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OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes

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

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 55009

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Special Issue Editors

Dr. Loris Vescovo

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Guest Editor

Research and Innovation Center, Fondazione E. Mach, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
Interests: remote sensing; proximal sensing, biogeochemical cycles; grassland ecology; plant traits

Dr. MaPi Cendrero-Mateo

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Guest Editor

Laboratory of Earth Observation, University of Valencia, C/ Catedrático Agustín Escardino, nº 9, 46980 Paterna, Valencia, Spain
Interests: chlorophyll fluorescence; point and imaging spectroscopy; plant phenotyping; remote sensing of plant photosynthesis

Dr. Yves Goulas

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Guest Editor

Laboratory of Dynamic Meteorology, Ecole polytechnique, Route de Saclay, F91128 Palaiseau, France
Interests: chlorophyll fluorescence; sun-induced fluorescence; laser-induced fluorescence; photosynthesis; remote sensing; radiative transfer

Dr. Helge Aasen

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Guest Editor

Crop Science Group, Institute of Agricultural Sciences, Department of Environmental Systems Science, Federal Institute of Technology Zürich (ETHZ), Universitätstrasse 2, 8092 Zürich, Switzerland
Interests: high-resolution (hyper-) spectral and optical sensing; UAVs; laser scanning; upscaling; field phenotyping; precision agriculture; plant traits

Dr. Alasdair MacArthur

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Guest Editor

Laboratory for Earth Observation, Department of Earth Physics and Thermodynamics, University of Valencia, Valencia, Spain
Interests: cal/val; spectro(radio)meter calibration and characterisation; proximal sensing; environmental remote sensing; optical Earth observation; near-ground EO platforms; field spectroscopy
Special Issues, Collections and Topics in MDPI journals

Dr. Javier Pacheco-Labrador

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Guest Editor

Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knöll-Str. 10, D-07745 Jena, Germany
Interests: spectroradiometry; instrumental characterization; BRDF; hyperspectral; radiative transfer models; biophysical variables of vegetation; functional traits of vegetation; sun induced fluorescence

Dr. Shari Van Wittenberghe

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Guest Editor

Laboratory of Earth Observation, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Interests: chlorophyll fluorescence;stress physiology; photoprotection; hyperspectral (point) spectroscopy

Special Issue Information

Dear Colleagues,

The ES1309 (OPTIMISE) Cost Action network brought together scientists working in different domains (spectral information systems; remote sensing with unmanned aerial vehicles; reflectance and fluorescence observations) to explore innovative optical tools for remote and proximal sensing of ecophysiological processes and biogeochemical cycles. The intention was to promote reflectance and fluorescence measurements of ecosystems for Earth system models, validate global satellite observations, investigate the use of innovative spectrometers and UAV platforms to make these measurements, and develop automated wireless communication systems with on-line spectral information storage, quality assurance and data product sharing portals.

This Special Issue is calling for papers reporting the science outputs of such innovative tools, using near-ground optical data of high spectral, temporal and spatial resolutions. From these data, scientists are better able to comprehend the links between plant physiology, ecosystem functioning and biogeochemical cycles, and can provide key validation datasets for space-based remote sensing missions (Sentinels, FLEX).

In this Special Issue, papers are welcome that address the following:

Progress made in remote sensing of sun-induced fluorescence (SIF) studies, considering, e.g., fluorescence and reflectance data acquisition protocols, state-of-the-art and performance of instrumentation, retrieval methods, and modelling applications
Best practice procedures for UAV spectral sampling
Research developments in UAV platforms and optical sensors, able to provide unprecedented opportunities for high spatial, spectral and multi-angular near-ground Earth observations
Multi-scale observations adopting empirical and modelling methods to monitor ecosystem functioning and biogeochemical cycles
Smart on-line platforms and spectral information systems to assign quality flags, process and analyze optical data along with biophysical and water/carbon flux state variables, to enable data to be shared with other scientific communities and networks.

Dr. Loris Vescovo
Dr. MaPi Cendrero-Mateo
Dr. Yves Goulas
Dr. Helge Aasen
Dr. Alasdair MacArthur
Dr. Javier Pacheco-Labrador
Dr. Shari Van Wittenberghe
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 submissions that pass pre-check are 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 semimonthly 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 2700 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

Remote Sensing
Proximal Sensing
Unmanned Aerial Vehicles
Reflectance
Fluorescence
Spectral Information Systems
Biogeochemical Cycles

Published Papers (8 papers)

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Research

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24 pages, 5071 KiB

Open AccessArticle

In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species

by Marlena Kycko, Bogdan Zagajewski, Samantha Lavender and Anca Dabija

Remote Sens. 2019, 11(11), 1296; https://doi.org/10.3390/rs11111296 - 30 May 2019

Cited by 11 | Viewed by 3782

Abstract

Vegetation, through its condition, reflects the properties of the environment. Heterogeneous alpine ecosystems play a critical role in global monitoring systems, but due to low accessibility, cloudy conditions, and short vegetation periods, standard monitoring methods cannot be applied comprehensively. Hyperspectral tools offer a variety of methods based on narrow-band data, but before extrapolation to an airborne or satellite scale, they must be verified using plant biometrical variables. This study aims to assess the condition of alpine sward dominant species (Agrostis rupestris, Festuca picta, and Luzula alpino-pilosa) of the UNESCO Man&Biosphere Tatra National Park (TPN) where the high mountain grasslands are strongly influenced by tourists. Data were analyzed for trampled, reference, and recultivated polygons. The field-obtained hyperspectral properties were verified using ground measured photosynthetically active radiation, chlorophyll content, fluorescence, and evapotranspiration. Statistically significant changes in terms of cellular structures, chlorophyll, and water content in the canopy were detected. Lower values for the remote sensing indices were observed for trampled plants (about 10–15%). Species in recultivated areas were characterized by a similar, or sometimes improved, spectral properties than the reference polygons; confirmed by fluorescence measurements (Fv/Fm). Overall, the fluorescence analysis and remote sensing tools confirmed the suitability of such methods for monitoring species in remote mountain areas, and the general condition of these grasslands was determined as good. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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28 pages, 9869 KiB

Open AccessArticle

Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing

by M. Pilar Cendrero-Mateo, Sebastian Wieneke, Alexander Damm, Luis Alonso, Francisco Pinto, Jose Moreno, Luis Guanter, Marco Celesti, Micol Rossini, Neus Sabater, Sergio Cogliati, Tommaso Julitta, Uwe Rascher, Yves Goulas, Helge Aasen, Javier Pacheco-Labrador and Alasdair Mac Arthur

Remote Sens. 2019, 11(8), 962; https://doi.org/10.3390/rs11080962 - 22 Apr 2019

Cited by 55 | Viewed by 8355

Abstract

The interest of the scientific community on the remote observation of sun-induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) ≤6% and <5% for F₆₈₇ and F₇₆₀, respectively. Furthermore, although the SFM was the least affected by an inaccurate definition of the absorption spectral window (RE = 5%) and/or interpolation strategy (RE = 15–30%), we observed a sensitivity of the SIF retrieval for the simulated training data underlying the SFM model implementation. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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30 pages, 7580 KiB

Open AccessArticle

Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers

by Javier Pacheco-Labrador, Andreas Hueni, Laura Mihai, Karolina Sakowska, Tommaso Julitta, Joel Kuusk, Dan Sporea, Luis Alonso, Andreas Burkart, M. Pilar Cendrero-Mateo, Helge Aasen, Yves Goulas and Alasdair Mac Arthur

Remote Sens. 2019, 11(8), 960; https://doi.org/10.3390/rs11080960 - 22 Apr 2019

Cited by 30 | Viewed by 5215

Abstract

Growing interest in the proximal sensing of sun-induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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15 pages, 5801 KiB

Open AccessArticle

Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

by Karolina Sakowska, Alasdair MacArthur, Damiano Gianelle, Michele Dalponte, Giorgio Alberti, Beniamino Gioli, Franco Miglietta, Andrea Pitacco, Franco Meggio, Francesco Fava, Tommaso Julitta, Micol Rossini, Duccio Rocchini and Loris Vescovo

Remote Sens. 2019, 11(6), 614; https://doi.org/10.3390/rs11060614 - 13 Mar 2019

Cited by 12 | Viewed by 4163

Abstract

The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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21 pages, 4511 KiB

Open AccessArticle

Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera

by Julia Kelly, Natascha Kljun, Per-Ola Olsson, Laura Mihai, Bengt Liljeblad, Per Weslien, Leif Klemedtsson and Lars Eklundh

Remote Sens. 2019, 11(5), 567; https://doi.org/10.3390/rs11050567 - 08 Mar 2019

Cited by 116 | Viewed by 11016

Abstract

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ±0.5 °C), the accuracy declined to ±5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera’s automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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33 pages, 10449 KiB

Open AccessArticle

Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem

by José Ramón Melendo-Vega, M. Pilar Martín, Javier Pacheco-Labrador, Rosario González-Cascón, Gerardo Moreno, Fernando Pérez, Mirco Migliavacca, Mariano García, Peter North and David Riaño

Remote Sens. 2018, 10(12), 2061; https://doi.org/10.3390/rs10122061 - 18 Dec 2018

Cited by 24 | Viewed by 6228

Abstract

The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations of proximal and remote sensing sensors: the ASD Fieldspec^® 3 spectroradiometer, the Airborne Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined concomitant observations of biophysical variables and RF to test the capability of the models to reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between 6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at remote sensing scales. RRMSE increased in periods when large fractions of standing dead material mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially in summer and at mid-high view angles. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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27 pages, 2923 KiB

Open AccessArticle

Potential of Photochemical Reflectance Index for Indicating Photochemistry and Light Use Efficiency in Leaves of European Beech and Norway Spruce Trees

by Daniel Kováč, Petra Veselovská, Karel Klem, Kristýna Večeřová, Alexander Ač, Josep Peñuelas and Otmar Urban

Remote Sens. 2018, 10(8), 1202; https://doi.org/10.3390/rs10081202 - 31 Jul 2018

Cited by 36 | Viewed by 6144

Abstract

Hyperspectral reflectance is becoming more frequently used for measuring the functions and productivity of ecosystems. The purpose of this study was to re-evaluate the potential of the photochemical reflectance index (PRI) for evaluating physiological status of plants. This is needed because the reasons for variation in PRI and its relationships to physiological traits remain poorly understood. We examined the relationships between PRI and photosynthetic parameters in evergreen Norway spruce and deciduous European beech grown in controlled conditions during several consecutive periods of 10–12 days between which the irradiance and air temperature were changed stepwise. These regime changes induced significant changes in foliar biochemistry and physiology. The responses of PRI corresponded particularly to alterations in the actual quantum yield of photosystem II photochemistry (Φ_PSII). Acclimation responses of both species led to loss of PRI sensitivity to light use efficiency (LUE). The procedure of measuring PRI at multiple irradiance-temperature conditions has been designed also for testing accuracy of ΔPRI in estimating LUE. A correction mechanism of subtracting daily measured PRI from early morning PRI has been performed to account for differences in photosynthetic pigments between irradiance-temperature regimes. Introducing ΔPRI, which provided a better estimate of non-photochemical quenching (NPQ) compared to PRI, also improved the accuracy of LUE estimation. Furthermore, ΔPRI was able to detect the effect of drought, which is poorly observable from PRI. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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Review

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33 pages, 3897 KiB

Open AccessReview

Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level

by Helge Aasen, Shari Van Wittenberghe, Neus Sabater Medina, Alexander Damm, Yves Goulas, Sebastian Wieneke, Andreas Hueni, Zbyněk Malenovský, Luis Alonso, Javier Pacheco-Labrador, M. Pilar Cendrero-Mateo, Enrico Tomelleri, Andreas Burkart, Sergio Cogliati, Uwe Rascher and Alasdair Mac Arthur

Remote Sens. 2019, 11(8), 927; https://doi.org/10.3390/rs11080927 - 16 Apr 2019

Cited by 54 | Viewed by 9159

Abstract

Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals. Full article

(This article belongs to the Special Issue OPTIMISE: Innovative Optical Tools for Proximal Sensing of Ecophysiological Processes)

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