high spectral and spatial resolution images. The aim of this study is to verify the reliability and accuracy of alteration information extraction using this platform. The seven-scene hyperspectral images of the Karatagh region in Hami of Xinjiang obtained by the platform in September 2017 were used to identify iron-bearing minerals. First, these images were preprocessed by splicing, atmospheric correction, and geometric correction. Second, the images were compared with the United States Geological Survey (USGS) standard spectra of similarities between the end-member spectra of goethite-, limonite-, and jarosite-altered minerals. Third, the three altered minerals were mapped in the test area based on the end-member spectrum. The results show that the correlations between the end-member spectra of three altered minerals, including goethite, limonite, and jarosite, obtained from the HySpex image and corresponding spectra in USGS reach 0.92873, 0.95098, and 0.8875, respectively. The end-member spectral reflectance values were higher than those in the spectral library. The original spectral data after the continuum-removal and the local quantitative display show that the spectrum at the feature absorption location was similar to the reference spectrum. On this basis, the distribution maps of goethite, limonite, and jarosite iron-stained altered minerals in the Hami Gobi area were produced and field verified. The data show that the information of identified minerals from the HySpex hyperspectral data were in accordance with the actual situation. Both methods can recognize jarosite well, the spectral angular mapping method is better than the back propagation (BP) neural network for goethite recognition, and the BP neural network of limonite recognition is better than the spectral angular mapping method. The above results prove that the alteration information can be effectively identified using the HySpex hyperspectral data integrated on a super-low altitude detection platform, which is of great significance to the rapid delineation of minerogenic prospects area.

MULTISPECTRAL AND HYPERSPECTRAL SATELLITE DATA FOR ALTERATION MAPPING USING PARTIAL-SUBPIXEL ALGORITHM IN AN UNEXPLORED REGION

Abstract. The focus of this paper is to comparatively evaluate the performance of ASTER and Hyperion data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs in an unexplored savannah region. The study employed the partial subpixel unmixing Mixture Tuned Match Filtering algorithm for spectral information extraction using the multispectral and hyperspectral satellite data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results show that the Hyperion, despite its distortions, can effectively discriminate associated alteration zones better than ASTER. Consequently, Hyperspectral data and analysis are thus recommended for use in similar unexplored regions for GT resource detection and monitoring.

Non‐linear unmixing of hyperspectral images using multiple‐kernel self‐organising maps

The spatial pixel resolution of common multispectral and hyperspectral sensors is generally not sufficient to avoid that multiple elementary materials contribute to the observed spectrum of a single pixel. To alleviate this limitation, spectral unmixing is a by-pass procedure which consists in decomposing the observed spectra associated with these mixed pixels into a set of component spectra, or endmembers, and a set of corresponding proportions, or abundances, that represent the proportion of each endmember in these pixels. In this study, a spectral unmixing technique is proposed to handle the challenging scenario of non-linear mixtures. This algorithm relies on a dedicated implementation of multiple-kernel learning using self-organising map proposed as a solver for the non-linear unmixing problem. Based on a priori knowledge of the endmember spectra, it aims at estimating their relative abundances without specifying the non-linear model under consideration. It is compared to state-of-the-art algorithms using synthetic yet realistic and real hyperspectral images. Results obtained from experiments conducted on synthetic and real hyperspectral images assess the potential and the effectiveness of this unmixing strategy. Finally, the relevance and potential parallel implementation of the proposed method is demonstrated.

Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs.

Spectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images. We propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error. The source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.

Globally standardized MODIS spectral mixture models

ABSTRACTStandardized substrate, vegetation, and dark (S, V, & D) spectral endmembers (EMs) are presented for spectral mixture analysis (SMA) of MODIS version 6 daily nadir looking BRDF-adjusted reflectance (daily NBAR). EMs are derived from a diverse collection of over 43,000,000MODIS spectra spanning 6 continents and all non-polar biomes. EM spectra are similar to those from decametre studies. Sensitivity analysis using 351 pairs of 27 EM combinations shows mean differences in SVD fraction estimates of 4±3%, 3±2%, and 3±2%, respectively. An additional snow EM is identified, but deemed tentative pending further cryospheric analysis. Vicarious validation with Landsat shows under 0.5% bias in V fraction, −7% and +11% biases in S and D fractions, and comparable dispersion (standard deviation 6% to 7%) in all fractions. Model misfit is low (root-mean-square error under 5% for 99.9% pixels). When V is compared to Normalized Difference, Enhanced, and Soil Adjusted Vegetation Indices (NDVI, EVI, SAVI), NDVI shows over twice the dispersion (standard deviation 29% vs 13–14%) and over 4 times the bias (+18% vs 4%) of EVI and SAVI. Combined with previous studies, these results extend the scaling linearity and low misfit of the global SVD linear mixture model from 2m up to 500m.

Hyperspectral Unmixing With Spectral Variability Using Adaptive Bundles and Double Sparsity

Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember classes), the spectral signature characterizing these classes may spatially vary due to intrinsic component fluctuations or external factors (illumination). These redundant multiple endmember spectra within each class adversely affect the performance of unmixing methods. This paper proposes a mixing model that explicitly incorporates a hierarchical structure of redundant multiple spectra representing each class. The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel. The resulting unmixing algorithm is able to adaptively recover several bundles of endmember spectra associated with each class and robustly estimate abundances. In addition, its flexibility allows a variable number of classes to be present within each pixel of the hyperspectral image to be unmixed. The proposed method is compared with other state-of-the-art unmixing methods that incorporate sparsity using both simulated and real hyperspectral data. The results show that the proposed method can successfully determine the variable number of classes present within each class and estimate the corresponding class abundances.

Theoretical Study in Estimating Mineral Compositions From Spectral Measurements With a Bayesian Approach

Summary We propose a systematic Bayesian method for inferring the mineral composition of rock samples from transmission Fourier-transform infrared spectroscopy (FTIR) measurements. Currently available FTIR inversion methodologies depend on measuring pure minerals, using them within least-squares approaches with a hand-tuned noise model, and implementing ad hoc post-processing of the obtained solution. Within the proposed data-driven framework, we replaced these previous inversion approaches with the automatic training and estimation steps. In this approach, the linear operator, comparable with the FTIR spectral standards (FTIR spectra of end-members or pure minerals), is estimated or trained on a calibration set of rock samples for which the mineral composition has been accurately determined by other means in the laboratory. With this linear operator, we then quantify the spectral-noise covariance matrix from the calibration set, which forms the basis of our estimation of the Bayesian posterior uncertainty on a mineral-composition estimate. This quantification of uncertainty in mineral estimation is a novel feature that can be used as a reliability measure. The uncertainty also describes the correlation between estimates of mineral-weight fractions, indicating which pairs of minerals cannot be independently estimated. Our Bayesian model also addresses the uncertainty propagated from the estimated linear operator and thus captures a possible mismatch of the model parameter from the true operator (i.e., semiblindness of the model). We demonstrate the advantages of our approach by performing experiments with synthetic data.

Mineral discrimination by combination of multispectral image and surrounding hyperspectral image

A hyperspectral (HS) imager is more effective than a multispectral (MS) imager in mineral discrimination, but spatial coverage of HS images is limited in comparison to that of MS images. Thus Kruse and Perry have proposed a method that uses coincident HS imaging and MS imaging data to extend mineral mapping to larger areas. We propose a method modified from the Kruse and Perry’s (K&P) method. Though the K&P method derives the MS-based endmember spectra by weighting the HS-based endmember spectra with the response functions of the MS sensor bands, the proposed method obtains the MS-based endmember spectra from surface reflectance spectra of the MS pixels at the same positions with the HS pixels selected as the HS-based endmembers in the overlapping area. The validation study using airborne visible/infrared imaging spectrometer and advanced spaceborne thermal emission and reflection radiometer images over Cuprite and Goldfield areas, Nevada, USA, demonstrates that the proposed method is more robust against spectral inconsistency between the HS- and the MS-images caused by calibration and/or atmospheric correction errors than the K&P method, though the proposed method is more sensitive to co-registration errors between the HS- and the MS-images than the K&P method.

Remote sensing satellite imagery for prospecting geothermal systems in an aseismic geologic setting: Yankari Park, Nigeria

The aim of this investigation is to evaluate the applicability of multispectral satellite imagery for hydrothermal alteration mineral mapping and thermal anomaly detection as a proxy for the characterization of subtle geothermal systems in an aseismic geologic setting. The Yankari Park, an area in northeastern Nigeria characterized by several surface manifestations of hotsprings and hydrothermally altered rock deposits was selected for this study. The study evaluates the effectiveness of the Independent Component Analysis and Mixture Tuned Matched Filtering (MTMF) algorithm for target detection of hydrothermal alteration zones and the Single Channel Algorithm for thermal anomaly detection associated with subtle geothermal systems using spectral bands of Landsat-8, ASTER and Landsat-7, respectively. The results indicated that the use of verified image endmember spectra gives more accurate results compared to that from library spectra by implementing MTMF. Results of mapping thermally anomalous pixels did not conform to known locations of the thermal springs, however, examining the spatial correlation of the anomaly areas with the major fault-fracture systems from the geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat conserving sedimentary formations. This investigation has significant implications as it signifies the need for the integrative use of alteration mineral mapping and thermal anomaly detection for cost-effective prefeasibility stage mapping of geothermal systems in an aseismic geologic setting located in African plate and other regions around the world.

NIRS quantification of lake sediment composition by multiple regression using end-member spectra

Here we develop a novel method for quantifying sediment components, e.g. biogenic silica, organic or mineral matter, from near infrared (NIR) spectra based on fitting by multiple regression of measured spectra for end-member materials. We show that with suitable end-members our new open-source multiple regression routine gives excellent simultaneous quantification of the major components of a sediment, the concentrations comparing well with independent methods of quantification. Widely used partial least squares regression approaches rely on large environmental training data sets; our method produces comparable results, but with the advantages of negating the need for a training dataset and with greater simplicity and theoretical robustness. We demonstrate that component NIR spectra are additive, a prerequisite for use of multiple regression to un-mix the compound spectra, and show that a number of environmental materials make suitable end-members for this analysis. We show that spectral mixing is not conservative with respect to mass proportion, but rather to the relative chromatic intensity of contributing sediment components. Concentrations can be calculated using the measured spectra by correction using a chromatic intensity factor, the value of which can be measured independently. We have applied our approach to a postglacial sediment sequence from Loch Grannoch (SW Scotland) and reveal a down-core pattern of varying dominance by biogenic silica, organic and mineral content from the late glacial to present. With isolation and measurement of appropriate end-members this multivariate regression approach to interrogating NIR spectra has utility across a wide range of sedimentary environments and potentially for other spectral analytical methods.

A dynamic soil endmember spectrum selection approach for soil and crop residue linear spectral unmixing analysis

Crop residue deposited on the soil surface helps protect against water and wind erosion, improve soil quality and increase soil organic matter and soil carbon storage. Linear spectral mixture analysis (LSMA) is an important technique in field crop residue estimation. Traditionally, only one single or fixed standard crop residue and soil endmember spectrum is performed for each of the presented endmember classes or field. Because the variation in soil endmember spectrum signatures significantly changes with soil moisture (SM), spectral unmixing with fixed endmember spectra can lead to poor accuracy of the abundance of the spectral constituents of pure crop residue. Herein, this paper presents a dynamic soil endmember spectrum selection approach for improving the performance of soil and rice residue spectral unmixing analysis in rice residue cover (RRC) estimation. This new approach uses SM and soil spectral reflectance model to modify soil endmember spectra in each spectral unmixing analysis. Two validation datasets computed results have verified the feasibility and correctness of the dynamic soil endmember method. Results indicated that the distribution of SM in farmland was crucial for RRC estimation. Compared with traditional fixed min and fixed mean soil endmember spectrum methods, the results of the method developed herein showed this method significantly improved RRC estimation accuracy for an SM content lower than 20% (volumetric water content) over the traditional methods tested. Therefore, our proposed approach can be used to improve RRC estimation accuracy in harvest field.

Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis.

The accurate assessment of rice yield is crucially important for China’s food security and sustainable development. Remote sensing (RS), as an emerging technology, is expected to be useful for rice yield estimation especially at regional scales. With the development of unmanned aerial vehicles (UAVs), a novel approach for RS has been provided, and it is possible to acquire high spatio-temporal resolution imagery on a regional scale. Previous reports have shown that the predictive ability of vegetation index (VI) decreased under the influence of panicle emergence during the later stages of rice growth. In this study, a new approach which integrated UAV-based VI and abundance information obtained from spectral mixture analysis (SMA) was established to improve the estimation accuracy of rice yield at heading stage. The six-band image of all studied rice plots was collected by a camera system mounted on an UAV at booting stage and heading stage respectively. And the corresponding ground measured data was also acquired at the same time. The relationship of several widely-used VIs and Rice Yield was tested at these two stages and a relatively weaker correlation between VI and yield was found at heading stage. In order to improve the estimation accuracy of rice yield at heading stage, the plot-level abundance of panicle, leaf and soil, indicating the fraction of different components within the plot, was derived from SMA on the six-band image and in situ endmember spectra collected for different components. The results showed that VI incorporated with abundance information exhibited a better predictive ability for yield than VI alone. And the product of VI and the difference of leaf abundance and panicle abundance was the most accurate index to reliably estimate yield for rice under different nitrogen treatments at heading stage with the coefficient of determination reaching 0.6 and estimation error below 10%.