Special Issue "Remote Sensing of Carbon Fluxes and Stocks"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Bassil El Masri
Website
Guest Editor
Assistant Professor, Murray State University, 102 Curris Center, Murray, KY 42701, USA
Interests: Remote sensing of vegetation, ecosystem modeling, soil-vegetation interactions, phenology, carbon and nitrogen cycles
Dr. Jingfeng Xiao
Website
Guest Editor
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Interests: Carbon cycle; remote sensing; climate-vegetation interactions; upscaling; terrestrial biosphere modeling
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Special Issue Information

Dear Colleagues,

Much concern has been raised regarding the extent to which rapid climate change and human activities affect ecosystem function and services. Quantifying carbon fluxes and stocks is essential for helping us understand the responses of terrestrial ecosystems to climate change and anthropogenic activities. Remote sensing observations are valuable for estimating carbon fluxes and stocks of terrestrial ecosystems and for assessing the impacts of the changing climate and anthropogenic drivers on the terrestrial carbon cycle at various spatial and temporal scales.

This Special Issue aims at compiling the most recent research on quantifying, modeling, and monitoring the terrestrial carbon fluxes and stocks using remote sensing data and techniques at landscape, regional, or global scales.

Specifically, we invite the following contributions based on various remote sensing data (e.g., passive optical remote sensing, microwave remote sensing, lidar, solar-induced chlorophyll fluorescence) and techniques (e.g., synergy and integration of various remotely sensed data, model–data fusion):

1) Estimating carbon fluxes at a variety of spatiotemporal scales;

2) Estimating aboveground biomass at different spatial scales;

3) Quantifying errors and uncertainties of carbon flux and/or stock estimates;

4) Assessing interannual variability and long-term trends of carbon fluxes and/or stocks;

5) Examining the terrestrial carbon cycle integrating remotely sensed data and modeling approaches;

6) Understanding the carbon–climate feedbacks at regional to global scales.

Dr. Bassil El Masri
Dr. Jingfeng Xiao
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 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

  • Carbon fluxes 
  • Aboveground biomass 
  • Remote sensing 
  • Carbon cycle 
  • Uncertainty analysis
  • Carbon–climate feedbacks

Published Papers (4 papers)

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Research

Open AccessArticle
Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
Remote Sens. 2021, 13(4), 818; https://doi.org/10.3390/rs13040818 - 23 Feb 2021
Viewed by 206
Abstract
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide [...] Read more.
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks)
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Open AccessArticle
Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers
Remote Sens. 2021, 13(4), 794; https://doi.org/10.3390/rs13040794 - 21 Feb 2021
Viewed by 297
Abstract
Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key [...] Read more.
Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks)
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Open AccessArticle
Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region–Comparison with Data from MODIS
Remote Sens. 2021, 13(3), 469; https://doi.org/10.3390/rs13030469 - 29 Jan 2021
Viewed by 397
Abstract
The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to [...] Read more.
The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R2 = 0.84 for Sentinel-2; R2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks)
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Open AccessArticle
Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production
Remote Sens. 2020, 12(9), 1405; https://doi.org/10.3390/rs12091405 - 29 Apr 2020
Cited by 2 | Viewed by 1228
Abstract
Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain [...] Read more.
Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIRv) index. EVI and NIRv were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO2 uptake (Aleaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in Aleaf. EVI and NIRv showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with Aleaf, and EVI and NIRv consequently mimic GPP’s multiplicative sensitivity to LAI and Aleaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIRv’s sensitivity to both LAI and Aleaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks)
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