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Special Issue "Terrestrial Carbon Cycle"

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

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editors

Guest Editor
Dr. Alemu Gonsamo

Department of Geography and Planning, University of Toronto, 100 St. George Street, M5S 3G3, Toronto, ON, Canada
Website | E-Mail
Phone: +1 416 946 7715
Interests: Earth observation; global change ecology; phenology; remote sensing of vegetation; terrestrial carbon cycle
Guest Editor
Dr. Holly Croft

Department of Geography and Planning, University of Toronto, 100 St. George Street, M5S 3G3, Toronto, ON, Canada
Website | E-Mail
Interests: remote sensing of vegetation; terrestrial carbon cycle; photosynthetic trait mapping; phenology; ecophysiology
Guest Editor
Dr. Mirco Migliavacca

Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans Knoll Strasse 10, Jena, Germany
Website | E-Mail
Interests: carbon cycle; phenology; sun-induced fluorescence; carbon-water interactions
Guest Editor
Dr. Gregory Duveiller

Directorate D – Sustainable Resources - Bio-Economy Unit, Joint Research Centre, European Commission, Via Enrico Fermi 2749, TP261, 26A/010B, I-21027 Ispra (VA), Italy
Website | E-Mail
Interests: carbon cycle; terrestrial ecosystem response to climate; sun-induced fluorescence; land use dynamics in Earth system; phenology

Special Issue Information

Dear Colleagues,

The terrestrial carbon cycle is controlled not only by photosynthesis, but also by respiration, carbon allocation, disturbance and rates of carbon turnover. However, these processes remain difficult to measure and challenging to model. As terrestrial ecosystem carbon cycle models become increasingly sophisticated, the level of uncertainty has also increased, as more mechanisms have been incorporated into the models. Therefore, spatially explicit quantification of terrestrial carbon budget remains uncertain. In this issue, we welcome contributions that make use of legacy or modern remote sensing observations to improve the characterisation of terrestrial carbon cycle processes. We particularly welcome novel remote sensing techniques and applications, such as chlorophyll fluorescence, CO2 flux observations, and photosynthetic trait mapping and their integration into mechanistic models to better understand carbon cycle processes. Model-data integration and observational studies at leaf, plant, field, regional and global scales are also welcome. Potential topics for research and review articles that make use of remote sensing observations include, but are not limited to:

  • Photosynthesis
  • Respiration
  • Net ecosystem productivity
  • Photosynthetic trait mapping
  • Satellite observation of terrestrial CO2 flux
  • Terrestrial ecosystem carbon cycle
  • Terrestrial ecosystem and climate change
  • Dynamic global vegetation models
  • Leaf-to-canopy photosynthesis scaling
  • Model-data integration for carbon cycle modelling
  • Terrestrial remote sensing in Earth system models
  • Application of chlorophyll fluorescence for photosynthesis mapping
Dr. Alemu Gonsamo
Dr. Holly Croft
Dr. Mirco Migliavacca
Dr. Gregory Duveiller
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

  • Carbon cycle
  • Photosynthesis
  • Respiration
  • Net ecosystem productivity
  • Leaf-to-canopy photosynthesis scaling
  • Model-data integration for carbon cycle modelling
  • Terrestrial CO2 flux observation

Published Papers (6 papers)

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Research

Open AccessArticle
Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.
Remote Sens. 2019, 11(14), 1733; https://doi.org/10.3390/rs11141733
Received: 24 June 2019 / Revised: 18 July 2019 / Accepted: 21 July 2019 / Published: 23 July 2019
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Abstract
Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon [...] Read more.
Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO2 emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO2 emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle. Full article
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Open AccessArticle
Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands
Remote Sens. 2019, 11(11), 1328; https://doi.org/10.3390/rs11111328
Received: 12 April 2019 / Revised: 28 May 2019 / Accepted: 29 May 2019 / Published: 3 June 2019
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Abstract
Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a [...] Read more.
Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( ε / S I F yield ), the one-leaf light-use efficiency (SL-LUE) model ( ε max ), and the two-leaf LUE (TL-LUE) model ( ε msu and ε msh ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the ε / S I F yield and ε max were similarly robust and considerably more stable than ε msu and ε msh for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( C V ) of ε / S I F yield was relatively small (15.0%) and was obviously lower than ε max ( C V = 27.0%), ε msu ( C V = 43.2%), and ε msh ( C V = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types. Full article
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Open AccessArticle
Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity
Remote Sens. 2019, 11(11), 1303; https://doi.org/10.3390/rs11111303
Received: 21 April 2019 / Revised: 24 May 2019 / Accepted: 28 May 2019 / Published: 31 May 2019
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Abstract
Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The [...] Read more.
Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments. Full article
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Open AccessArticle
Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model
Remote Sens. 2019, 11(11), 1287; https://doi.org/10.3390/rs11111287
Received: 15 April 2019 / Revised: 27 May 2019 / Accepted: 28 May 2019 / Published: 30 May 2019
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Abstract
The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique [...] Read more.
The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique has made it possible to overcome this dilemma. In this study, we have incorporated remotely sensed phenology into the Biome-BGC model and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP). Specifically, we first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model. Then, based on the GPP derived from EC measurements, we combined the global sensitivity analysis method and the simulated annealing optimization algorithm to effectively calibrate the ecophysiological parameters of the Biome-BGC model. Finally, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015 based on the Biome-BGC model after a phenology module modification and parameter calibration. The results indicate that the improved Biome-BGC model effectively overcomes the limitations of the original Biome-BGC model and is able to reproduce the seasonal dynamics and magnitude of GPP in alpine grasslands. Meanwhile, the simulated results also reveal that the GPP of alpine grasslands on the TP has increased significantly from 1982 to 2015 and shows a large spatial heterogeneity, with a mean of 289.8 gC/m2/yr or 305.8 TgC/yr. Our study demonstrates that the incorporation of remotely sensed phenology into the Biome-BGC model and the use of EC measurements to calibrate model parameters can effectively overcome the limitations of its application in alpine grassland ecosystems, which is important for detecting trends in vegetation productivity. This approach could also be upscaled to regional and global scales. Full article
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Open AccessArticle
Underestimates of Grassland Gross Primary Production in MODIS Standard Products
Remote Sens. 2018, 10(11), 1771; https://doi.org/10.3390/rs10111771
Received: 26 September 2018 / Revised: 1 November 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
Cited by 2 | PDF Full-text (3900 KB) | HTML Full-text | XML Full-text
Abstract
As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are [...] Read more.
As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R2), and root mean square errors (RMSE). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R2 = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm−2 day−1) for the three grassland biomes. GPPMOD had varied performances with R2 values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (εmax) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in εmax, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes. Full article
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Open AccessArticle
Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data
Remote Sens. 2018, 10(9), 1346; https://doi.org/10.3390/rs10091346
Received: 14 June 2018 / Revised: 13 August 2018 / Accepted: 19 August 2018 / Published: 23 August 2018
Cited by 7 | PDF Full-text (9570 KB) | HTML Full-text | XML Full-text
Abstract
We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about [...] Read more.
We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates. Full article
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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Graphical abstract

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