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Remote Sensing of the Terrestrial Carbon Cycle
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Remote Sensing of the Terrestrial Carbon Cycle

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (26 May 2024) | Viewed by 3873

Special Issue Editors

Prof. Dr. Alexander Olchev

E-Mail Website
Guest Editor
Department of Meteorology and Climatology, Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia
Interests: climate change; carbon cycle; greenhouse fluxes; mathematical modeling; remote sensing; field flux measurements
Special Issues, Collections and Topics in MDPI journals
Dr. Shamil Maksyutov

E-Mail Website
Guest Editor
Earth System Division, National Institute for Environmental Studies, Tsukuba 305-8506, Japan
Interests: carbon cycle; emission inventory; remote sensing; data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern climate change and human activity have a strong impact on terrestrial ecosystems that may lead to changes in ecosystem structure and functioning. Remote sensing is powerful to monitor the terrestrial ecosystems in space and time, analyze land cover and vegetation changes, estimate the surface gross and net primary production, assess greenhouse (GHG) fluxes, etc.

The aim of this Special Issue is to bring together the most recent achievements in the study of water and carbon budgets and GHG fluxes of terrestrial ecosystems in middle and tropical latitudes using remote sensing data. Over recent decades, a number of remote sensing and field studies to estimate the spatial and temporal variability of vegetation properties, to derive the carbon balance of the underlying surface and to retrieve the surface GHG fluxes were conducted. Despite this, a number of very important questions related to, e.g., determining the plant canopy properties and species composition, assessing the spatial and temporal variability of gross and net primary production, ecosystem respiration, quantifying accuracy of carbon budget and GHG flux estimations from remote sensing data, etc., remain open and require new multifaceted studies.

For this Special Issue, we invite scientists working in satellite and Unmanned Aerial Vehicle (UAV) remote sensing, atmospheric physics, forest ecology, mathematical modeling, meteorology, biogeochemistry, ecology to contribute new aggregated remote sensing and field studies of greenhouse fluxes and water and carbon budgets on different spatial scales (from the ecosystem to the global scale).

Contributions may include, but are not limited to, remote sensing studies of vegetation properties, canopy structure, surface carbon budgets and GHG fluxes of terrestrial ecosystems in different spatial scales; the effects of atmospheric hazards on terrestrial ecosystem structure and functioning using remote sensing and in situ data analysis; development of new methods (process-based models, machine learning techniques, etc.) to retrieve the main components of carbon budget and GHG fluxes in terrestrial ecosystems from remote sensing data; spatial forest and wetland assessment and mapping using remote sensing data and techniques; remote sensing and agriculture; etc.

Prof. Dr. Alexander Olchev
Dr. Shamil Maksyutov
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

  • satellite and UVA remote sensing
  • GHG flux
  • carbon cycle
  • terrestrial ecosystems
  • multispectral UAV imagery, lidars
  • field flux measurements

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 17793 KiB  
Article
An Inverse Modeling Approach for Retrieving High-Resolution Surface Fluxes of Greenhouse Gases from Measurements of Their Concentrations in the Atmospheric Boundary Layer
by Iuliia Mukhartova, Andrey Sogachev, Ravil Gibadullin, Vladislava Pridacha, Ibragim A. Kerimov and Alexander Olchev
Remote Sens. 2024, 16(13), 2502; https://doi.org/10.3390/rs16132502 - 8 Jul 2024
Cited by 1 | Viewed by 1577
Abstract
This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations [...] Read more.
This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km2 forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results. Full article
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Carbon Cycle)
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14 pages, 2717 KiB  
Article
The Effects of Precipitation Event Characteristics and Afforestation on the Greening in Arid Grasslands, China
by Xuan Guo, Qun Guo, Zhongmin Hu, Shenggong Li, Qingwen Min, Songlin Mu, Chengdong Xu and Linli Sun
Remote Sens. 2023, 15(18), 4621; https://doi.org/10.3390/rs15184621 - 20 Sep 2023
Cited by 1 | Viewed by 1686
Abstract
Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to [...] Read more.
Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future. Full article
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Carbon Cycle)
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