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Special Issue "Applications of Spectroscopy in Agriculture and Vegetation Research"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Dr. Jochem Verrelst

Senior Scientist, Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Website | E-Mail
Phone: (+34) 963544067
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX, vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
Guest Editor
Dr. Tobias Hank

Senior Scientist, Department of Geography, Faculty of Geosciences, LUDWIG-MAXIMILIANS-UNIVERSITÄT München, Munich 80333, Germany
Website | E-Mail
Phone: +49(0)89/2180-6682
Interests: precision agriculture; biophysical modelling; imaging spectroscopy; data assimilation
Guest Editor
Dr. Martin Schlerf

Senior Research Associate, Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, L-4362 Esch/Alzette, Luxembourg
Website | E-Mail
Phone: +352 275 888 5004
Interests: hyperspectral and thermal remote sensing; retrieval of biochemical and structural vegetation properties; water stress detection; crop nitrogen assessment
Guest Editor
Dr. Xia Yao

Senior Scientist, College of Agriculture, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, P. R. China
Website | E-Mail
Phone: 86-25-84396565
Interests: crop SIF; phenotyping on LiDAR and UAV platforms; quantification of crop properties; disease surveillance; hyperspectral remote sensing
Guest Editor
Dr. Karolina Sakowska

Biometeorology Research Group, Institute of Ecology, University of Innsbruck, 6020 Innsbruck, Austria
Website | E-Mail
Phone: +39 377 9999 168
Interests: remote sensing of vegetation properties; ecosystem–atmosphere exchange of carbon dioxide and carbonyl sulfide (COS); fluorescence and COS as tracers for canopy photosynthesis

Special Issue Information

Dear Colleagues,

Vegetated ecosystems are an important component of terrestrial (agro)ecosystems largely mediating gas and energy exchange at the atmosphere–biosphere–pedosphere interface. The precise spatial and temporal acquisition of information about vegetation status, health, and photosynthetic functioning is fundamental to model the dynamic response of vegetation to changing environmental conditions.

This information can probably be best obtained with imaging spectrometers, which provide a unique opportunity to collect spectrally and spatially continuous data on vegetation traits at ecologically relevant scales over wide areas. With the ongoing progress of implementing imaging spectrometers in UAVs, aircrafts, and spaceborne missions, imaging spectroscopy has become a mature technique for capturing and quantifying vegetation properties and dynamics.

Apart from the steady progress in imaging spectroscopy sensor technology, also the continuing increase of computational power and the increasing availability and democratization of spectroscopic data have nurtured the recent boost in imaging spectroscopy science, leading to a methodological expansion and a diversification in vegetation-related applications. With imaging spectroscopy science now reaching maturity, it becomes feasible to compile recent applications in a dedicated Special Issue.

This Special Issue aims to address fundamental and applied research relating the spectral properties of vegetation to agronomic and biophysical variables, genetic and phenotypic parameters, as well as diurnal and seasonal dynamics linked to light harvesting and photoprotection.

Among the ongoing initiatives in this field, the CA17134 (SENSECO) COST Action, which aims to bring together scientists working on spectroscopy in agriculture and vegetation research, is a recent and prominent one. SENSECO places emphasis on investigating the role of spatial and temporal scales, as well as on promoting the synergistic use of multiple sensors at multiple scales and streamlining data quality and traceability. These kinds of initiatives intend to unite scientists to unravel the links between plant physiology, agroecosystem functioning, and biogeochemical cycles and will strengthen the progress in terrestrial imaging spectroscopy missions dedicated to vegetation monitoring (e.g., FLEX, EnMAP).

To gather a consolidated overview about the different activities around spectroscopy applications in agriculture and vegetation research within the COST Action and in connection with dedicated campaigns and satellite mission preparation programs, we invite papers on the following non-exhaustive list of topics:

  • Progress made in spectroscopy studies: considering, e.g., reflectance and fluorescence data acquisition protocols and point clouds, state of the art and performance of instrumentation, retrieval methods, and modelling applications.
  • Quantification of agronomic biophysical and biochemical variables: proximal sensing, UAV platforms for precision farming applications such as phenotyping, assessment of crop damage, identification of diseases, pests, water and nutrient management, growth status (LAI, biomass), photosynthetic activity, grain quality, crop acreage, and productivity.
  • Multi-scale observations in the spatial and temporal domains to quantify and monitor leaf-to-ecosystem functioning and biogeochemical traits and cycles. Synergistic use of multiple sensors to bridge the scaling gap.
  • Methodological progress in retrieval methods in the domains of machine learning, physical model inversion, hybrid methods, and from experimental-towards-operational methods and coupling of models and imaging spectroscopy.
  • Quantification of dynamic vegetation functioning (stress, productivity) from hyperspectral reflectance, fluorescence, and point-data sources.

Dr. Jochem Verrelst
Dr. Tobias Hank
Dr. Martin Schlerf
Dr. Xia Yao
Dr. Karolina Sakowska
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 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 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

  • spectroscopy
  • reflectance
  • fluorescence
  • vegetation
  • agriculture
  • scaling
  • sensor synergy
  • biophysical variables
  • quality
  • productivity

Published Papers (3 papers)

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Research

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Open AccessArticle Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information
Remote Sens. 2019, 11(3), 366; https://doi.org/10.3390/rs11030366
Received: 26 December 2018 / Revised: 31 January 2019 / Accepted: 7 February 2019 / Published: 12 February 2019
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Abstract
The variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface [...] Read more.
The variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface water. However, remote sensors do not measure soil water content directly. Therefore, it is of great importance to establish a SM retrieval model. In this paper, the relationship between SM and diffuse reflectance was first derived using the absorption coefficient and scattering coefficient related to SM. Then, based on Kubelka–Munk (KM) theory, the SM retrieval model using reflectance information was further derived, which is a semi-empirical model with an unknown parameter obtained either from fitting or from experimental measurements. The validity and reliability of the model were confirmed with the validation set. The results showed that the root mean square errors of prediction (RMSEPs) of four soils were generally less than 0.017, while the coefficients of determination (R2s) of four soils were generally more than 0.85, and the ratios of the performance to deviation (RPDs) of four soils were greater than 2.5 (470–2400 nm). Therefore, the model has high prediction accuracy, and can be well applied to the prediction of water content in different sorts of soils. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance
Remote Sens. 2019, 11(2), 197; https://doi.org/10.3390/rs11020197
Received: 18 December 2018 / Revised: 12 January 2019 / Accepted: 17 January 2019 / Published: 20 January 2019
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Abstract
Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding [...] Read more.
Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Other

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Open AccessLetter Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model
Remote Sens. 2018, 10(12), 2063; https://doi.org/10.3390/rs10122063
Received: 19 September 2018 / Revised: 13 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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Abstract
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of [...] Read more.
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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