Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across a Spectrum of Function, Structure, Physiological Capacity, and Weather

We test the relationship between canopy photosynthesis and reflected near-infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near-infrared and red radiation from vegetation, with light-emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near-infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near-infrared radiation were highly correlated with measurements of canopy photosynthesis on half-hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near-infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient-rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient-poor, mineral soils. Reflected near-infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near-infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

AmeriFlux FLUXNET-1F US-Tw5 East Pond Wetland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw5 East Pond Wetland. This is the FLUXNET version of the carbon flux data for the site US-Tw5 East Pond Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell Wetland site is a 6.5 acre restored wetland on Twitchell Island, that is managed by the California Department of Water Resources (DWR) and the U.S. Geological Survey (USGS). In the fall of 1997, the site was permanently flooded to a depth of approximately 55 cm. The wetland remained fairly unvegetated in patches increasing in size towards the east. The site underwent a major disturbance in 2013 when the vegetation was removed to seed a nearby restored wetland. A flux tower equipped to analyze energy, H2O, CO2, and CH4 fluxes was installed on April 17, 2018

High methane emissions as trade-off for phosphorus removal in surface flow treatment wetlands

Constructed wetlands (CW) treating runoff from agricultural catchments reduce the nutrient load of water, however, they can also be significant sources of greenhouse gases, especially methane (CH4). We simultaneously assessed CH4 emission potentials and phosphorus (P) removal efficiency in a 0.45 ha in-stream surface flow CW to determine the main drivers of CH4 emissions, and to analyze the temporal dynamics of CH4 emissions and P removal during an almost 4-year period. The TP (total phosphorus) removal efficiency had a clear seasonal dynamic, with the highest removal occurring during summer and early autumn (monthly average 60.5%), when the flow rate was lowest and water residence time longest. Due to increasing sedimentation and related anaerobic conditions, the mean hourly CH4 emissions for each year demonstrated an increasing trend over the years: from 88 µg CH4-C m−2 h−1 in 2018–2505 µg CH4-C m−2 h−1 in 2021. There was a clear seasonality in CH4 emissions: up to 90% of CH4 fluxes occurred during the warm period (from May to October). We assume that maintenance of treatment wetlands is essential and predominantly regular removal of aboveground vegetation at the second half of the growing season would decrease CH4 emissions. Nevertheless, due to the P saturation in sediments, regular sediment removal in the long term is also necessary

Carbon Flux Trajectories and Site Conditions from Restored Impounded Marshes in the Sacramento‐San Joaquin Delta

Wetlands can sequester carbon over the long term, providing natural climate solutions. This requires that existing carbon stocks be maintained and additional greenhouse gas (GHG) emissions are outweighed by carbon sequestration. We analyzed 25 site-years of continuous CO 2 and methane fluxes by eddy covariance from 4 restored wetlands to detect long-term trends and estimate their GHG budgets. Sites showed large interannual variability with no clear trends in CO 2 fluxes beyond the initial uptake phase during vegetation establishment following restoration. Methane emissions followed either decreasing or increasing trends likely because of variable site conditions, such as water level fluctuations and soil mineral concentrations. Several site-years were GHG neutral or sinks depending on the global warming potential used. Carbon sink sites were projected to offset the radiative forcing from methane emissions after 62 to 202 years. We compared and contrasted restoration design and management strategies based on this and previous studies at these sites to balance the climate mitigation potentials with other beneficial wetland services. We conclude that these restored wetlands indeed provide climate mitigation benefits provided they are appropriately maintained over the long term (100 years), as poor management can cause large carbon losses even decades after restoration

Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function

Seasonal phenological dynamics of vegetation hold important clues on ecosystem performance towards management goals, such as carbon uptake, and thus should be considered in projections of their targeted services. However, in wetlands spatio-temporal heterogeneity due to mixing of open water, soil, green and dead vegetation makes it difficult to generalize ecosystem functioning across different regions. Remote sensing observations can provide spatially-explicit, cost-effective phenology indicators; however, little is known about their capacity to indicate the links between wetland ecosystem structure and function. Here we assessed this potential by comparing one-year Enhanced Vegetation Index (EVI) from satellite products at high (5m; RapidEye) and low (30m; Landsat) spatial resolutions with eddy covariance time series of net carbon exchange, field digital camera (phenocam) greenness and water temperature among three floristically similar restored wetlands in California, USA. Phenological timing differed by wetland site: depending on satellite, the range in site-median start of greening was up to 28 days, end of greening – up to 73 days, start of senescence – up to 79 days, and end of senescence – up to 10 days. Key transition dates from satellite inputs agreed with seasonal changes in net carbon exchange, phenocam greenness and water temperatures, suggesting that phenological contrasts could result in part from site differences in vegetation configuration and litter affecting the exposure of canopy, soil and water to sunlight and thus sub-canopy microclimate and ecosystem functioning. Yet, the agreement between satellite inputs was non-systematic, with the greatest disparities at the more heterogeneous, less vegetated site. Phenological model fitting uncertainty increased with greater spatial resolution, highlighting the tradeoff between the accuracy of representing vegetation and the complexity of local seasonal variation. These findings highlight the sensitivity of satellite-derived phenology to structural and functional heterogeneity of ecosystems and call for more rigorous spatially-explicit analyses to inform assessments of restoration and management outcomes