Eddy Covariance Footprint Research Articles

Matching Satellite Sun-Induced Chlorophyll Fluorescence to Flux Footprints Improves Its Relationship with Gross Primary Productivity

Sun-induced chlorophyll fluorescence (SIF) holds enormous potential for accurately estimating terrestrial gross primary productivity (GPP). However, current studies often overlook the spatial representativeness of satellite SIF and GPP observations. This research downscaled TROPOMI SIF (TROPOSIF) and its enhanced product (eSIF) in China’s Saihanba Forest Region to obtain high-resolution SIF data. SIF was simulated using the SCOPE model, and the downscaled SIF’s reliability was validated at two forest eddy covariance (EC) sites (SHB1 and SHB2) in the study area. Subsequently, the downscaled SIF data were matched to the EC footprint of the two forest sites, and the relationship between SIF and GPP was compared at various observational scales. Additionally, the ability of downscaled TROPOSIF and eSIF to track GPP was compared, along with the correlations among several vegetation indices (VIs) and GPP. The findings reveal the following: (1) Downscaled TROPOSIF and eSIF showed a strong linear relationship with SCOPE-modeled SIF (R2 ≥ 0.86). The eSIF closely matched the SCOPE simulation (RMSE: 0.06 mw m−2 nm−1 sr−1) and displayed a more consistent seasonal variation pattern with GPP. (2) Comparisons among coarse-resolution SIF, EC footprint-averaged SIF (SIFECA), and EC footprint-weighted SIF (SIFECW) demonstrated significant improvements in the linear relationship between downscaled SIF and GPP (the R2 increased from the range of 0.47–0.65 to 0.78–0.85). SIFECW exhibited the strongest relationship with GPP, indicating that matching SIF to flux footprints improves their relationship. (3) As the distance from the flux tower increased, the relationship between SIF and GPP weakened, reaching its lowest point beyond 1 km from the tower. Moreover, in the highly heterogeneous landscape of the SHB2 site, the relationship between VIs and GPP was poor, with no clear pattern as distance from the flux tower increased. In conclusion, the strong spatial dependency of SIF and tower-based GPP emphasizes the importance of using high-resolution SIF to accurately quantify their relationship.

Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data

Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m − 2s − 1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

Differences in CO2 Emissions on a Bare-Drained Peat Area in Sarawak, Malaysia, Based on Different Measurement Techniques

The drainage and cultivation of peatlands will lead to subsidence and mineralisation of organic matter, increasing carbon (C) loss as more CO2 is emitted. There is little information about carbon emissions from bare peat soil. A study was undertaken to measure the CO2 emissions from a logged-over peat swamp area that was purposely vegetation-free. We aimed to report CO2 emissions from a bare, drained peatland developed for an oil palm plantation. For 12 months, we used eddy covariance (EC), closed chambers, and soil subsidence measurements to derive CO2 emissions from a logged-over peat swamp area. Significant variations in the estimated soil CO2 efflux were observed in the three tested measurement techniques. The average CO2 flux rate measured by the EC technique was 4.94 ± 0.12 µmol CO2 m−2 s−1 (or 68.55 tonnes CO2 ha−1 year−1). Meanwhile, the soil CO2 efflux rate measured by the closed chamber technique was 4.19 ± 0.22 µmol CO2 m−2 s−1 (or 58.14 tonnes CO2 ha−1 year−1). Subsidence amounted to 1.9 cm year−1, corresponding to 36.12 tonnes CO2 ha−1 year−1. The estimation of the C loss was found to be highest by the EC technique, lower by the soil chamber technique, and lowest by the peat subsidence rate technique. The higher CO2 emission rate observed in the EC technique could be attributed to soil microbial respiration and decomposing woody residues in the nearby stacking rows due to the large EC footprint. It could also be affected by CO2 advection from oil palms adjacent to the study site. Despite the large differences in the CO2 emission rates by the different techniques, this study provides valuable information on the soil heterotrophic respiration of deep peat in Sarawak. Carbon emissions from a bare peat area cover only a fraction of the soil CO2 respiration component, i.e., the soil heterotrophic respiration. Further investigations are needed to determine the CO2 emissions by soil microbial activities and plant roots from other peat areas in Sarawak.

How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production

Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks.

Evaluation of Eddy Covariance Footprint Models Through the Artificial Line Source Emission of Methane

AbstractThis paper evaluates the performance of footprint models through a line source (LS) system releasing methane (CH4) measured by an eddy covariance (EC) system in a rice‐wheat rotation agro‐ecosystem. Two footprint models namely Kormann and Meixner (KM) and Flux Footprint Prediction (FFP), are used. The “line source width (LW)” was introduced, which could highly impact the model estimation, leading to an error of ∼500% for the FFP and ∼200% for the KM. The surface roughness length (z0) also affects the FFP estimation, which is much weaker than the effect of LW with a 15% error. An overestimation of 217.8% is observed when the LS is close to the EC system, which reduces to 12.2% when the source stays distant. The analysis of different experimental configurations illustrated that the estimated emission calculated based on footprint model improves with increasing emission height, averaging period, and emission rate. The most accurate estimations are achieved by the configurations of canopy level (CL; 5.5% overestimation), 15 min averaging period (1% underestimation), and moderate emission (ME; 1.9% underestimation), respectively. For KM, the CL‐ME‐30 min setup leads to the best performance, while SL (soil level)‐ME‐15 min is the best configuration for the FFP estimation. A multivariate regression model is proposed to provide preliminary guidance for the footprint model application. The designed LS system is valuable for validating the performance of flux measurements in the specific ecosystems (e.g., grassland, sand land, and forest) as well as other greenhouse gases (e.g., N2O and NH3).

Carbon balance of a Finnish bog: temporal variability and limiting factors based on 6 years of eddy-covariance data

Abstract. Pristine boreal mires are known as substantial sinks of carbon dioxide (CO2) and net emitters of methane (CH4). Bogs constitute a major fraction of pristine boreal mires. However, the bog CO2 and CH4 balances are poorly known, having been largely estimated based on discrete and short-term measurements by manual chambers and seldom using the eddy-covariance (EC) technique. Eddy-covariance (EC) measurements of CO2 and CH4 exchange were conducted in the Siikaneva mire complex in southern Finland in 2011–2016. The site is a patterned bog having a moss–sedge–shrub vegetation typical of southern Eurasian taiga, with several ponds near the EC tower. The study presents a complete series of CO2 and CH4 EC flux (FCH4) measurements and identifies the environmental factors controlling the ecosystem–atmosphere CO2 and CH4 exchange. A 6-year average growing season (May–September) cumulative CO2 exchange of −61 ± 24 g C m−2 was observed, which partitions into mean total respiration (Re) of 167 ± 33 (interannual range 146–197) g C m−2 and mean gross primary production (GPP) of 228 ± 46 (interannual range 193–257) g C m−2, while the corresponding FCH4 amounts to 7.1 ± 0.7 (interannual range 6.4–8.4) g C m−2. The contribution of October–December CO2 and CH4 fluxes to the cumulative sums was not negligible based on the measurements during one winter. GPP, Re and FCH4 increased with temperature. GPP and FCH4 did not show any significant decline even after a substantial water table drawdown in 2011. Instead, GPP, Re and FCH4 were limited in the cool, cloudy and wet growing season of 2012. May–September cumulative net ecosystem exchange (NEE) of 2013–2016 averaged at about −73 g C m−2, in contrast with the hot and dry year 2011 and the wet and cool year 2012. Suboptimal weather likely reduced the net sink by about 25 g C m−2 in 2011 due to elevated Re, and by about 40 g C m−2 in 2012 due to limited GPP. The cumulative growing season sums of GPP and CH4 emission showed a strong positive relationship. The EC source area was found to be comprised of eight distinct surface types. However, footprint analyses revealed that contributions of different surface types varied only within 10 %–20 % with respect to wind direction and stability conditions. Consequently, no clear link between CO2 and CH4 fluxes and the EC footprint composition was found despite the apparent variation in fluxes with wind direction.

Comparison of benthic oxygen exchange measured by aquatic Eddy Covariance and Benthic Chambers in two contrasting coastal biotopes (Bay of Brest, France)

To the best of our knowledge, the understanding of benthic metabolism of coastal sedimentary areas is still limited due to the complexity of determining their true in situ dynamics over large spatial and temporal scales. Multidisciplinary methodological approaches are then necessary to increase our comprehension of factors controlling benthic processes and fluxes. An aquatic Eddy Covariance (EC) system and Benthic Chambers (BC) were simultaneously deployed during the winter of 2013 in the Bay of Brest within a Maerl bed and a bare mudflat to quantify and compare O2 exchange at the sediment–water interface. Environmental abiotic parameters (i.e., light, temperature, salinity, current velocity and water depth) were additionally monitored to better understand the mechanisms driving benthic O2 exchange. At both sites, EC measurements showed short-term variations (i.e. 15 min) in benthic O2 fluxes according to environmental conditions. At the Maerl station, EC fluxes ranged from -21.0 mmol m−2 d−1 to 71.3 mmol m−2 d−1 and averaged 22.0 ± 32.7 mmol m−2 d−1 (mean ± SD), whilst at the bare muddy station, EC fluxes ranged from -43.1 mmol m−2 d−1 to 12.1 mmol m−2 d−1 and averaged -15.9 ± 14.0 mmol m−2 d−1 (mean ± SD) during the total deployment. Eddy Covariance and Benthic Chambers measurements showed similar patterns of temporal O2 flux changes at both sites. However, at the Maerl station, BC may have underestimated community respiration. This may be due to the relative large size of the EC footprint (compared to BC), which takes into account the mesoscale spatial heterogeneity (e.g. may have included contributions from bare sediment patches). Also, we hypothesize that the influence of bioturbation induced by large-sized mobile benthic fauna on sediment oxygen consumption was not fully captured by BC compared to EC. Overall, the results of the present study highlight the importance of taking into account specific methodology limitations with respect to sediment spatial macro-heterogeneity and short-term variations of environmental parameters to accurately assess benthic O2 exchange in the various benthic ecosystems of the coastal zone.

To What Extend Does the Eddy Covariance Footprint Cutoff Influence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards?

Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (ustar), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service's Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values.

Integrating eddy fluxes and remote sensing products in a rotational grazing native tallgrass prairie pasture

Eddy covariance (EC) systems provide integrated fluxes within their footprint areas. Spatial heterogeneity of up-scaled areas and spatio-temporal mismatches between EC footprint and remote sensing pixels jeopardize the performance of most satellite-based models. To examine the impact of spatial resolution of satellite products on up-scaling of fluxes, we compared the relationships between measured eddy fluxes and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 and 250 m spatial resolutions, Visible Infrared Imaging Radiometer Suite (VIIRS) at 500 m spatial resolution, and Landsat at 30 m spatial resolution but integrated at the paddock-scale. The experiment was conducted over a grazed native tallgrass prairie pasture, which was divided into nine paddocks for rotational grazing. The EVI data from all satellites showed consistency in detecting vegetation phenology. Seasonality of EC-measured fluxes corresponded well with remotely-sensed vegetation phenology. Approximately 80% of contribution to eddy fluxes came from within 80 m upwind distance of the 2.7 m tall EC tower. As a result, the major contributing area for the measured fluxes was mostly limited to the paddock containing the EC tower. Different timings and duration of grazing caused some heterogeneity among paddocks within the pasture. The EVI of different spatial scales showed strong relationships with CO2 fluxes. However, Landsat-derived EVI integrated for the paddock containing the EC tower showed substantially stronger relationships with CO2 fluxes than did MODIS and VIIRS-derived EVI integrated for multiple paddocks, most likely due to similar spatial resolutions of remote sensing and EC observations. Results illustrate that satellite products of fine-scale spatial resolution that are comparable to EC footprints can help improve the performance of satellite-based models for modeling or up-scaling of eddy fluxes, especially in heterogeneous ecosystems.

Fixed and variable components of evapotranspiration in a Mediterranean wild-olive - grass landscape mosaic

Dry regions are typically characterized by heterogeneous ecosystems where trees are competing with the surrounding grasses for limited amount of water. In these regions, evapotranspiration (ET) is the leading loss term in the soil water budget, and its estimate, and the dynamic contribution of each ET component (i.e. tree and grass transpiration, and dry bare soil and wet surface evaporation), are still poorly quantified. In a typical heterogeneous Mediterranean ecosystem in Sardinia, we combined eddy-covariance estimates of ET with sap flux and energy balance estimates of wild-olive tree transpiration, a common tree species of the region, and with modeled evapotranspiration from the seasonal grass. Trees located in the southern edge of clumps, thus receiving more radiation, transpired more and showed a greater sensitivity to increasing vapor pressure deficit and soil moisture than trees located in clump centers or northern edges. Transpiration of the tree clumps in the footprint (Et), summed up with the modeled evapotranspiration components of the surrounding grass (mostly transpiration during the wet season and evaporation during the dry season), matched latent heat flux measurements, lending confidence in the estimates. Proper accounting for heterogeneity of sources within the eddy covariance footprint seems to have overcome potential errors from not preserving an important assumption of the method, the land-surface homogeneity, highlighting the methods reliability in such inhomogeneous ecosystem. Compared to ET, Et of wild olives was nearly constant over the hydrologic year, insensitive to variation in soil moisture and atmospheric conditions. In contrast, under favorable spring environmental conditions (radiation, vapor pressure deficit, and soil moisture), the pasture leaf area transpires at high rates, contributing to, and dominating the high ET during that season. Conversely, in dry periods, when evapotranspiration from the grass cover is dominated by low evaporation from the, principally, bare soil, Et dominants ecosystem ET.

High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System

High spatial resolution maps of land surface energy, water and CO2 fluxes, e.g. evapotranspiration (ET) and gross primary productivity (GPP), are important for agricultural monitoring, ecosystem and water resources management. However, it is not clear which is the optimal (e.g. coarsest possible) spatial resolution to capture those fluxes accurately. Unmanned Aerial Systems (UAS) can address this by collecting very high spatial resolution (<1 m, VHR) imagery. The objective of this study is to model ET and GPP dynamics using VHR optical and thermal imagery and quantify the influence of the spatial heterogeneity in the flux simulations and validations. The study was conducted at a deciduous willow bioenergy eddy covariance (EC) flux site in Denmark. Flight campaigns were conducted during the growing seasons of 2016 and 2017 with a hexacopter equipped with RGB, multispectral and thermal infrared cameras. A ‘top-down’ modeling approach consisting of the Priestley–Taylor Jet Propulsion Laboratory model and a light use efficiency model sharing the same canopy biophysical constraints was used to estimate ET and GPP. Model outputs were benchmarked by EC flux observations with the source weighted footprint. Our results indicate that our model can well estimate the instantaneous net radiation, ET, GPP, evaporative fraction, light use efficiency and water use efficiency with root-mean-square deviations (RMSD) of 31.6 W·m−2, 41.2 W·m−2, 3.12 μmol·C·m−2·s−1, 0.08, 0.16 g·C·MJ−1 and 0.35 g·C·kg−1, respectively. Further, it is found that using a footprint model to sample different areas of VHR imagery can be a tool to provide better diurnal estimates to benchmark with EC data. Moreover, these VHR maps (0.3 m) allowed us to quantify metrics of spatial heterogeneity by using semivariogram analysis and by aggregating model inputs into different spatial resolutions. For instance, we find that in this site, the aggregation of simulated GPP using the source weighted mean of the EC footprint was about 30% lower in RMSD than using the arithmetic mean of the footprint. This demonstrates the accuracy of the modeled VHR spatial patterns. Nevertheless, we also find that imagery resolution consistent with the canopy size (around 1.5 m in our study) is sufficient to capture the spatial heterogeneity of the fluxes as transpiration and canopy assimilation of CO2 are processes regulated at the tree crown level. Our results highlight the importance of considering the land surface heterogeneity for flux modeling and the source contribution within the EC footprint for model benchmarking at appropriate spatial resolutions.

Grazing-related nitrous oxide emissions: from patch scale to field scale

Abstract. Grazed pastures are strong sources of the greenhouse gas nitrous oxide (N2O). The quantification of N2O emissions is challenging due to the strong spatial and temporal variabilities of the emission sources and so N2O emission estimates are very uncertain. This study presents N2O emission measurements from two grazing systems in western Switzerland over the grazing season of 2016. The 12 dairy cows of each herd were kept in an intensive rotational grazing management. The diet for the two herds of cows consisted of different protein-to-energy ratios (system G: grass only diet; system M: grass with additional maize silage) resulting in different nitrogen (N) excretion rates. The N in the excretion was estimated by calculating the animal nitrogen budget taking into account the measurements of feed intake, milk yield, and body weight of the cow herds. Directly after the rotational grazing phases, background and urine patches were identified based on soil electric conductivity measurements while fresh dung patches were identified visually. The magnitude and temporal pattern of these different emission sources were measured with a fast-box (FB) chamber and the field-scale fluxes were quantified using two eddy covariance (EC) systems. The FB measurements were finally upscaled to the field level and compared to the EC measurements for quality control by using EC footprint estimates of a backward Lagrangian stochastic dispersion model. The comparison between the two grazing systems was performed during emission periods that were not influenced by fertilizer applications. This allowed the calculation of the excreta-related N2O emissions per cow and grazing hour and resulted in considerably higher emissions for system G compared to system M. Relating the found emissions to the excreta N resulted in excreta-related emission factors (EFs) of 0.74±0.26 % for system M and 0.83±0.29 % for system G. These EF values were thus significantly smaller compared to the default EF of 2 % provided by the IPCC guidelines for cattle excreta deposited on pasture. The measurements showed that urine patch emission dominated the field-scale fluxes (57 %), followed by significant background emissions (38 %), and only a small contribution of dung patch emission (5 %). The resulting source-specific EFs exhibited a clear difference between urine (1.12±0.43 %) and dung (0.16±0.06 %), supporting a disaggregation of the grazing-related EFs by excreta type in emission inventories. The study also highlights the advantage of a N-optimized diet, which resulted in reduced N2O emissions from animal excreta.

CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions.

Improving the energy balance closure over a winter wheat field by accounting for minor storage terms

Turbulent fluxes at the land surface measured by the Eddy Covariance (EC) technique are typically considerably less than the difference between net radiation and ground heat flux. This is known as the energy balance closure (EBC) problem. It is crucial for validating land surface models as it provokes substantial uncertainty to the magnitude and partitioning of energy fluxes. The gap in the energy balance calls for searching for additional energy terms in the soil-plant-atmosphere system. To evaluate the contribution of these minor storage terms to the measured EBC, we conducted an experimental study to evaluate the contribution of these minor storage terms to measured EBC in the Kraichgau region in southwest Germany over two consecutive growing seasons (2015 and 2016). The measured and calculated minor storage terms comprised the enthalpy change in the plant canopy (Sc), the air enthalpy change (Sa), the energy consumption and release by photosynthesis and respiration (Sp), and the atmospheric moisture change (Sq). Furthermore, the soil heat storage (Sg) was determined at different locations within the EC footprint and compared to the single point measurements of Sg at the EC station. Calorimetric and harmonic analysis were performed to compute ground heat flux. Sp had the strongest effect in improving EBC due to the high net CO2 uptake during the productive phase of plant growth. In 2015, all minor storage terms together increased EBC by 5.0% on average, with a maximum value of 7.4% in May, while the improvement in 2016 was 6.8% on average and 8.4% in May. Ground heat flux computed with the harmonic analysis based on plate data narrowed the EBC by 3% more than the calorimetric method. In summary, a better EBC can be achieved by considering minor storage terms and applying a harmonic analysis to ground heat flux data. Regarding future research, we recommend to focus on year-round measurements of energy terms because energy stored during the growing season might be lost from the system during the rest of the year. Nonetheless, the significant contribution of minor energy terms to EBC indicates that turbulent energy fluxes are most likely overestimated when all the missing energy is assumed to be turbulent–the typical approach when fluxes are corrected by the Bowen ratio post-closure method for instance.

Improving ecosystem‐scale modeling of evapotranspiration using ecological mechanisms that account for compensatory responses following disturbance

AbstractMountain pine beetle outbreaks in western North America have led to extensive forest mortality, justifiably generating interest in improving our understanding of how this type of ecological disturbance affects hydrological cycles. While observational studies and simulations have been used to elucidate the effects of mountain beetle mortality on hydrological fluxes, an ecologically mechanistic model of forest evapotranspiration (ET) evaluated against field data has yet to be developed. In this work, we use the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to incorporate the ecohydrological impacts of mountain pine beetle disturbance on ET for a lodgepole pine‐dominated forest equipped with an eddy covariance tower. An existing degree‐day model was incorporated that predicted the life cycle of mountain pine beetles, along with an empirically derived submodel that allowed sap flux to decline as a function of temperature‐dependent blue stain fungal growth. The eddy covariance footprint was divided into multiple cohorts for multiple growing seasons, including representations of recently attacked trees and the compensatory effects of regenerating understory, using two different spatial scaling methods. Our results showed that using a multiple cohort approach matched eddy covariance‐measured ecosystem‐scale ET fluxes well, and showed improved performance compared to model simulations assuming a binary framework of only areas of live and dead overstory. Cumulative growing season ecosystem‐scale ET fluxes were 8 – 29% greater using the multicohort approach during years in which beetle attacks occurred, highlighting the importance of including compensatory ecological mechanism in ET models.

Spatio-Temporal Relationships between Optical Information and Carbon Fluxes in a Mediterranean Tree-Grass Ecosystem

Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPPhh) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems.

Net CO2 emissions from a primary boreo-nemoral forest over a 10 year period

Primary forests play an important role in the global carbon balance. With little to no human intervention, primary forests are shaped and characterised by disturbances such as weather extremes, fire, insect and pathogen attacks. Such disturbances have a direct impact on the volume of coarse woody debris (CWD) which contributes to the total ecosystem respiration (Re). There are currently few studies that present continuous long term measurements of the carbon balance of northern primary forests. We used the eddy covariance method to measure continuous carbon dioxide (CO2) fluxes from a Swedish primary boreo-nemoral forest over a ten year period. By mapping the measured CO2 fluxes to the forest ecosystem we could indicate that small areas that had some form of disturbance and areas with significant levels of CWD within the eddy covariance footprint contributed to the total Re resulting in the forest being a net carbon source. A weighing algorithm was used to account for directional ecosystem heterogeneity and to estimate a representative carbon balance for the ecosystem. The forest ecosystem was a continuous source of CO2 to the atmosphere, losing around 25Mg per hectare of CO2 over a ten year period.

Methane fluxes from a small boreal lake measured with the eddy covariance method

AbstractFluxes of methane, CH4, were measured with the eddy covariance (EC) method at a small boreal lake in Sweden. The mean CH4 flux during the growing season of 2013 was 20.1 nmol m−2 s−1 and the median flux was 16 nmol m−2 s−1 (corresponding to 1.7 mmol m−2 d−1 and 1.4 mmol m−2 d−1). Monthly mean values of CH4 flux measured with the EC method were compared with fluxes measured with floating chambers (FC) and were in average 62% higher over the whole study period. The difference was greatest in April partly because EC, but not FC, accounted for fluxes due to ice melt and a subsequent lake mixing event. A footprint analysis revealed that the EC footprint included primarily the shallow side of the lake with a major inlet. This inlet harbors emergent macrophytes that can mediate high CH4 fluxes. The difference between measured EC and FC fluxes can hence be explained by different footprint areas, where the EC system “sees” the part of the lake presumably releasing higher amounts of CH4. EC also provides more frequent measurements than FC and hence more likely captures ebullition events. This study shows that small lakes have CH4 fluxes that are highly variable in time and space. Based on our findings we suggest to measure CH4 fluxes from lakes as continuously as possible and to aim for covering as much of the lakes surface as possible, independently of the selected measuring technique.