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Remote Sensing of Carbon Fluxes and Stocks II

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

Deadline for manuscript submissions: 18 December 2024 | Viewed by 14748

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Special Issue Editor

Dr. Bassil El Masri

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Guest Editor

Earth and Environmental Sciences, 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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Much concern has been raised regarding the extent to which rapid climate change and human activities affect ecosystem functions 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 the 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.

The previous Special Issue on “Remote Sensing of Carbon Fluxes and Stocks” was a success. The second volume solicits papers dealing with the compilation of the most recent research on quantifying, modeling, and monitoring 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):

Estimating carbon fluxes at a variety of spatiotemporal scales;
Estimating aboveground biomass at different spatial scales;
Quantifying errors and uncertainties of carbon flux and/or stock estimates;
Assessing interannual variability and long-term trends of carbon fluxes and/or stocks;
Examining the terrestrial carbon cycle integrating remotely sensed data and modeling approaches;
Understanding carbon–climate feedbacks at regional to global scales.

Dr. Bassil El Masri
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. 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

carbon fluxes
aboveground biomass
remote sensing
carbon cycle
uncertainty analysis
carbon–climate feedbacks

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Published Papers (6 papers)

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Research

31 pages, 9784 KiB

Open AccessArticle

Spatiotemporal Variability of Gross Primary Productivity in Türkiye: A Multi-Source and Multi-Method Assessment

by Eyyup Ensar Başakın, Paul C. Stoy, Mehmet Cüneyd Demirel and Quoc Bao Pham

Remote Sens. 2024, 16(11), 1994; https://doi.org/10.3390/rs16111994 - 31 May 2024

Viewed by 779

Abstract

We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Türkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal–Wallis and Mann–Whitney U methods, and long-term trends were analyzed using Modified Mann–Kendall (MMK), innovative trend analysis (ITA), and empirical mode decomposition (EMD). Our results show that at least one GPP product significantly differs from the others over the seven geographic regions of Türkiye (χ² values of 50.8, 21.9, 76.9, 42.6, 149, 34.5, and 168; p < 0.05), and trend analyses reveal a significant increase in GPP from all satellite-based products over the latter half of the study period. Throughout the year, the average number of months in which each dataset showed significant increases across all study regions are 6.7, 8.1, 5.9, 9.6, and 8.7 for MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2, respectively. The ITA and EMD methods provided additional insight into the MMK test in both visualizing and detecting trends due to their graphical techniques. Overall, the GPP products investigated here suggest ‘greening’ for Türkiye, consistent with the findings from global studies, but the use of different statistical approaches and satellite-based GPP estimates creates different interpretations of how these trends have emerged. Ground stations, such as eddy covariance towers, can help further improve our understanding of the carbon cycle across the diverse ecosystem of Türkiye. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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17 pages, 9275 KiB

Open AccessEditor’s ChoiceArticle

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

by Nicolas Francos, Paolo Nasta, Carolina Allocca, Benedetto Sica, Caterina Mazzitelli, Ugo Lazzaro, Guido D’Urso, Oscar Rosario Belfiore, Mariano Crimaldi, Fabrizio Sarghini, Eyal Ben-Dor and Nunzio Romano

Remote Sens. 2024, 16(5), 897; https://doi.org/10.3390/rs16050897 - 3 Mar 2024

Cited by 2 | Viewed by 3125

Abstract

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R² = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R² = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R² = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R² = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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32 pages, 11786 KiB

Open AccessArticle

Evaluation of Simulated CO₂ Point Source Plumes from High-Resolution Atmospheric Transport Model

by Chao Li, Xianhua Wang, Hanhan Ye, Shichao Wu, Hailiang Shi, Haiyan Luo, Zhiwei Li, Wei Xiong, Dacheng Li, Erchang Sun and Yuan An

Remote Sens. 2023, 15(18), 4518; https://doi.org/10.3390/rs15184518 - 14 Sep 2023

Cited by 1 | Viewed by 1639

Abstract

Coal-fired power plants, as major anthropogenic CO₂ emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO₂ emissions from these point sources is crucial, as it will aid in quantifying CO₂ emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO₂ diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO₂ simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO₂ diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO₂ diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R² (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO₂ point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO₂ point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO₂ concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO₂ emissions using top-down approaches. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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24 pages, 9843 KiB

Open AccessArticle

Identifying Spatial Variation of Carbon Stock in a Warm Temperate Forest in Central Japan Using Sentinel-2 and Digital Elevation Model Data

by Huiqing Pei, Toshiaki Owari, Satoshi Tsuyuki and Takuya Hiroshima

Remote Sens. 2023, 15(8), 1997; https://doi.org/10.3390/rs15081997 - 10 Apr 2023

Cited by 4 | Viewed by 3318

Abstract

The accurate estimation of carbon stocks in natural and plantation forests is a prerequisite for the realization of carbon peaking and neutrality. In this study, the potential of optical Sentinel-2A data and a digital elevation model (DEM) to estimate the spatial variation of carbon stocks was investigated in a mountainous warm temperate region in central Japan. Four types of image preprocessing techniques and datasets were used: spectral reflectance, DEM-based topography indices, vegetation indices, and spectral band-based textures. A random forest model combined with 103 field plots as well as remote sensing image parameters was applied to predict and map the 2160 ha University of Tokyo Chiba Forest. Structural equation modeling was used to evaluate the factors driving the spatial distribution of forest carbon stocks. Our study shows that the Sentinel-2A data in combination with topography indices, vegetation indices, and shortwave-infrared (SWIR)-band-based textures resulted in the highest estimation accuracy. The spatial distribution of carbon stocks was successfully mapped, and stand-age- and forest-type-level variations were identified. The SWIR-2-band and topography indices were the most important variables for modeling, while the forest stand age and curvature were the most important determinants of the spatial distribution of carbon stock density. These findings will contribute to more accurate mapping of carbon stocks and improved quantification in different forest types and stand ages. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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16 pages, 3507 KiB

Open AccessArticle

Development of Hybrid Models to Estimate Gross Primary Productivity at a Near-Natural Peatland Using Sentinel 2 Data and a Light Use Efficiency Model

by Ruchita Ingle, Saheba Bhatnagar, Bidisha Ghosh, Laurence Gill, Shane Regan, John Connolly and Matthew Saunders

Remote Sens. 2023, 15(6), 1673; https://doi.org/10.3390/rs15061673 - 20 Mar 2023

Cited by 5 | Viewed by 2459

Abstract

Peatlands store up to 2320 Mt of carbon (C) on only ~20% of the land area in Ireland; however, approximately 90% of this area has been drained and is emitting up to 10 Mt C per year. Gross primary productivity (GPP) is a one of the key components of the peatland carbon cycle, and detailed knowledge of the spatial and temporal extent of GPP under changing management practices is imperative to improve our predictions of peatland ecology and biogeochemistry. This research assesses the relationship between remote sensing and ground-based estimates of GPP for a near-natural peatland in Ireland using eddy covariance (EC) techniques and high-resolution Sen-tinel 2A satellite imagery. Hybrid models were developed using multiple linear regression along with six widely used conventional indices and a light use efficiency model. Estimates of GPP using NDVI, EVI, and NDWI2 hybrid models performed well using literature-based light use efficiency parameters and showed a significant correlation from 89 to 96% with EC-derived GPP. This study also reports additional site-specific light use efficiency parameters for dry and hydrologically normal years on the basis of light response curve methods (LRC). Overall, this research has demonstrated the potential of combining EC techniques with satellite-derived models to better understand and monitor key drivers and patterns of GPP for raised bog ecosystems under different climate scenarios and has also provided light use efficiency parameters values for dry and wetter conditions that can be used for the estimation of GPP using LUE models across various site and scales. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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19 pages, 4711 KiB

Open AccessArticle

Modeling Soil CO₂ Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models

by Xarapat Ablat, Chong Huang, Guoping Tang, Nurmemet Erkin and Rukeya Sawut

Remote Sens. 2023, 15(5), 1415; https://doi.org/10.3390/rs15051415 - 2 Mar 2023

Cited by 2 | Viewed by 2440

Abstract

Monitoring tropical and subtropical forest soil CO₂ emission efflux (FSCO₂) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO₂ monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS LST fusion images and a linear mixed effect model to estimate FSCO₂ at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R² = 0.47) of FSCO₂ variability in the Monthly random effect model, while it only accounted for 19% of FSCO₂ variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R² = 0.55 and t value > 1.9) for FSCO₂ variability and yielded the smallest deviation from observed FSCO₂. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO₂ monitoring in tropical and subtropical forests. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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