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Special Issue "Recent Progress and Developments in Imaging Spectroscopy"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editors

Guest Editor
Dr. Mathias Kneubühler

Remote Sensing Laboratories (RSL), Department of Geography, University of Zurich
Website | E-Mail
Interests: imaging spectroscopy; multi-angular measurements; sensor calibration and validation; ecosystems
Guest Editor
Prof. Dr. Alexander Damm

Department of Geography, University of Zurich, Switzerland
Website | E-Mail
Interests: fluorescence spectroscopy, remote sensing of vegetation, plant water relations, carbon and water cycle, plant photosynthesis, ecosystem functioning and environmental change
Guest Editor
Dr. Andreas Müller

German Aerospace Center (DLR), German Remote Sensing Data Center, International Ground Segment, Oberpfaffenhofen D-82234 Wessling, Germany
Website | E-Mail
Interests: imaging spectroscopy; applications; airborne flight campaigns; sensor calibration and validation; ground segment

Special Issue Information

Dear Colleagues,

Imaging spectroscopy is increasingly finding its way into transdisciplinary research aiming to integrate state-of-the-art methods and data analysis concepts in response to today’s key environmental and societal challenges. The European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group (SIG) on Imaging Spectroscopy aims at encouraging interdisciplinary discussions among specialists working with innovative Earth Observation methods and technologies.

With the 10th workshop jointly organized by EARSeL and the University of Zurich (Switzerland) in April 2017, the SIG Imaging Spectroscopy workshop series celebrates its 20th anniversary.

As a follow-up to the workshop, we are calling for papers on the work presented at the 10th EARSeL SIG Imaging Spectroscopy Workshop. In addition to this, we welcome papers from the global research community actively involved in research involving imaging spectroscopy. As such, the special issue is open to anyone doing research in this field. The selection of papers for publication will depend on quality and rigor of research. Specific topics include, but are not limited to:

Advanced methods for spectroscopy calibration, data processing, and archiving

  • Sensor calibration and product validation
  • Software systems for imaging spectroscopy
  • Big data and data mining
  • Inversion schemes and data assimilation
  • In-situ, field and laboratory spectroscopy
  • Atmospheric compensation techniques
  • Spectral databases and information systems
  • Very high resolution spectroscopy
  • Statistical and computational methods for data analysis

Integrated approaches in Earth System Science using spectroscopy

  • Combined use of Earth Observation technologies (LiDAR, SAR, etc. and spectroscopy)
  • Forward and inverse modeling of spheres
  • Sphere specific analysis methods (atmosphere, biosphere, hydrosphere, pedosphere, anthroposphere)
  • Ecosystem processes and functions in vegetated ecosystems, soils, snow and ice, atmosphere, coastal and inland waters, urban areas
  • Scaling, interactions and feedback mechanisms between and across spheres
  • Transdisciplinary applications and Ecosystem Services
  • Spectroscopy in the context of societal challenges (water scarcity, food security, biodiversity loss, etc.)

Next generation platforms and sensors

  • Spectroscopy from ground, drone, air- and spaceborne platforms
  • Visible, near-, mid- and thermal infrared spectral and multi-angular spectral measurements
  • Emerging concepts, technologies and missions

Dr. Mathias Kneubühler
Dr. Alexander Damm
Dr. Andreas Müller
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensor Calibration
  • Product Validation
  • Platforms
  • High Resolution Spectroscopy
  • VIS
  • NIR
  • SWIR
  • Multi-Angular Measurements
  • Spectral Data Bases
  • Forward and Inverse Modeling
  • Sphere Specific Analysis Methods
  • Ecosystem Processes and Functions
  • Ecosystem Services (ESS)
  • New Concepts and Technologies
  • Future Missions

Published Papers (16 papers)

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Editorial

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Open AccessEditorial Recent Progress and Developments in Imaging Spectroscopy
Remote Sens. 2018, 10(9), 1497; https://doi.org/10.3390/rs10091497
Received: 12 September 2018 / Accepted: 18 September 2018 / Published: 19 September 2018
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(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)

Research

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Open AccessArticle Mapping Asphaltic Roads’ Skid Resistance Using Imaging Spectroscopy
Remote Sens. 2018, 10(3), 430; https://doi.org/10.3390/rs10030430
Received: 16 January 2018 / Revised: 22 February 2018 / Accepted: 5 March 2018 / Published: 10 March 2018
Cited by 1 | PDF Full-text (3226 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this study is to evaluate a realistic feasibility of using hyperspectral remote sensing (also termed imaging spectroscopy) airborne data for mapping asphaltic roads’ transportation safety. This is done by quantifying the road-tire friction, an attribute responsible for vehicle control and
[...] Read more.
The purpose of this study is to evaluate a realistic feasibility of using hyperspectral remote sensing (also termed imaging spectroscopy) airborne data for mapping asphaltic roads’ transportation safety. This is done by quantifying the road-tire friction, an attribute responsible for vehicle control and emergency stopping. We engaged in a real-life operational scenario, where the roads’ friction was modeled against the reflectance information extracted directly from the image. The asphalt pavement’s dynamic friction coefficient was measured by a standardized technique using a Dynatest 6875H (Dynatest Consulting Inc., Westland, MI, USA) Friction Measuring System, which uses the common test-wheel retardation method. The hyperspectral data was acquired by the SPECIM AisaFenix 1K (Specim, Spectral Imaging Ltd., Oulu, Finland) airborne system, covering the entire optical range (350–2500 nm), over a selected study site, with roads characterized by different aging conditions. The spectral radiance data was processed to provide apparent surface reflectance using ground calibration targets and the ACORN-6 atmospheric correction package. Our final dataset was comprised of 1370 clean asphalt pixels coupled with geo-rectified in situ friction measurement points. We developed a partial least squares regression model using PARACUDA-II spectral data mining engine, which uses an automated outlier detection procedure and dual validation routines—a full cross-validation and an iterative internal validation based on a Latin Hypercube sampling algorithm. Our results show prediction capabilities of R2 = 0.632 for full cross-validation and R2 = 0.702 for the best available model in internal validation, both with significant results (p < 0.0001). Using spectral assignment analysis, we located the spectral bands with the highest weight in the model and discussed their possible physical and chemical assignments. The derived model was applied back on the hyperspectral image to predict and map the friction values of every road pixel in the scene. Combining the standard method with imaging spectroscopy may provide the required expansion of the available data to furnish decision makers with a full picture of the roads’ status. This technique’s limitations originate mainly in compositional variations between different roads, and the requirement for the application of multiple calibrations between scenes. Possible improvements could be achieved by using more spectral regions (e.g., thermal) and additional remote sensing techniques (e.g., LIDAR) as well as new platforms (e.g., UAV). Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessFeature PaperArticle Optimized Spectrometers Characterization Procedure for Near Ground Support of ESA FLEX Observations: Part 1 Spectral Calibration and Characterisation
Remote Sens. 2018, 10(2), 289; https://doi.org/10.3390/rs10020289
Received: 31 December 2017 / Revised: 3 February 2018 / Accepted: 10 February 2018 / Published: 13 February 2018
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Abstract
The paper presents two procedures for the wavelength calibration, in the oxygen telluric absorption spectral bands (O2-A, λc = 687 nm and O2-B, λc = 760.6 nm), of field fixed-point spectrometers used for reflectance and Sun-induced fluorescence measurements. In the first
[...] Read more.
The paper presents two procedures for the wavelength calibration, in the oxygen telluric absorption spectral bands (O2-A, λc = 687 nm and O2-B, λc = 760.6 nm), of field fixed-point spectrometers used for reflectance and Sun-induced fluorescence measurements. In the first case, Ne and Ar pen-type spectral lamps were employed, while the second approach is based on a double monochromator setup. The double monochromator system was characterized for the estimation of errors associated with different operating configurations. The proposed methods were applied to three Piccolo Doppio-type systems built around two QE Pros and one USB2 + H16355 Ocean Optics spectrometers. The wavelength calibration errors for all the calibrations performed on the three spectrometers are reported and potential methodological improvements discussed. The suggested calibration methods were validated, as the wavelength corrections obtained by both techniques for the QE Pro designed for fluorescence investigations were similar. However, it is recommended that a neon emission line source, as well as an argon or mercury-argon source be used to have a reference wavelength closer to the O2-B feature. The wavelength calibration can then be optimised as close to the O2-B and O2-A features as possible. The monochromator approach could also be used, but that instrument would need to be fully characterized prior to use, and although it may offer a more accurate calibration, as it could be tuned to emit light at the same wavelengths as the absorption features, it would be more time consuming as it is a scanning approach. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment
Remote Sens. 2018, 10(2), 256; https://doi.org/10.3390/rs10020256
Received: 25 November 2017 / Revised: 16 January 2018 / Accepted: 3 February 2018 / Published: 7 February 2018
Cited by 2 | PDF Full-text (10379 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned airborne vehicles (UAV) equipped with novel, miniaturized, 2D frame format hyper- and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format
[...] Read more.
Unmanned airborne vehicles (UAV) equipped with novel, miniaturized, 2D frame format hyper- and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format 2D images, which provide redundant information about the object. Radiometric correction of spectral image data is important for eliminating any external disturbance from the captured data. Corrections should include sensor, atmosphere and view/illumination geometry (bidirectional reflectance distribution function—BRDF) related disturbances. An additional complication is that UAV remote sensing campaigns are often carried out under difficult conditions, with varying illumination conditions and cloudiness. We have developed a global optimization approach for the radiometric correction of UAV image blocks, a radiometric block adjustment. The objective of this study was to implement and assess a combined adjustment approach, including comprehensive consideration of weighting of various observations. An empirical study was carried out using imagery captured using a hyperspectral 2D frame format camera of winter wheat crops. The dataset included four separate flights captured during a 2.5 h time period under sunny weather conditions. As outputs, we calculated orthophoto mosaics using the most nadir images and sampled multiple-view hyperspectral spectra for vegetation sample points utilizing multiple images in the dataset. The method provided an automated tool for radiometric correction, compensating for efficiently radiometric disturbances in the images. The global homogeneity factor improved from 12–16% to 4–6% with the corrections, and a reduction in disturbances could be observed in the spectra of the object points sampled from multiple overlapping images. Residuals in the grey and white reflectance panels were less than 5% of the reflectance for most of the spectral bands. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards
Remote Sens. 2018, 10(2), 220; https://doi.org/10.3390/rs10020220
Received: 16 January 2018 / Revised: 27 January 2018 / Accepted: 30 January 2018 / Published: 1 February 2018
Cited by 2 | PDF Full-text (3959 KB) | HTML Full-text | XML Full-text
Abstract
This research focuses on the effect of trampling on vegetation in high-mountain ecosystems through the electromagnetic spectrum’s interaction with plant pigments, cell structure, water content and other substances that have a direct impact on leaf properties. The aim of the study was to
[...] Read more.
This research focuses on the effect of trampling on vegetation in high-mountain ecosystems through the electromagnetic spectrum’s interaction with plant pigments, cell structure, water content and other substances that have a direct impact on leaf properties. The aim of the study was to confirm with the use of fluorescence methods of variability in the state of high-mountain vegetation previously measured spectrometrically. The most heavily visited part of the High Tatras in Poland was divided into polygons and, after selecting the dominant species within alpine swards, a detailed analysis of trampled and reference patterns was performed. The Analytical Spectral Devices (ASD) FieldSpec 3/4 were used to acquire high-resolution spectral properties of plants, their fluorescence and the leaf chlorophyll content with the difference between the plant surface temperature (ts), and the air temperature (ta) as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR) used as reference data. The results show that, along tourist trails, vegetation adapts to trampling with the impact depending on the species. A lower chlorophyll value was confirmed by a decrease in fluorescence, and the cellular structures were degraded in trampled compared to reference species, with a lower leaf reflectance. In addition, at the extreme, trampling can eliminate certain species such as Luzula alpino-pilosa, for which significant changes were noted due to trampling. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessFeature PaperArticle Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery
Remote Sens. 2018, 10(2), 200; https://doi.org/10.3390/rs10020200
Received: 4 December 2017 / Revised: 8 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
Cited by 2 | PDF Full-text (15117 KB) | HTML Full-text | XML Full-text
Abstract
The atmospheric correction of optical remote sensing data requires the determination of aerosol and gas optical properties. A method is presented which allows the detection of the aerosol scattering effects from optical remote sensing data at spatial sampling intervals below 5 m in
[...] Read more.
The atmospheric correction of optical remote sensing data requires the determination of aerosol and gas optical properties. A method is presented which allows the detection of the aerosol scattering effects from optical remote sensing data at spatial sampling intervals below 5 m in cloud-free situations from cast shadow pixels. The derived aerosol optical thickness distribution is used for improved atmospheric compensation. In a first step, a novel spectral cast shadow detection algorithm determines the shadow areas using spectral indices. Evaluation of the cast shadow masks shows an overall classification accuracy on an 80% level. Using the such derived shadow map, the ATCOR atmospheric compensation method is iteratively applied on the shadow areas in order to find the optimum aerosol amount. The aerosol optical thickness is found by analyzing the physical atmospheric correction of fully shaded pixels in comparison to directly illuminated areas. The shadow based aerosol optical thickness estimation method (SHAOT) is tested on airborne imaging spectroscopy data as well as on photogrammetric data. The accuracy of the reflectance values from atmospheric correction using the such derived aerosol optical thickness could be improved from 3–4% to a level of better than 2% in reflectance for the investigated test cases. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessFeature PaperArticle An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests
Remote Sens. 2018, 10(2), 199; https://doi.org/10.3390/rs10020199
Received: 11 November 2017 / Revised: 16 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
Cited by 4 | PDF Full-text (11299 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Spatial information on forest functional composition is needed to inform management and conservation efforts, yet this information is lacking, particularly in tropical regions. Canopy foliar traits underpin the functional biodiversity of forests, and have been shown to be remotely measurable using airborne 350–2510
[...] Read more.
Spatial information on forest functional composition is needed to inform management and conservation efforts, yet this information is lacking, particularly in tropical regions. Canopy foliar traits underpin the functional biodiversity of forests, and have been shown to be remotely measurable using airborne 350–2510 nm imaging spectrometers. We used newly acquired imaging spectroscopy data constrained with concurrent light detection and ranging (LiDAR) measurements from the Carnegie Airborne Observatory (CAO), and field measurements, to test the performance of the Spectranomics approach for foliar trait retrieval. The method was previously developed in Neotropical forests, and was tested here in the humid tropical forests of Malaysian Borneo. Multiple foliar chemical traits, as well as leaf mass per area (LMA), were estimated with demonstrable precision and accuracy. The results were similar to those observed for Neotropical forests, suggesting a more general use of the Spectranomics approach for mapping canopy traits in tropical forests. Future mapping studies using this approach can advance scientific investigations and applications based on imaging spectroscopy. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic
Remote Sens. 2018, 10(2), 175; https://doi.org/10.3390/rs10020175
Received: 13 December 2017 / Revised: 12 January 2018 / Accepted: 19 January 2018 / Published: 26 January 2018
Cited by 2 | PDF Full-text (14254 KB) | HTML Full-text | XML Full-text
Abstract
Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts
[...] Read more.
Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database
Remote Sens. 2018, 10(2), 153; https://doi.org/10.3390/rs10020153
Received: 18 December 2017 / Revised: 15 January 2018 / Accepted: 19 January 2018 / Published: 23 January 2018
Cited by 1 | PDF Full-text (4081 KB) | HTML Full-text | XML Full-text
Abstract
The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable
[...] Read more.
The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland–Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model’s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5–4.9 g·kg−1; ratio of performance to deviation (RPD) = 1.4–1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Evaluation of Accuracy and Practical Applicability of Methods for Measuring Leaf Reflectance and Transmittance Spectra
Remote Sens. 2018, 10(1), 25; https://doi.org/10.3390/rs10010025
Received: 6 October 2017 / Revised: 13 December 2017 / Accepted: 21 December 2017 / Published: 24 December 2017
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Abstract
Leaf reflectance and transmittance spectra are urgently needed in interpretation of remote sensing data and modeling energy budgets of vegetation. The measurement methods should be fast to operate and preferably portable to enable quick collection of spectral databases and in situ measurements. At
[...] Read more.
Leaf reflectance and transmittance spectra are urgently needed in interpretation of remote sensing data and modeling energy budgets of vegetation. The measurement methods should be fast to operate and preferably portable to enable quick collection of spectral databases and in situ measurements. At the same time, the collected spectra must be comparable across measurement campaigns. We compared three different methods for acquiring leaf reflectance and transmittance spectra. These were a single integrating sphere (ASD RTS-3ZC), a small double integrating sphere (Ocean Optics SpectroClip-TR), and a leaf clip (PP Systems UNI501 Mini Leaf Clip). With all methods, an ASD FieldSpec 4 spectrometer was used to measure white paper and tree leaves. Single and double integrating spheres showed comparable within-method variability in the measurements. Variability with leaf clip was slightly higher. The systematic difference in mean reflectance spectra between single and double integrating spheres was only minor (average relative difference of 1%), whereas a large difference (14%) was observed in transmittance. Reflectance measured with leaf clip was on average 14% higher compared to single integrating sphere. The differences between methods influenced also spectral vegetation indices calculated from the spectra, particularly those that were designed to track small changes in spectra. Measurements with double integrating sphere were four, and with leaf clip six times as fast as with single integrating sphere, if slightly reduced signal level (integration time reduced from optimum) was allowed for the double integrating sphere. Thus, these methods are fast alternatives to a conventional single integrating sphere. However, because the differences between methods depended on the measured target and wavelength, care must be taken when comparing the leaf spectra acquired with different methods. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms
Remote Sens. 2017, 9(11), 1103; https://doi.org/10.3390/rs9111103
Received: 23 August 2017 / Revised: 3 October 2017 / Accepted: 24 October 2017 / Published: 29 October 2017
Cited by 1 | PDF Full-text (4352 KB) | HTML Full-text | XML Full-text
Abstract
We explored the potentials of both non-imaging laboratory and airborne imaging spectroscopy to assess arable soil quality indicators. We focused on microbial biomass-C (MBC) and hot water-extractable C (HWEC), complemented by organic carbon (OC) and nitrogen (N) as well-studied spectrally active parameters. The
[...] Read more.
We explored the potentials of both non-imaging laboratory and airborne imaging spectroscopy to assess arable soil quality indicators. We focused on microbial biomass-C (MBC) and hot water-extractable C (HWEC), complemented by organic carbon (OC) and nitrogen (N) as well-studied spectrally active parameters. The aggregation of different spectral variable selection strategies was used to analyze benefits for reachable estimation accuracies and to explore spectral predictive mechanisms for MBC and HWEC. With selected variables, quantification accuracies improved markedly for MBC (laboratory: RPD = 2.32 instead of 1.33 with full spectra; airborne: 2.35 instead of 1.80) and OC (laboratory: RPD = 3.08 instead of 2.36; airborne: 2.20 instead of 1.94). Patterns of selected variables indicated similarities between HWEC and OC, but significant differences between all other soil variables. This agreed to our results of indirect approaches in which both (i) wet-chemical data of OC and N and (ii) spectra fitted to measured OC and N values were used to estimate MBC and HWEC. Compared to these approaches, we found marked benefits of laboratory and airborne data for a direct spectral quantification of MBC (but not for HWEC). This suggests specificity of spectra for MBC, usable for the determination of this important soil parameter. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Spectral Similarity and PRI Variations for a Boreal Forest Stand Using Multi-angular Airborne Imagery
Remote Sens. 2017, 9(10), 1005; https://doi.org/10.3390/rs9101005
Received: 1 August 2017 / Revised: 14 September 2017 / Accepted: 22 September 2017 / Published: 29 September 2017
Cited by 2 | PDF Full-text (6016 KB) | HTML Full-text | XML Full-text
Abstract
The photochemical reflectance index (PRI) is a proxy for light use efficiency (LUE), and is used in remote sensing to measure plant stress and photosynthetic downregulation in plant canopies. It is known to depend on local light conditions within a canopy indicating non-photosynthetic
[...] Read more.
The photochemical reflectance index (PRI) is a proxy for light use efficiency (LUE), and is used in remote sensing to measure plant stress and photosynthetic downregulation in plant canopies. It is known to depend on local light conditions within a canopy indicating non-photosynthetic quenching of incident radiation. Additionally, when measured from a distance, canopy PRI depends on shadow fraction—the fraction of shaded foliage in the instantaneous field of view of the sensor—due to observation geometry. Our aim is to quantify the extent to which sunlit fraction alone can describe variations in PRI so that it would be possible to correct for its variation and identify other possible factors affecting the PRI–sunlit fraction relationship. We used a high spatial and spectral resolution Aisa Eagle airborne imaging spectrometer above a boreal Scots pine site in Finland (Hyytiälä forest research station, 61°50′N, 24°17′E), with the sensor looking in nadir and tilted (off-nadir) directions. The spectral resolution of the data was 4.6 nm, and the spatial resolution was 0.6 m. We compared the PRI for three different scatter angles ( β = 19 ° , 55 ° and 76 °, defined as the angle between sensor and solar directions) at the forest stand level, and observed a small (0.006) but statistically significant (p < 0.01) difference in stand PRI. We found that stand mean PRI was not a direct function of sunlit fraction. However, for each scatter angle separately, we found a clear non-linear relationship between PRI and sunlit fraction. The relationship was systematic and had a similar shape for all of the scatter angles. As the PRI–sunlit fraction curves for the different scatter angles were shifted with respect to each other, no universal curve could be found causing the observed independence of canopy PRI from the average sunlit fraction of each view direction. We found the shifts of the curves to be related to a leaf structural effect on canopy scattering: the ratio of needle spectral reflectance to transmittance. We demonstrate that modeling PRI–sunlit fraction relationships using high spatial resolution imaging spectroscopy data is suitable and needed in order to quantify PRI variations over forest canopies. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra
Remote Sens. 2017, 9(9), 927; https://doi.org/10.3390/rs9090927
Received: 24 July 2017 / Revised: 25 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
Cited by 2 | PDF Full-text (7774 KB) | HTML Full-text | XML Full-text
Abstract
Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral
[...] Read more.
Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside of these RTMs is that they are computationally expensive, which makes them impractical in routine processing, such as scene generation and retrieval applications. To bypass their computational burden, a computationally effective technique has been proposed by only using a limited number of model runs, called emulation. The idea of emulation is approximating the original RTM by a surrogate machine learning model with low computation time. However, a concern is whether the emulator reaches sufficient accuracy. To this end, we analyzed key aspects of emulator development that may impact the precision of emulating SCOPE-like R and SIF spectra, being: (1) type of machine learning, (2) type of dimensionality reduction (DR) method, and (3) number of components and lookup table (LUT) size. The machine learning family of Gaussian processes regression and neural networks were found best suited to function as emulators. The classical principal component analysis (PCA) remains a robust DR method, but the number of components needs to be optimized depending on the complexity of the spectral data. Based on a small Latin hypercube sampling LUT of 500 samples (70% used for training) covering a selection of SCOPE input variables, the best-performing emulators can reconstruct any combination for the selected SCOPE input variables with relative errors along the spectral range below 2% for R and 4% for SIF. That is sufficient for a precise reconstruction for the large majority of possible combinations, and errors can be further reduced when increasing LUT size for training. As a proof of concept, we imported the best-performing emulators into a newly developed Automated Scene Generator Module (A-SGM) to generate a R and SIF synthetic scene of a vegetated surface. Using emulators as alternative of SCOPE reduced the processing time from the order of days to the order of minutes while preserving sufficient accuracy. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessReview Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows
Remote Sens. 2018, 10(7), 1091; https://doi.org/10.3390/rs10071091
Received: 25 May 2018 / Revised: 18 June 2018 / Accepted: 30 June 2018 / Published: 9 July 2018
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Abstract
In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial
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In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the ‘journey’ of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessReview Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements
Remote Sens. 2018, 10(2), 207; https://doi.org/10.3390/rs10020207
Received: 2 December 2017 / Revised: 17 January 2018 / Accepted: 26 January 2018 / Published: 30 January 2018
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Abstract
Coniferous species are present in almost all major vegetation biomes on Earth, though they are the most abundant in the northern hemisphere, where they form the northern tree and forest lines close to the Arctic Circle. Monitoring coniferous forests with satellite and airborne
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Coniferous species are present in almost all major vegetation biomes on Earth, though they are the most abundant in the northern hemisphere, where they form the northern tree and forest lines close to the Arctic Circle. Monitoring coniferous forests with satellite and airborne remote sensing is active, due to the forests’ great ecological and economic importance. We review the current understanding of spectral behavior of different components forming coniferous forests. We look at the spatial, directional, and seasonal variations in needle, shoot, woody element, and understory spectra in coniferous forests, based on measurements. Through selected case studies, we also demonstrate how coniferous canopy spectra vary at different spatial scales, and in different viewing angles and seasons. Finally, we provide a synthesis of gaps in the current knowledge on spectra of elements forming coniferous forests that could also serve as a recommendation for planning scientific efforts in the future. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessReview Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study
Remote Sens. 2018, 10(1), 85; https://doi.org/10.3390/rs10010085
Received: 24 November 2017 / Revised: 20 December 2017 / Accepted: 8 January 2018 / Published: 10 January 2018
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Abstract
Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables
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Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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