Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use

An approach to estimate atmospheric greenhouse gas total columns mole fraction from partial column sampling

© 2018 by the authors. This study presents a new conceptual approach to estimate total column mole fractions of CO2 and CH4 using partial column data. It provides a link between airborne in situ and remote sensing observations of greenhouse gases. The method relies on in situ observations, external ancillary sources of information (e.g., atmospheric transport models), and a regression kriging framework. We evaluate our new approach using National Oceanic and Atmospheric Administration's (NOAA's) AirCore program-in situ vertical profiles of CO2 and CH4 collected from weather balloons. Our paper shows that under the specific conditions of this study and assumption of unbiasedness, airborne observations up to 6500-9500maltitude are required to achieve comparable total column CO2 mole fraction uncertainty as the Total Carbon Column Observing Network (TCCON) network provides, given as a precision of the ratio between observed and true total column-integrated mole fraction, assuming 400 ppm XCO2 (2σ, e.g., 0.8 ppm). If properly calibrated, our approach could be applied to vertical profiles of CO2 collected from aircraft using a few flask samples, while retaining similar uncertainty level. Our total column CH4 estimates, by contrast, are less accurate than TCCON's. Aircrafts are not as spatially constrained as TCCON ground stations, so our approach adds value to aircraft-based vertical profiles for evaluating remote sensing platforms

An approach to estimate atmospheric greenhouse gas total columns mole fraction from partial column sampling

This study presents a new conceptual approach to estimate total column mole fractions of CO2 and CH4 using partial column data. It provides a link between airborne in situ and remote sensing observations of greenhouse gases. The method relies on in situ observations, external ancillary sources of information (e.g., atmospheric transport models), and a regression kriging framework. We evaluate our new approach using National Oceanic and Atmospheric Administration's (NOAA's) AirCore program-in situ vertical profiles of CO2 and CH4 collected from weather balloons. Our paper shows that under the specific conditions of this study and assumption of unbiasedness, airborne observations up to 6500-9500maltitude are required to achieve comparable total column CO2 mole fraction uncertainty as the Total Carbon Column Observing Network (TCCON) network provides, given as a precision of the ratio between observed and true total column-integrated mole fraction, assuming 400 ppm XCO2 (2σ, e.g., 0.8 ppm). If properly calibrated, our approach could be applied to vertical profiles of CO2 collected from aircraft using a few flask samples, while retaining similar uncertainty level. Our total column CH4 estimates, by contrast, are less accurate than TCCON's. Aircrafts are not as spatially constrained as TCCON ground stations, so our approach adds value to aircraft-based vertical profiles for evaluating remote sensing platforms

Towards hyper-dimensional variography using the product-sum covariance model

Modeling hyper-dimensional spatial variability is a complex task from both practical and theoretical standpoints. In this paper we develop a method for modeling hyper-dimensional covariance (variogram) structures using the product-sum covariance model initially developed to model spatio-temporal variability. We show that the product-sum model can be used recursively up to an arbitrarily large number of dimensions while preserving relative modeling simplicity and yielding valid covariance models. The method can be used to model variability in anisotropic conditions with multiple axes of anisotropy or when temporal evolution is involved, and thus is applicable to "full anisotropic 3D+time" situations often encountered in environmental sciences. It requires fewer assumptions than the traditional product-sum modeling approach. The new method also presents an alternative to classical approaches to modeling zonal anisotropy and requires fewer parameters to be estimated from data. We present an example by applying the method in conjunction with ordinary kriging to map photosynthetically-active radiation (PAR) for 2006, in Oklahoma, CA, USA and to explore effects of spatio-temporal variability in PAR on gross primary productivity (GPP)

Separating the effects of phenology and diffuse radiation on gross primary productivity in winter wheat

Gross primary productivity (GPP) has been reported to increase with the fraction of diffuse solar radiation, for a given total irradiance. The correlation between GPP and diffuse radiation suggests effects of diffuse radiation on canopy light-use efficiency, but potentially confounding effects of vegetation phenology have not been fully explored. We applied several approaches to control for phenology, using 8 years of eddy-covariance measurements of winter wheat in the U.S. Southern Great Plains. The apparent enhancement of daily GPP due to diffuse radiation was reduced from 260% to 75%, after subsampling over the peak growing season or by subtracting a 15 day moving average of GPP, suggesting a role of phenology. The diffuse radiation effect was further reduced to 22% after normalizing GPP by a spectral reflectance index to account for phenological variations in leaf area index LAI and canopy photosynthetic capacity. Canopy photosynthetic capacity covaries with diffuse fraction at a given solar irradiance at this site because both factors are dependent on day of year or solar zenith angle. Using a two-leaf Sun-shaded canopy radiative transfer model, we confirmed that the effects of phenological variations in photosynthetic capacity can appear qualitatively similar to the effects of diffuse radiation on GPP and therefore can be difficult to distinguish using observations. The importance of controlling for phenology when inferring diffuse radiation effects on GPP raises new challenges and opportunities for using radiation measurements to improve carbon cycle models

A large increase in U.S. methane emissions over the past decade inferred from satellite data and surface observations

The global burden of atmospheric methane has been increasing over the past decade, but the causes are not well understood. National inventory estimates from the U.S. Environmental Protection Agency indicate no significant trend in U.S. anthropogenic methane emissions from 2002 to present. Here we use satellite retrievals and surface observations of atmospheric methane to suggest that U.S. methane emissions have increased by more than 30% over the 2002-2014 period. The trend is largest in the central part of the country, but we cannot readily attribute it to any specific source type. This large increase in U.S. methane emissions could account for 30-60% of the global growth of atmospheric methane seen in the past decade

Carbon flux phenology from the sky: Evaluation for maize and soybean

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. Presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a cropspecific measurement tool

The influence of land cover on surface energy partitioning and evaporative fraction regimes in the U.S. Southern Great Plains

Land-atmosphere interactions are important to climate prediction, but the underlying effects of surface forcing of the atmosphere are not well understood. In the U.S. Southern Great Plains, grassland/pasture and winter wheat are the dominant land covers but have distinct growing periods that may differently influence land-atmosphere coupling during spring and summer. Variables that influence surface flux partitioning can change seasonally, depending on the state of local vegetation. Here we use surface observations from multiple sites in the U.S. Department of Energy Atmospheric Radiation Measurement Southern Great Plains Climate Research Facility and statistical modeling at a paired grassland/agricultural site within this facility to quantify land cover influence on surface energy balance and variables controlling evaporative fraction (latent heat flux normalized by the sum of sensible and latent heat fluxes). We demonstrate that the radiative balance and evaporative fraction are closely related to green leaf area at both winter wheat and grassland/pasture sites and that the early summer harvest of winter wheat abruptly shifts the relationship between evaporative fraction and surface state variables. Prior to harvest, evaporative fraction of winter wheat is strongly influenced by leaf area and soil-atmosphere temperature differences. After harvest, variations in soil moisture have a stronger effect on evaporative fraction. This is in contrast with grassland/pasture sites, where variation in green leaf area has a large influence on evaporative fraction throughout spring and summer, and changes in soil-atmosphere temperature difference and soil moisture are of relatively minor importance