Hyperspectral Measurements Research Articles

Use of hyperspectral reflectance to non-destructively estimate seagrass Zostera noltei biomass

Seagrasses are marine plants ranked worldwide amongst the most valuable coastal ecosystems. The global conservation effort requires accurate estimation of seagrass abundance and taxonomic composition. Hyperspectral reflectance has been used to estimate seagrass biomass, despite requiring destructive laboratory measurements that goes against conservation principles.Therefore, we assessed the efficiency of Zostera noltei hyperspectral reflectance to estimate its aboveground biomass in intertidal areas, having Ria de Aveiro coastal lagoon (Portugal) as a case study, with seasonal samplings during a whole year comparing (non-destructive) field- and (destructive) laboratory-based measurements of the spectral signature. Several vegetation indices (VIs) that focus on the spectral absorption of specific pigments were also tested: Normalized Difference Vegetation Indices (NDVI)-like ratios, Double Difference (DD)-like Ratios and Simple Ratios (SR).Here we show that Z. noltei hyperspectral signature, measured in the field and in the laboratory, is clearly related to the aboveground biomass of the plant along the year. We found that among the VIs, NDVI-like ratios showed higher correlation with Z. noltei aboveground biomass than the other types of VIs. Taking into consideration the sensitivity to biomass saturation, the NDVI705 was identified as the best vegetation index to estimate Z. noltei biomass during the whole year and in both type of measurements, up to a saturation level, also demonstrating the successful use of non-destructive methods.The suitability of hyperspectral reflectance measurements in order to estimate Z. noltei aboveground biomass was confirmed, confirming also the potential efficient use of field-based measurements (non-destructive) for conservation practices and sustainable management of the species in intertidal ecosystems and allowing aboveground biomass to be potentially proposed as a metric for the evaluation of seagrass healthy condition under EU policies (e.g. Water Framework Directive, Marine Strategy Framework Directive).

Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification

Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.

Relation between leaf area index and NDVI for subarctic deciduous vegetation

ABSTRACT We consider the relationship between leaf area index (LAI) and normalized difference vegetation index (NDVI) for green-leaf vegetation from a subarctic study site, specifically to test whether relationships optimized for lower-latitude vegetation can be assumed to hold at higher latitudes. We focus attention particularly on dwarf-shrub vegetation, which has received little previous investigation. We have collected hyperspectral measurements of the optical properties (reflectance and absorptance) of single leaves from dwarf shrub and tree species common to northern European Russia, and have developed a simple physical model of the properties of assemblages (‘leaf stacks’) of these leaves. The model is shown to provide a satisfactory explanation of the effect of varying the number of leaves in a stack on its NDVI, and can be easily adapted to make simple measurements using relatively inexpensive equipment. Our results show that the LAI-NDVI relationship for a vegetation canopy will saturate (approach within 10% of its limiting value) when the LAI reaches a value of around 2 to 3. Values this low are not uncommon in subarctic vegetation. It is also shown that dwarf shrub vegetation may show lower NDVI than trees for the same LAI.

RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits

Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (Cab), carotenoids (Ccx), and water (Cw) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural Ccx-Cab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for Ccx, R2 = 0.85 (i) and 0.53 (ii) for Cab, and R2 = 0.97 (i) and 0.67 (ii) for Cw, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (Ccx: R2 = 0.86, RMSE = 2.1 μg cm−2; Cab: R2 = 0.67, RMSE = 12.5 μg cm-2; Cw: R2 = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding Cab (R2 = 0.84; RMSE = 9.06 μg cm-2) and canopy water content (CWC, R2 = 0.70; RMSE = 0.05 cm). Retrieved Ccx values were reasonable according to Cab-Ccx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite.

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

Spatial-Spectral Evidence of Glare Influence on Hyperspectral Acquisitions.

Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers (CCs) in three different lighting conditions, with different backgrounds, different exposure times, and different orientations. The reflectance spectra obtained from the imaging system have been compared to pointwise reference measures obtained with contact spectrophotometers. To assess and identify the influence of glare, we present the Glare Effect (GE) index, which compares the contrast of the grayscale patches of the CC in the hyperspectral images with the contrast of the reference spectra of the same patches. We evaluate, in both spatial and spectral domains, the amount of glare affecting every hyperspectral image in each acquisition scenario, clearly evidencing an unwanted light contribution to the reflectance spectra of each point, which increases especially for darker pixels and pixels close to light sources or bright patches.

Inversion of multiangular polarimetric measurements from the ACEPOL campaign: an application of improving aerosol property and hyperspectral ocean color retrievals

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the time frame of late 2022 to early 2023, will carry the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer, and two multiangle polarimeters (MAPs), the UMBC Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). One purpose of the PACE MAPs is to better characterize aerosol properties, which can then be used to improve atmospheric correction for the retrieval of ocean color in coastal waters. Though this is theoretically promising, the use of MAP data in the atmospheric correction of colocated hyperspectral ocean color measurements have not yet been well demonstrated. In this work, we performed aerosol retrievals using the MAP measurements from the Research Scanning Polarimeter (RSP) and demonstrate its application to the atmospheric correction of hyperspectral radiometric measurements from SPEX airborne. Both measurements were collected on the same aircraft from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign in 2017. Two cases over ocean with small aerosol loading (aerosol optical depth ∼0.04) are identified including colocated RSP and SPEX airborne measurements and Aerosol Robotic Network (AERONET) ground-based observations. The aerosol retrievals are performed and compared with two options: one uses reflectance measurement only and the other uses both reflectance and polarization. It is demonstrated that polarization information helps reduce the uncertainties of aerosol microphysical and optical properties. The retrieved aerosol properties are then used to compute the contribution of atmosphere and ocean surface for atmospheric correction over the discrete bands from RSP measurements and the hyperspectral SPEX airborne measurements. The water-leaving signals determined this way are compared with both AERONET and Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color products for performance analysis. The results and lessons learned from this work will provide a basis to fully exploit the information from the unique combination of sensors on PACE for aerosol characterization and ocean ecosystem research.

The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda

Bananas and plantains provide food and income for more than 50 million smallholder farmers in East and Central African (ECA) countries. However, banana productivity generally achieves less than optimal yield potential (<30%) in most regions, including Uganda. Numerous studies have been undertaken to identify the key challenges that smallholder banana growers face at different stages of the banana value chain, with one of the main constraints being a lack of policy-relevant agricultural data. The World Bank (WB) initiated a methodological survey design aimed at identifying the distribution of banana varieties across a number of key Ugandan growing regions, at the individual household scale. To achieve this outcome a number of approaches including ground-based surveys, DNA tissue collection of selected banana plants and remote sensing were evaluated. For the remote sensing component, the set objectives were to develop statistical models from the hyperspectral reflectance properties of individual leaves that could differentiate typical ECA banana varieties, as well as their parentage (usage). The study also explored the potential of extrapolating the ground-based hyperspectral measures to high-resolution WorldView-3 (WV3) satellite imagery, therefore creating the potential of mapping the distribution of banana varieties at a regional scale. The DNA testing of 43 banana varieties propagated at the National Banana Research Program site at National Agricultural Research Organization (NARO) research station in Kampala, Uganda, identified 12 genetically different varieties. A canonical powered partial least square (CPPLS) model developed from hyperspectral reflectance properties of the sampled banana leaves successfully differentiated BLU, BOG, GON, GRO and KAY genotypes. The Random Forest (RF) algorithm was also evaluated to determine if spectral bands coinciding with those provided by WV3 data could segregate banana varieties. The results suggested that this was achievable and as such presents an opportunity to extrapolate the hyperspectral classifications to broader areas of land. The ability to spectrally differentiate these five genotypes has merit as they are not typical east African varieties. As such, identifying the distribution and density of these varieties across Uganda provides vital information to the banana breeders of NARO of where their new varieties are being disseminated too, data that has been previously difficult to obtain. Although the results from this pilot study indicated that not all banana varieties could be spectrally differentiated, the methodology developed and the positive results that were achieved do present remote sensing as a complimentary technology to the ongoing surveying of banana and other crop types grown within Ugandan household farming systems.

Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 &lt; 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.

Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields

Crop nitrogen (N) needs to be accurately predicted to allow farmers to effectively match the N supply to the crop N demand during crop growth in order to minimize environmental impacts as excess N could seep into the water supplies around the field. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral MicaSense imagery validated with ground hyperspectral measurements to predict canopy nitrogen weight (g/m2) of wheat and cornfields in Ontario. A simple linear regression was established to predict the canopy nitrogen weight from various vegetation indices (VI). Ratio Vegetation Index (RVI) performed the best out of all the tested vegetation indices, with an R 2 of 0.93 for the wheat fields and 0.83 for the corn fields. RVI estimation was also consistent throughout the growing season, which is optimal in precision agriculture. Once applied the RVI-based regression model to the UAV imagery, the best RMSE was 0.95 g/m2 for the wheat McColl field using the image of May 24th and 0.66 g/m2 for the corn Jack North field using the image of June 7th. Such information for accurately predicting nitrogen is important for farmers as it will lead to a more efficient fertilizer application program.

Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods

Remote sensing-based discrimination and mapping of tea (Camellia sinensis) plantations are valuable for efficient management of inventory and optimization of resources by the tea production industry. Apart from the diverse tea plant varieties, growth of natural plant species is a common scenario in tea plantations. The objective of this research is spectral discrimination of nine popular tea plant varieties in the presence of six natural plant species in Munnar, Western Ghats of India. Canopy level hyperspectral reflectance measurements acquired for tea and natural plant species were analyzed using several statistical, and machine learning methods namely, k-nearest neighbourhood classifier (k-NN), linear discriminant analysis (LDA), support vector machines (SVM), normalized spectral similarity score (NS3), maximum likelihood classifier (MLC), and artificial neural networks (ANNs). In addition, the existence and statistical significance of the spectral separability among 15 tea and natural plant species was assessed by non-parametric MANOVA. Results indicate that six out of nine tea plant varieties could be discriminated with accuracies between 75% and 80%. The presence of natural plant species has decreased the inter-species spectral variability for a few tea plant varieties. However, there has been enhanced spectral variability for a few other tea plant varieties. The presence of natural plant species does not need to be disadvantageous to the spectral discrimination of tea species.

Improved Severe Weather Forecasts Using LEO and GEO Satellite Soundings

AbstractIt is shown here that improvements in numerical weather prediction (NWP) model forecasts of hazardous weather can be obtained by assimilating profile retrievals obtained in real time from combined direct broadcast system (DBS) polar satellite hyperspectral and geostationary satellite multispectral radiance data. Results of NWP model forecasts are shown for two recent tornado outbreak cases: 1) the 3 March 2019 tornado outbreak over the southeast United States and 2) the tornado outbreak that occurred across Illinois, Indiana, and Ohio during the night of 27 May and the morning of 28 May 2019, and 3) the 4 March 2019 severe precipitation event that occurred in southeast China. Improvements in both quantitative precipitation forecasts (QPFs) and predictions of the location of tornado occurrence are obtained. It is also shown that geostationary satellite hyperspectral soundings [i.e.,Fengyun-4A(FY-4A) Geosynchronous Interferometric Infrared Sounder (GIIRS)] further improve hazardous precipitation forecasts when used, in addition to the combined polar hyperspectral and geostationary multispectral satellite profile data, to initialize the numerical forecast model. The lowest false alarm rate (FAR) and the highest probability of detection (POD) and critical success index (CSI) scores are achieved when assimilating atmospheric profile retrievals obtained by combining all the available satellite high-vertical-resolution hyperspectral radiance measurements with geostationary satellite high-spatial-resolution and high-temporal-resolution multispectral radiance measurements.

Practical Recommendations for Hyperspectral and Thermal Proximal Disease Sensing in Potato and Leek Fields

Thermal and hyperspectral proximal disease sensing are valuable tools towards increasing pesticide use efficiency. However, some practical aspects of the implementation of these sensors remain poorly understood. We studied an optimal measurement setup combining both sensors for disease detection in leek and potato. This was achieved by optimising the signal-to-noise ratio (SNR) based on the height of measurement above the crop canopy, off-zenith camera angle and exposure time (ET) of the sensor. Our results indicated a clear increase in SNR with increasing ET for potato. Taking into account practical constraints, the suggested setup for a hyperspectral sensor in our experiment involves (for both leek and potato) an off-zenith angle of 17°, height of 30 cm above crop canopy and ET of 1 ms, which differs from the optimal setup of the same sensor for wheat. Artificial light proved important to counteract the effect of cloud cover on hyperspectral measurements. The interference of these lamps with thermal measurements was minimal for a young leek crop but increased in older leek and after long exposure. These results indicate the importance of optimising the setup before measurements, for each type of crop.

Low-Cost Hyperspectral Imaging System: Design and Testing for Laboratory-Based Environmental Applications.

The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.

Radiometric consistency between GOES-16 ABI and VIIRS on Suomi NPP and NOAA-20

NOAA has both geostationary (GEO) and polar orbiting (LEO) satellites that are procured through different vendors. For long-term environmental studies, it is essential to establish consistent radiometric calibration among satellite instruments irrespective of the satellite platforms. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites has been providing critical weather and climate-related data. With the launch of NOAA-20 in November 2017, the global coverage of VIIRS has doubled. With more than 7 years in space, S-NPP VIIRS has been rigorously calibrated and validated. We aim to quantify the radiometric consistency between the two VIIRS instruments through double differencing using GOES-16 Advanced Baseline Imager (ABI) as a reference instrument. We also provide insight into the temporal radiometric consistency between VIIRS and ABI. Since no direct simultaneous nadir overpasses (SNOs) exist between SNPP and NOAA-20 for intercomparison, GEO-LEO intercalibration is performed using SNOs between ABI and VIIRS instruments. We show that the NOAA-20 VIIRS measured top-of-atmosphere (TOA) reflectance is consistently lower than that of the S-NPP, mostly on the order of 2% to 3%, consistent with the past studies. GOES-16 ABI-observed reflectance is higher than both VIIRS instruments. SNOs over the all-sky tropical ocean are analyzed to quantify the bias for both NOAA-20 and S-NPP VIIRS relative to ABI. The impact on bias due to spectral differences is accounted for using spectral band adjustment factors estimated using hyperspectral measurements from SCIAMACHY. Uncertainties exist mainly due to calibration uncertainties in VIIRS and ABI, the time difference between the VIIRS and ABI observations, differences in cloud contamination, spectral response function differences, and lack of in-situ hyperspectral data to account for the spectral bias.

Detection and community-level identification of microbial mats in the McMurdo Dry Valleys using drone-based hyperspectral reflectance imaging

AbstractThe reflectance spectroscopic characteristics of cyanobacteria-dominated microbial mats in the McMurdo Dry Valleys (MDVs) were measured using a hyperspectral point spectrometer aboard an unmanned aerial system (remotely piloted aircraft system, unmanned aerial vehicle or drone) to determine whether mat presence, type and activity could be mapped at a spatial scale sufficient to characterize inter-annual change. Mats near Howard Glacier and Canada Glacier (ASPA 131) were mapped and mat samples were collected for DNA-based microbiome analysis. Although a broadband spectral parameter (a partial normalized difference vegetation index) identified mats, it missed mats in comparatively deep (&gt; 10 cm) water or on bouldery surfaces where mats occupied fringing moats. A hyperspectral parameter (B6) did not have these shortcomings and recorded a larger dynamic range at both sites. When linked with colour orthomosaic data, B6 band strength is shown to be capable of characterizing the presence, type and activity of cyanobacteria-dominated mats in and around MDV streams. 16S rRNA gene polymerase chain reaction amplicon sequencing analysis of the mat samples revealed that dominant cyanobacterial taxa differed between spectrally distinguishable mats, indicating that spectral differences reflect underlying biological distinctiveness. Combined rapid-repeat hyperspectral measurements can be applied in order to monitor the distribution and activity of sentinel microbial ecosystems across the terrestrial Antarctic.

Энтропийные методы моделирования самоорганизующихся систем по данным гиперспектрального зондирования

Для исследования сложных самоорганизующихся систем создаются различные математические модели. В геосистемах детерминированный характер действий обусловлен их стохастическими качествами. В рассматриваемых системах регулярные детерминированные процессы формируются многочисленными случайными межэлементными взаимодействиями, происходящими на микроуровне. Во многих случаях корректно вывести детерминированный закон эволюции исследуемой системы или ее части невозможно в связи с большим количеством непредсказуемых и неизвестных факторов, влияющих на нее. Однако на микроуровне доступны для наблюдения статистические распределения элементов системы, что позволяет прогнозировать ее поведение и оценивать факторы, действующие на систему. Наиболее универсальные методы моделирования систем со стохастическими свойствами основаны на фундаментальных понятиях статистической механики - информационных энтропиях Гиббса-Шеннона и Реньи. В статье исследуются энтропийные методы вычислений количественных оценок состояния пространственно-распределенных геосистем и их дивергенции в процессе самоорганизации: -расхождений, расхождений одуванчика, вариабельности спектра размерностей Реньи. Рассмотрены особенности исследования систем с мультифрактальными структурами по данным гиперспектральных измерений. На примерах иллюстрируется применение энтропийных моделей в многочисленных экспериментах с реальными данными, полученными из месторождения природного газа. Основываясь на данных гиперспектральных излучений гомогенного растительного покрова, производится верификация применения энтропийных методов для определения границ углеводородов.

Determination of the remote-sensing reflectance from above-water measurements with the "3C model": a further assessment.

The Three-Component Reflectance Model (3C) was primarily developed to improve the determination of the remote-sensing reflectance (Rrs) from above-water radiometric hyperspectral measurements performed during sub-optimal conditions (i.e., cloudy sky, variable viewing geometry, high glint perturbations, low illumination conditions). In view of further validating the model and showing its broad range of uses, this work presents the application of 3C to above-water radiometry data collected in oceanic and coastal waters with a variety of measurement conditions. Rrs derived from measurements performed during optimal and slightly sub-optimal conditions exhibit equivalence with Rrs obtained with an established above-water method that is commonly used to support ocean color validation activities. Additionally, the study shows that 3C can still provide relevant Rrs retrievals from field data characterized by low-light illumination, high glint perturbations and variable measurement geometries, for which the established method cannot be confidently applied. Finally, it is shown that the optimization residual returned by the 3C full-spectrum inversion procedure can be a potential relative indicator to assess the quality of derived Rrs.

Preclinical Imaging and Spectroscopy in the NIR-II Window with Single-Walled Carbon Nanotubes

We developed a near-infrared small animal imaging system, IR VIVO, that provides real-time images, videos, and spectral imaging in the NIR-I and shortwave infrared (SWIR or NIR-II) regions. We found that SWIR wavelengths can give optimal resolution for in vivo optical imaging of deep organs in mice due to the low tissue autofluorescence, scattering and absorption of light at these wavelengths. We also show how this higher penetration depth can enable the detection of small wavelength changes in the emission of carbon nanotube-based sensors implanted in vivo using a continuously tunable filter.First, we demonstrated superior image clarity and penetration depth at NIR-II wavelengths (1000-1700 nm) in vivo using an FDA approved dye, indocyanine green (ICG). We used 780 nm excitation and detection using a Zephir 1.7 InGaAs camera and 1250 nm long-pass emission filter. We found low tissue autofluorescence at 1250 nm. The ICG dye imaging resulted in superior visualization of microvasculature, perfusion, measurement of heart rate, respiratory rate, hepatobiliary, and intestinal contractions. Further anatomical and functional imaging was carried out by looking at the kinetics of ICG, resulting in identification of different organs.In vivo near-infrared hyperspectral imaging of carbon nanotubes was conducted using a continuously-tunable filter in the imager. A diffraction volume Bragg grating (VBG), was used to provide narrow-band wavelength selection. Coupled with a homogeneous global illumination, spectrally-defined images were acquired in the entire field of view. The result was a dataset containing both 2D spatial information and the full spectrum for each point in the image. We conducted near-infrared hyperspectral imaging of single-walled carbon nanotubes to measure small wavelength changes of the nanotubes implanted into live mice. Hyperspectral measurements of carbon nanotube sensors for lipids in the liver as well as implantable sensors for doxorubicin were conducted, facilitating liver disease monitoring and drug pharmacokinetics measurements.We believe wide-area NIR-II imaging and spectral/hyperspectral measurements have broad potential applications for the use of carbon nanotubes and other NIR-I/II materials in basic materials/biology, translational, and clinical work, including sensor arrays, point-of-care measurements, implants, whole-animal imaging, and intraoperative/surgical imaging.

Information content of absorption spectra and implications for ocean color inversion.

The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.