Mapping CO2 fluxes of cypress swamp and marshes in the Greater Everglades using eddy covariance measurements and Landsat data

Zhang, Caiyun; Brodylo, David; Sirianni, Matthew J.; Li, Tiantian; Comas, Xavier; Douglas, Thomas A.; Starr, Gregory

doi:10.1016/j.rse.2021.112523

Cited by 14 publications

(10 citation statements)

References 39 publications

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“…Therefore, to more effectively control CH 4 emission from the CW–MFC, it is necessary to further study the interaction mechanism between microbial electrogenesis and plant physiological activities in the future. According to the literature, due to the temporal and spatial variability of GHG emissions from wetlands, the emission flux of CH 4 increases as the CO 2 flux increases, mainly because the increase in CO 2 concentration changes the concentration of oxygen in the root zone and the availability of carbon sources (Kao-Kniffin and Zhu 2013 ; Zhang et al 2021a ). Although wetlands are an important sink of exotic biogenic carbon (e.g., TOC in wastewater), biological activities (i.e., microbial and plant activities) and chemical phenomena are the key factors affecting the carbon dynamics in different phases.…”

Section: Resultsmentioning

confidence: 99%

Effects of bioelectricity generation processes on methane emission and bacterial community in wetland and carbon fate analysis

Liu

Xue

Wang

et al. 2022

Bioresour. Bioprocess.

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Wetlands are an important carbon sink for greenhouse gases (GHGs), and embedding microbial fuel cell (MFC) into constructed wetland (CW) has become a new technology to control methane (CH4) emission. Rhizosphere anode CW–MFC was constructed by selecting rhizome-type wetland plants with strong hypoxia tolerance, which could provide photosynthetic organics as alternative fuel. Compared with non-planted system, CH4 emission flux and power output from the planted CW–MFC increased by approximately 0.48 ± 0.02 mg/(m2·h) and 1.07 W/m3, respectively. The CH4 emission flux of the CW–MFC operated under open-circuit condition was approximately 0.46 ± 0.02 mg/(m2·h) higher than that under closed-circuit condition. The results indicated that plants contributed to the CH4 emission from the CW–MFC, especially under open-circuit mode conditions. The CH4 emission from the CW–MFC was proportional to external resistance, and it increased by 0.67 ± 0.01 mg/(m2·h) when the external resistance was adjusted from 100 to 1000 Ω. High throughput sequencing further showed that there was a competitive relationship between electrogenic bacteria and methanogens. The flora abundance of electrogenic bacteria was high, while methanogens mainly consisted of Methanothrix, Methanobacterium and Methanolinea. The form and content of element C were analysed from solid phase, liquid phase and gas phase. It was found that a large amount of carbon source (TC = 254.70 mg/L) was consumed mostly through microbial migration and conversion, and carbon storage and GHGs emission accounted for 60.38% and 35.80%, respectively. In conclusion, carbon transformation in the CW–MFC can be properly regulated via competition of microorganisms driven by environmental factors, which provides a new direction and idea for the control of CH4 emission from wetlands. Graphical Abstract

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Section: Resultsmentioning

confidence: 99%

Effects of bioelectricity generation processes on methane emission and bacterial community in wetland and carbon fate analysis

Liu

Xue

Wang

et al. 2022

Bioresour. Bioprocess.

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“…The atmospheric carbon uptake and emission phenomena assessment via remote sensing techniques can be based on either net ecosystem exchange (NEE) and CO 2 Flux [24] estimations. In the first case, eddy flux tower collateral information and different micrometeorological computational tools and techniques are required [46]. On the other hand, CO 2 Flux uses mainly remote sensing data.…”

Section: Discussionmentioning

confidence: 99%

CO2Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia

et al. 2022

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In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia.

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“…2) were extracted (including the algorithms used, modeling/validation methods, remote sensing data, meteorological data, biophysical data, and ancillary data). In some studies, multiple algorithms were applied to the same dataset or models with different features were developed (Virkkala et al, 2021;Zhang et al, 2021;Cleverly et al, 2020;Tramontana et al, 2016). In these cases, multiple data records will be documented.…”

Section: Features Of Prediction Modelsmentioning

confidence: 99%

“…In contrast, in models that include multiple PFTs, some variables that play a significant role in the prediction of each of the multiple PFTs may have higher importance. For example, growing degree days (GDDs) may be a more effective variable for NEE of tundra in the Northern Hemisphere high latitudes (Virkkala et al, 2021), while measured groundwater levels may be important for wetlands (Zhang et al, 2021). Some of these predictor variables are measured at flux stations (e.g., meteorological factors such as precipitation and temperature), while others are extracted from reanalyzed meteorological datasets and satellite remote sensing image data (e.g., vegetation indices).…”

Section: Introductionmentioning

confidence: 99%

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

et al. 2022

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Abstract. Net ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial ecosystems. Many previous studies have combined flux observations and meteorological, biophysical, and ancillary predictors using machine learning to simulate the site-scale NEE. However, systematic evaluation of the performance of such models is limited. Therefore, we performed a meta-analysis of these NEE simulations. A total of 40 such studies and 178 model records were included. The impacts of various features throughout the modeling process on the accuracy of the model were evaluated. Random forests and support vector machines performed better than other algorithms. Models with larger timescales have lower average R2 values, especially when the timescale exceeds the monthly scale. Half-hourly models (average R2 = 0.73) were significantly more accurate than daily models (average R2 = 0.5). There are significant differences in the predictors used and their impacts on model accuracy for different plant functional types (PFTs). Studies at continental and global scales (average R2 = 0.37) with multiple PFTs, more sites, and a large span of years correspond to lower R2 values than studies at local (average R2 = 0.69) and regional (average R2 = 0.7) scales. Also, the site-scale NEE predictions need more focus on the internal heterogeneity of the NEE dataset and the matching of the training set and validation set.

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Mapping CO2 fluxes of cypress swamp and marshes in the Greater Everglades using eddy covariance measurements and Landsat data

Cited by 14 publications

References 39 publications

Effects of bioelectricity generation processes on methane emission and bacterial community in wetland and carbon fate analysis

Effects of bioelectricity generation processes on methane emission and bacterial community in wetland and carbon fate analysis

CO2Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

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