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Leaf to Ecosystem: The Latest in Measuring Bio-Atmospheric Integrations at...
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Leaf to Ecosystem: The Latest in Measuring Bio-Atmospheric Integrations at Multiple Scales

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biometeorology".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 8470

Special Issue Editor

Prof. Dr. George G. Burba

E-Mail Website
Guest Editor
LI-COR Biosciences, Robert B. Daugherty Water for Food Institute, and the School of Natural Resources at the University of Nebraska, Lincoln, USA
Interests: atmosphere–biosphere interactions; trace gas and energy exchange, and related environmental processes and parameters

Special Issue Information

Dear Colleagues,

I am glad to inform you that the following Special Issue is now open for submissions:

Full Title:

From Single Leaf to Ecosystem: The Latest Scientific, Methodological and Technical Developments Connecting Bio-Atmospheric Measurements Scales from Leaf and Soil, to Field and Region

Key topics:

  1. The latest scientific, methodological and technical developments connecting ecosystem measurement scales, from leaf and soil, to the field, and to region: in situ, remote sensing, and modelling.
  2. Important considerations for linking leaf and soil measurements to the ecosystem and agrosystem field-scale measurements.
  3. Important aspects of linking ecosystem and agrosystem field-scale measurements to regional and global scales.

Details:

Measurements of greenhouse gas (GHG) exchange, related processes, and indicators are essential for understanding the drivers of global climate change, the short- and long-term consequences of the ecosystem and agrosystem management, and related changes on multiple scales.

Such information is important for two reasons. It contributes to the identification and prediction of physical and physiological processes underlying ongoing and future environmental changes that affect the health and resilience of ecosystems, and the sustainability and productivity of agrosystems. Furthermore, it helps influence important decisions on their mitigation, such as local and global policies.

Although the measurements of GHG fluxes and key related processes and indicators are conducted on a variety of scales, from a single leaf to a large region, many research projects focus on one single scale, while actual physical and physiological processes are happening over a continuum of multiple scales.

One of the major challenges associated with measuring and modelling over such a continuum is the transferability between measurement scales ranging from leaf and soil chambers, greenhouse-based imaging, field towers and UAVs, to aircraft and satellites.

This Special Issue seeks the latest developments that help bridge research efforts and measurement techniques at all scales into more vertically integrated approaches.

We are striving for 10–15 papers for this issue, with a limit of about 20 papers on a first submitted/first accepted basis. We envision several types of publications, including full original research papers, overview papers, and technical notes. All accepted manuscripts will be citable peer-reviewed articles.

Contributions:

The Special Issue will be largely based on the manuscripts originated from six closely-related events, listed below, but will also consider and welcome any other relevant manuscripts.

  • Potsdam GHG Flux Workshop: “From Natural to Urban Systems”, Helmholtz-Zentrum Potsdam, German Research Centre for Geosciences (GFZ), Potsdam, Germany, October 19–23, 2015
  • Potsdam GHG Flux Workshop: “From Photosystems to Ecosystems”, Helmholtz-Zentrum Potsdam, German Research Centre for Geosciences (GFZ), Potsdam, Germany, October 24–26, 2017
  • 3 Sessions on “Advanced Plant Phenotyping for Global Food Security: Lessons Across Measurement Scales from Leaf, to Field, to Remote Sensing I, II, III” at American Geophysical Union Fall Meeting, New Orleans, Louisiana, December 11–15, 2017
  • Potsdam GHG Flux Workshop - Nanjing: “From Leaf, Soil and Canopy to Remote Sensing and Modelling”, Nanjing University of Information Science and Technology (former Nanjing Institute of Meteorology), Nanjing, China, October 22–25, 2018

Prof. Dr. George G. Burba
Guest Editor

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. Atmosphere 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 2400 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

  • Leaf measurements
  • Soil measurements
  • Ecosystem fluxes
  • Scalability
  • Natural systems
  • Agricultural systems
  • Micrometeorology
  • Bio-optics
  • Phenotyping
  • Remote sensing
  • Modelling

Published Papers (2 papers)

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Research

18 pages, 5505 KiB  
Article
New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
by Minseok Kang, Kazuhito Ichii, Joon Kim, Yohana M. Indrawati, Juhan Park, Minkyu Moon, Jong-Hwan Lim and Jung-Hwa Chun
Atmosphere 2019, 10(10), 568; https://doi.org/10.3390/atmos10100568 - 22 Sep 2019
Cited by 21 | Viewed by 4906
Abstract
In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to [...] Read more.
In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach. Full article
(This article belongs to the Special Issue Leaf to Ecosystem: The Latest in Measuring Bio-Atmospheric Integrations at Multiple Scales)
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11 pages, 705 KiB  
Communication
Towards Hyper-Dimensional Variography Using the Product-Sum Covariance Model
by Jovan M. Tadić, Ian N. Williams, Vojin M. Tadić and Sébastien C. Biraud
Atmosphere 2019, 10(3), 148; https://doi.org/10.3390/atmos10030148 - 18 Mar 2019
Cited by 3 | Viewed by 2609
Abstract
Modeling hyper-dimensional spatial variability is a complex task from both practical and theoretical standpoints. In this paper we develop a method for modeling hyper-dimensional covariance (variogram) structures using the product-sum covariance model initially developed to model spatio-temporal variability. We show that the product-sum [...] Read more.
Modeling hyper-dimensional spatial variability is a complex task from both practical and theoretical standpoints. In this paper we develop a method for modeling hyper-dimensional covariance (variogram) structures using the product-sum covariance model initially developed to model spatio-temporal variability. We show that the product-sum model can be used recursively up to an arbitrarily large number of dimensions while preserving relative modeling simplicity and yielding valid covariance models. The method can be used to model variability in anisotropic conditions with multiple axes of anisotropy or when temporal evolution is involved, and thus is applicable to “full anisotropic 3D+time” situations often encountered in environmental sciences. It requires fewer assumptions than the traditional product-sum modeling approach. The new method also presents an alternative to classical approaches to modeling zonal anisotropy and requires fewer parameters to be estimated from data. We present an example by applying the method in conjunction with ordinary kriging to map photosynthetically-active radiation (PAR) for 2006, in Oklahoma, CA, USA and to explore effects of spatio-temporal variability in PAR on gross primary productivity (GPP). Full article
(This article belongs to the Special Issue Leaf to Ecosystem: The Latest in Measuring Bio-Atmospheric Integrations at Multiple Scales)
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