Special Issue "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 "Biosphere/Hydrosphere/Land - Atmosphere Interactions".
Deadline for manuscript submissions: 31 July 2019
Prof. Dr. George G. Burba
I am glad to inform you that the following Special Issue is now open for submissions:
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
- 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.
- Important considerations for linking leaf and soil measurements to the ecosystem and agrosystem field-scale measurements.
- Important aspects of linking ecosystem and agrosystem field-scale measurements to regional and global scales.
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.
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
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. 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 1400 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.
- Leaf measurements
- Soil measurements
- Ecosystem fluxes
- Natural systems
- Agricultural systems
- Remote sensing
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: New Gap Filling Strategies for Long-Period Flux Data Gaps Using Data-Driven Approach
Authors: Minseok Kang 1,*, Kazuhito Ichii 2, Joon Kim 1,3,4, and Yohana M. Indrawati 4
Affiliations: 1 National Center for AgroMeteorology, Seoul, South Korea; 2 Center for Environmental Remote Sensing, Chiba University, Chiba, Japan; Email: [email protected]; 3 Department of Landscape Architecture and Rural Systems Engineering/ Institutes of Green Bio Science and Technology, Seoul National University, Seoul, South Korea; 4 Interdisciplinary Program in Agricultural & Forest Meteorology, Seoul National University, Seoul, South Korea
Abstract: In KoFlux, the Haenam Farmland (HFK) site has the longest record (from July 2002 to present) of carbon/water/energy flux measurement using eddy covariance technique. The HFK site is located in a typical Korean farmland which is characterized by mosaic patches of various agricultural lands including rice paddy, beans and sesame fields. The long-term database at the HFK is vital to better understand how the farmlands have adapted and been managed with natural and/or human disturbances at various time and spatial scales. Unfortunately, there are long gaps particularly in 2007 and 2014, which hinder the researchers from analyzing the decade-longtime series data. The general gap-filling method is impractical to apply for such long gaps. For example, the marginal distribution sampling method would perform poorly because marginally distributed data rarely exist around gaps. Recently, data-driven approach (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially using machine learning technique) is used to estimate terrestrial CO2/H2O fluxes. Such an approach can be applied to our case after appropriate modifications. In this presentation, we evaluate an applicability of data-driven approach to the filling of long gaps in flux data (i.e., gross primary production (GPP), ecosystem respiration (RE), net ecosystem exchange (NEE), and evapotranspiration (ET)). The data-driven approach using two machine learning techniques (i.e., support vector regression (SVR) and artificial neural network (ANN)) and remote sensing data reproduces the GPP and ET from EC measurement reasonably well. To propose new gap-filling strategies, we have tested various hypotheses. According to the preliminary results, we found that (1) estimation using in-situ measurement data for input to machine learning is more reasonable than that using remote sensing (or modeling) data; (2) closer (to gaps) training dataset for machine learning does not result in better estimation; and (3) longer training dataset for machine learning results in better estimation. We are currently testing another flux database at a natural temperate deciduous forest (i.e., the GDK site) and conducting residual analysis.
Keywords: eddy covariance; long-term database; gap-filling; long gap; data-driven approach