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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13606

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Dr. Victoria Meyer

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Guest Editor

Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
Interests: remote sensing; tropical forest; biomass; Lidar; deforestation; degradation; carbon cycle; water cycle

Special Issue Information

Dear Colleagues,

The land and ocean carbon sinks play a key role in the carbon cycle, regulating atmospheric CO₂ concentrations. However, their associated uncertainties remain large, leading to large uncertainties in climate feedbacks and therefore in atmospheric CO₂ concentrations predictions.

Remote sensing is an incomparable source of information to better quantify how much carbon is stored in the world’s forests and oceans, and exchanged with the atmosphere. It is also a powerful tool to observe carbon dynamics through time and space. There is a growing number of remote sensing missions focusing on our world’s forests to try and reduce the uncertainties around the amount of carbon that forests store or lose through deforestation and degradation, and more methods are being developed to look at ocean CO₂. Airborne and spaceborne instruments looking at atmospheric CO₂ concentrations are also contributing to the field in a major way. The common goal of all these missions is to improve carbon estimations and dynamics, reduce their uncertainties, and understand the interactions between the different components of carbon cycle science.

This Special Issue aims to bring together studies that focus on different aspects of the carbon cycle, encompassing the remote sensing of terrestrial CO₂ (forests, land use/land cover change), ocean CO₂, and atmospheric CO₂. We also seek papers that describe new or future instruments and data that will advance the field of remote sensing of the carbon cycle. Papers using either airborne or spaceborne remote sensing data are welcome. Studies focusing on the interactions between the carbon cycle and the water cycle are also encouraged.

Dr. Victoria Meyer
Guest Editor

Manuscript Submission Information

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Keywords

carbon cycle
forest biomass
ocean CO₂
atmospheric CO₂
Earth remote sensing

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Published Papers (3 papers)

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Research

19 pages, 4643 KiB

Open AccessArticle

Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data

by Dekker Ehlers, Chao Wang, John Coulston, Yulong Zhang, Tamlin Pavelsky, Elizabeth Frankenberg, Curtis Woodcock and Conghe Song

Remote Sens. 2022, 14(5), 1115; https://doi.org/10.3390/rs14051115 - 24 Feb 2022

Cited by 28 | Viewed by 5362

Abstract

The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R² of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Cycle Science)

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19 pages, 6842 KiB

Open AccessArticle

Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau

by Xuqiang Zhou, Xufeng Wang, Songlin Zhang, Yang Zhang and Xuejie Bai

Remote Sens. 2020, 12(22), 3735; https://doi.org/10.3390/rs12223735 - 13 Nov 2020

Cited by 6 | Viewed by 2367

Abstract

Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R² of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m⁻²·day⁻¹ on average, and MAD decreased by 0.87 g C·m⁻²·day⁻¹ on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Cycle Science)

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13 pages, 1369 KiB

Open AccessCommunication

A New Remote Sensing Method to Estimate River to Ocean DOC Flux in Peatland Dominated Sarawak Coastal Regions, Borneo

by Sim ChunHock, Nagur Cherukuru, Aazani Mujahid, Patrick Martin, Nivedita Sanwlani, Thorsten Warneke, Tim Rixen, Justus Notholt and Moritz Müller

Remote Sens. 2020, 12(20), 3380; https://doi.org/10.3390/rs12203380 - 16 Oct 2020

Cited by 9 | Viewed by 5042

Abstract

We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three steps. In the first step, we developed an algorithm for estimating DOC [...] Read more.

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), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Cycle Science)

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