A robust data cleaning procedure for eddy covariance flux measurements

Abstract. The sources of systematic error responsible for introducing significant biases in the eddy covariance (EC) flux computation are manifold, and their correct identification is made difficult by the lack of reference values, by the complex stochastic dynamics, and by the high level of noise characterizing raw data. This work contributes to overcoming such challenges by introducing an innovative strategy for EC data cleaning. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values, ensuring scalability of the whole process. The selection of best performing tests was carried out by means of Monte Carlo experiments, whereas the impact on real data was evaluated on data distributed by the Integrated Carbon Observation System (ICOS) research infrastructure. Results evidenced that the proposed procedure leads to an effective cleaning of EC flux data, avoiding the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. We expect that the proposed data cleaning procedure can serve as a basis towards a unified quality control strategy for EC datasets, in particular in centralized data processing pipelines where the use of robust and automated routines ensuring results reproducibility constitutes an essential prerequisite

Influences of observation errors in eddy flux data on inverse model parameter estimation

Eddy covariance data are increasingly used to estimate parameters of ecosystem models. For proper maximum likelihood parameter estimates the error structure in the observed data has to be fully characterized. In this study we propose a method to characterize the random error of the eddy covariance flux data, and analyse error distribution, standard deviation, cross- and autocorrelation of CO<sub>2</sub> and H<sub>2</sub>O flux errors at four different European eddy covariance flux sites. Moreover, we examine how the treatment of those errors and additional systematic errors influence statistical estimates of parameters and their associated uncertainties with three models of increasing complexity – a hyperbolic light response curve, a light response curve coupled to water fluxes and the SVAT scheme BETHY. In agreement with previous studies we find that the error standard deviation scales with the flux magnitude. The previously found strongly leptokurtic error distribution is revealed to be largely due to a superposition of almost Gaussian distributions with standard deviations varying by flux magnitude. The crosscorrelations of CO<sub>2</sub> and H<sub>2</sub>O fluxes were in all cases negligible (<i>R</i><sup>2</sup> below 0.2), while the autocorrelation is usually below 0.6 at a lag of 0.5 h and decays rapidly at larger time lags. This implies that in these cases the weighted least squares criterion yields maximum likelihood estimates. To study the influence of the observation errors on model parameter estimates we used synthetic datasets, based on observations of two different sites. We first fitted the respective models to observations and then added the random error estimates described above and the systematic error, respectively, to the model output. This strategy enables us to compare the estimated parameters with true parameters. We illustrate that the correct implementation of the random error standard deviation scaling with flux magnitude significantly reduces the parameter uncertainty and often yields parameter retrievals that are closer to the true value, than by using ordinary least squares. The systematic error leads to systematically biased parameter estimates, but its impact varies by parameter. The parameter uncertainty slightly increases, but the true parameter is not within the uncertainty range of the estimate. This means that the uncertainty is underestimated with current approaches that neglect selective systematic errors in flux data. Hence, we conclude that potential systematic errors in flux data need to be addressed more thoroughly in data assimilation approaches since otherwise uncertainties will be vastly underestimated

Early mapping of industrial tomato in Central and Southern Italy with Sentinel 2, aerial and RapidEye additional data

Timely crop information, i.e. well before harvesting time and at first stages of crop development, can benefit farmers and producer organizations. The current case study documents the procedure to deliver early data on planted tomato to users, showing the potential of Sentinel 2 to map tomato at the very beginning of the crop season, which is a challenging task. Using satellite data, integrated with ground and aerial data, an initial estimate of area planted with tomato and early tomato maps were generated in seven main production areas in Italy. Estimates of the amount of area planted with tomato provided similar results either when derived from field surveys or from remote sensing-based classification. Tomato early maps showed a producer accuracy > 80% in seven cases out of nine, and a user accuracy > 80% in five cases out of nine, with differences attributed to the varying agricultural characteristics and environmental heterogeneity of the study areas. The additional use of aerial data improved producer accuracy moderately. The ability to identify abrupt growth changes, such as those caused by natural hazards, was also analysed: Sentinel 2 detected significant changes in tomato growth between a hailstorm-affected area and a control area. The study suggests that Sentinel 2, with enhanced spectral capabilities and open data policy, represents very valuable data, allowing crop monitoring at an early development stage

Carbon balance assessment of a natural steppe of southern Siberia by multiple constraint approach

Steppe ecosystems represent an interesting case in which the assessment of carbon balance may be performed through a cross validation of the eddy covariance measurements against ecological inventory estimates of carbon exchanges (Ehman et al., 2002; Curtis et al., 2002). <br><br> Indeed, the widespread presence of ideal conditions for the applicability of the eddy covariance technique, as vast and homogeneous grass vegetation cover over flat terrains (Baldocchi, 2003), make steppes a suitable ground to ensure a constrain to flux estimates with independent methodological approaches. <br><br> We report about the analysis of the carbon cycle of a true steppe ecosystem in southern Siberia during the growing season of 2004 in the framework of the TCOS-Siberia project activities performed by continuous monitoring of CO<sub>2</sub> fluxes at ecosystem scale by the eddy covariance method, fortnightly samplings of phytomass, and ingrowth cores extractions for NPP assessment, and weekly measurements of heterotrophic component of soil CO<sub>2</sub> effluxes obtained by an experiment of root exclusion. <br><br> The carbon balance of the monitored natural steppe was, according to micrometeorological measurements, a sink of carbon of 151.7±36.9 g C m<sup>−2</sup>, cumulated during the growing season from May to September. This result was in agreement with the independent estimate through ecological inventory which yielded a sink of 150.1 g C m<sup>−2</sup> although this method was characterized by a large uncertainty (±130%) considering the 95% confidence interval of the estimate. Uncertainties in belowground process estimates account for a large part of the error. Thus, in particular efforts to better quantify the dynamics of root biomass (growth and turnover) have to be undertaken in order to reduce the uncertainties in the assessment of NPP. This assessment should be preferably based on the application of multiple methods, each one characterized by its own merits and flaws

The ecosystem carbon sink implications of mountain forest expansion into abandoned grazing land: The role of subsoil and climatic factors

Woody encroachment is a widespread phenomenon resulting from the abandonment of mountain agricultural and pastoral practices during the last century. As a result, forests have expanded, increasing biomass and necromass carbon (C) pools. However, the impact on soil organic carbon (SOC) is less clear. The main aim of this study was to investigate the effect of woody encroachment on SOC stocks and ecosystem C pools in six chronosequences located along the Italian peninsula, three in the Alps and three in the Apennines. Five stages along the chronosequences were identified in each site. Considering the topsoil (0 30 cm), subsoil (30 cm-bedrock) and whole soil profile, the temporal trend in SOC stocks was similar in all sites, with an initial increment and subsequent decrement in the intermediate phase. However, the final phase of the woody encroachment differed significantly between the Alps (mainly conifers) and the Apennines (broadleaf forests) sites, with a much more pronounced increment in the latter case. Compared to the previous pastures, after mature forest (>62 years old) establishment, SOC stocks increased by: 2.1(mean) ± 18.1(sd) and 50.1 ± 25.2 Mg C·ha -1 in the topsoil, 7.3 ± 17.4 and 93.2 ± 29.7 Mg C·ha -1 in the subsoil, and 9.4 ± 24.4 and 143.3 ± 51.0 Mg C·ha -1 in the whole soil profile in Alps and Apennines, respectively. Changes in SOC stocks increased with mean annual air temperature and average minimum winter temperature, and were negatively correlated with the sum of summer precipitation. At the same time, all other C pools (biomass and necromass) increased by 179.1 ± 51.3 and 304.2 ± 67.6 Mg C·ha -1 in the Alps and the Apennines sites, respectively. This study highlights the importance of considering both the subsoil, since deep soil layers contributed 38% to the observed variations in carbon stocks after land use change, and the possible repercussions for the carbon balance of large areas where forests are expanding, especially under pressing global warming scenarios. © 2019 Elsevier B.V.The project of this work is part of the research activities of the PhD in science, technology and biotechnology for sustainability. The first author received a fully founded scholarship partially by the University of Tuscia (Viterbo - Italy) and partially by the University of Molise . Tommaso Chiti participated in the project by conducting his work with the funding obtained through the LIFE MediNet project (grant number LIFE15 PRE IT/732295 ). Jorge Curiel Yuste was financed in part by the Basque Government through the BERC 2018-2021 (grant code) program and by Spanish Ministry of Economy and Competitiveness (MINECO) through BC3 María de Maeztu excellence accreditation MDM-2017-0714. I.C (grant code)

Characterizing ecosystem-atmosphere interactions from short to interannual time scales

International audienceCharacterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series currently comprise an observation period of up to one decade. In this study, we explored whether the observation period is already sufficient to detect cross-relationships between the variables beyond the annual cycle, as they are expected from comparable studies in climatology. We investigated the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation. We found that most observations (Net Ecosystem Exchange, NEE, Gross Primary Productivity, GPP, Ecosystem Respiration, Reco, Vapor Pressure Deficit, VPD, Latent Heat, LE, Sensible Heat, H, Wind Speed, u, and Precipitation, P) were influenced significantly by low-frequency components (interannual variability). Furthermore, we extracted a set of nontrivial relationships and found clear seasonal hysteresis effects except for the interrelation of NEE with Global Radiation (Rg). SSA provides a new tool for the investigation of these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing, since it can be applied as a novel gap filling approach relying on the temporal correlation structure of the time series structure only

Remote sensing of ecosystem light use efficiency with MODIS-based PRI

Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well

Eddy Covariance flux errors due to random and systematic timing errors during data acquisition

Modern eddy covariance (EC) systems collect high-frequency data (10–20 Hz) via digital outputs of instru ments. This is an important evolution with respect to the tra ditional and widely used mixed analog/digital systems, as fully digital systems help overcome the traditional limita tions of transmission reliability, data quality, and complete ness of the datasets

COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy

Mediterranean maquis is a complex and widespread ecosystem in the region, intrinsically prone to fire. Many species have developed specific adaptation traits to cope with fire, ensuring resistance and resilience. Due to the recent changes in socio-economy and land uses, fires are more and more frequent in the urban-rural fringe and in the coastlines, both now densely populated. The detection of fires and the monitoring of vegetation regrowth is thus of primary interest for local management and for understanding the ecosystem dynamics and processes, also in the light of the recurrent droughts induced by climate change. Among the main objectives of the COSMO-SkyMed radar constellation mission there is the monitoring of environmental hazards; the very high revisiting time of this mission is optimal for post-hazard response activities. However, very few studies exploited such data for fire and vegetation monitoring. In this research, Cosmo-SkyMed is used in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth. The positive results obtained in this research highlight the importance of the very high-resolution continuous acquisitions and the multi-polarization information provided by COSMO-SkyMed for monitoring fire impacts on vegetation

Temperature sensitivity of decomposition in relation to soil organic matter pools: Critique and outlook

Knorr et al. (2005) concluded that soil organic carbon pools with longer turnover times are more sensitive to temperature. We show that this conclusion is equivocal, largely dependent on their specific selection of data and does not persist when the data set of Kätterer et al. (1998) is analysed in a more appropriate way. Further, we analyse how statistical properties of the model parameters may interfere with correlative analyses that relate the Q 10 of soil respiration with the basal rate, where the latter is taken as a proxy for soil organic matter quality. We demonstrate that negative parameter correlations between Qio-values and base respiration rates are statistically expected and not necessarily provide evidence for a higher temperature sensitivity of low quality soil organic matter. Consequently, we propose it is premature to conclude that stable soil carbon is more sensitive to temperature than labile carbon