{"type": "FeatureCollection", "features": [{"id": "10.1016/j.agrformet.2007.08.011", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:05Z", "type": "Journal Article", "created": "2007-09-26", "title": "Comprehensive Comparison Of Gap-Filling Techniques For Eddy Covariance Net Carbon Fluxes", "description": "Abstract   We review 15 techniques for estimating missing values of net ecosystem CO 2  exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on a set of 10 benchmark datasets from six forested sites in Europe.  The goal of gap filling is the reproduction of the NEE time series and hence this present work focuses on estimating missing NEE values, not on editing or the removal of suspect values in these time series due to systematic errors in the measurements (e.g., nighttime flux, advection). The gap filling was examined by generating 50 secondary datasets with artificial gaps (ranging in length from single half-hours to 12 consecutive days) for each benchmark dataset and evaluating the performance with a variety of statistical metrics. The performance of the gap filling varied among sites and depended on the level of aggregation (native half-hourly time step versus daily), long gaps were more difficult to fill than short gaps, and differences among the techniques were more pronounced during the day than at night.  The non-linear regression techniques (NLRs), the look-up table (LUT), marginal distribution sampling (MDS), and the semi-parametric model (SPM) generally showed good overall performance. The artificial neural network based techniques (ANNs) were generally, if only slightly, superior to the other techniques. The simple interpolation technique of mean diurnal variation (MDV) showed a moderate but consistent performance. Several sophisticated techniques, the dual unscented Kalman filter (UKF), the multiple imputation method (MIM), the terrestrial biosphere model (BETHY), but also one of the ANNs and one of the NLRs showed high biases which resulted in a low reliability of the annual sums, indicating that additional development might be needed. An uncertainty analysis comparing the estimated random error in the 10 benchmark datasets with the artificial gap residuals suggested that the techniques are already at or very close to the noise limit of the measurements. Based on the techniques and site data examined here, the effect of gap filling on the annual sums of NEE is modest, with most techniques falling within a range of \u00b125\u00a0g\u00a0C\u00a0m \u22122 \u00a0year \u22121 .", "keywords": ["Net ecosystem exchange (NEE)", "Gap-filling comparison", "550", "FLUXNET", "0207 environmental engineering", "Eddy covariance", "02 engineering and technology", "01 natural sciences", "630", "Carbon flux", "Review of gap-filling techniques", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agrformet.2007.08.011"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Forest%20Meteorology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agrformet.2007.08.011", "name": "item", "description": "10.1016/j.agrformet.2007.08.011", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agrformet.2007.08.011"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2007-12-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2020.112030", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:57Z", "type": "Journal Article", "created": "2020-08-12", "title": "Sun-induced fluorescence closely linked to ecosystem transpiration as evidenced by satellite data and radiative transfer models", "description": "Closed Access[EN] Transpiration (7) returns about half of continental precipitation back into the atmosphere. However, the global spatial and temporal dynamics of transpiration are highly uncertain, and current estimates rely on either indirect remote sensing or empirical model formulations. Here, we show that T can be estimated reliably at the global scale using observations of plant sun-induced fluorescence (SIF). To do so, we derive T using two different methods from globally-distributed eddy-covariance measurements and compare it with satellite SIF retrievals from GOME-2 and OCO-2. Whereas most research to date has focused on the link between SIF and gross primary production (GPP), we demonstrate that SIF is as highly correlated with T (mean correlation coefficient R of 0.76 across sites for 16-day periods with GOME-2 and 0.75 at the daily scale with OCO-2). SIF shows a greater predictive skill to estimate T than traditional optical vegetation indices and its dynamics are very similar to those of T. Through the use of an advanced radiative transfer model, we also demonstrate a clear empirical link between SIF and T. At 83 FLUXNET sites, remote sensing data and flux-derived GPP and T are used to estimate the relevant parameters of the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) radiative transfer model and to model SIF. While the relationship between SIF and photosynthesis (GPP) is mostly controlled by leaf biochemical properties and plant structure, the SIF-T relationship appears largely determined by air temperature and intrinsic water use efficiency. Our findings suggest that ongoing advances in satellite SIF retrievals will allow for a more direct estimation of transpiration over large scales", "keywords": ["Evapotranspiration", "FLUXNET", "0207 environmental engineering", "02 engineering and technology", "Solar-induced fluorescence", "15. Life on land", "01 natural sciences", "Transpiration", "OCO-2", "GOME-2", "SCOPE", "13. Climate action", "FISICA APLICADA", "Photosynthesis", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2020.112030"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2020.112030", "name": "item", "description": "10.1016/j.rse.2020.112030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2020.112030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-01T00:00:00Z"}}, {"id": "10.3390/rs10111720", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:10Z", "type": "Journal Article", "created": "2018-10-31", "title": "Towards Estimating Land Evaporation at Field Scales Using GLEAM", "description": "<p>The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013\uffe2\uff80\uff932017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink\uffe2\uff80\uff99s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.</p>", "keywords": ["microwave remote sensing", "EVAPOTRANSPIRATION", "WACMOS-ET PROJECT", "Science", "FLUXNET", "Q", "LSA-SAF", "15. Life on land", "01 natural sciences", "6. Clean water", "MODEL", "CARBON", "VARIABILITY", "terrestrial evaporation", "root-zone soil moisture", "13. Climate action", "Earth and Environmental Sciences", "SURFACE EVAPORATION", "GLOBAL DATABASE", "WATER", "SOIL-MOISTURE RETRIEVALS", "terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF", "GLEAM", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/11/1720/pdf"}, {"href": "https://doi.org/10.3390/rs10111720"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10111720", "name": "item", "description": "10.3390/rs10111720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10111720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-31T00:00:00Z"}}, {"id": "10.5281/zenodo.1158523", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:19:59Z", "type": "Dataset", "title": "fLUE", "description": "Open AccessThis dataset contains fLUE data as described in Stocker et al., (2018) <em>New Phytologist</em>. fLUE is derived from the FLUXNET 2015 dataset, Tier 1, daily. Only sites are included where the method for quantifying fLUE satisfied performance criteria (see Stocker et al. 2018). <em>site</em>: Site name (ID) from the FLUXNET network <em>date</em>: DD/MM/YY year: year <em>doy</em>: day of year <em>fLUE</em>: unitless, fraction of actual over potential light use efficiency, derived from artificial neural networks. This quantifies the fractional reduction in light use efficiency due to soil moisture (1 = no reduction). <em>is_flue_drought</em>: TRUE if the data is identified as a 'drought' based on deviation of fLUE from 1 (see Stocker et al., 2018) <em>cluster</em>: sites are assigned to clusters based on their typical parallel evolution of greenness and fLUE throughout drought events. cDD: 'drought deciduous', cGR: 'evergreen', cLS: 'low sensitivity', cNA: 'not affected'.", "keywords": ["13. Climate action", "FLUXNET", "carbon cycle", "GPP", "drought", "15. Life on land", "soil moisture", "6. Clean water"], "contacts": [{"organization": "Stocker, Benjamin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.1158523"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.1158523", "name": "item", "description": "10.5281/zenodo.1158523", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.1158523"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-24T00:00:00Z"}}, {"id": "10.5281/zenodo.1158524", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:19:59Z", "type": "Dataset", "title": "fLUE", "description": "Open AccessThis dataset contains fLUE data as described in Stocker et al., (2018) <em>New Phytologist</em>. fLUE is derived from the FLUXNET 2015 dataset, Tier 1, daily. Only sites are included where the method for quantifying fLUE satisfied performance criteria (see Stocker et al. 2018). <em>site</em>: Site name (ID) from the FLUXNET network <em>date</em>: DD/MM/YY year: year <em>doy</em>: day of year <em>fLUE</em>: unitless, fraction of actual over potential light use efficiency, derived from artificial neural networks. This quantifies the fractional reduction in light use efficiency due to soil moisture (1 = no reduction). <em>is_flue_drought</em>: TRUE if the data is identified as a 'drought' based on deviation of fLUE from 1 (see Stocker et al., 2018) <em>cluster</em>: sites are assigned to clusters based on their typical parallel evolution of greenness and fLUE throughout drought events. cDD: 'drought deciduous', cGR: 'evergreen', cLS: 'low sensitivity', cNA: 'not affected'.", "keywords": ["13. Climate action", "FLUXNET", "carbon cycle", "GPP", "drought", "15. Life on land", "soil moisture", "6. Clean water"], "contacts": [{"organization": "Stocker, Benjamin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.1158524"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.1158524", "name": "item", "description": "10.5281/zenodo.1158524", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.1158524"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-24T00:00:00Z"}}, {"id": "10.5281/zenodo.3247929", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:20:42Z", "type": "Dataset", "title": "GPP at FLUXNET Tier 1 sites from P-model", "description": "Gross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location. The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. <em>Geosci. Mod. Dev. </em>(in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column <em>setup</em>. The data differs slightly for each file: <strong>Daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>date: </em>YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in __meanseason, specifying the day-of-year), <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>8-daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>date_start</em> : YYYY-MM-DD specifying the first day of the respective 8-day period <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Annual</strong> gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>year: </em>YYYY <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Spatial</strong> gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Mean seasonal cycle</strong> gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>doy: </em>day-of-year <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.", "keywords": ["FLUXNET", "GPP", "Carbon cycle", "Photosynthesis", "Remote sensing"], "contacts": [{"organization": "Stocker, Benjamin D", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3247929"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3247929", "name": "item", "description": "10.5281/zenodo.3247929", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3247929"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-18T00:00:00Z"}}, {"id": "10.5281/zenodo.3559850", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:20:44Z", "type": "Dataset", "title": "GPP at FLUXNET Tier 1 sites from P-model", "description": "Gross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location. The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. <em>Geosci. Mod. Dev. </em>(in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column <em>setup</em>. The data differs slightly for each file: <strong>Daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>date: </em>YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in __meanseason, specifying the day-of-year), <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>8-daily</strong> gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>date_start</em> : YYYY-MM-DD specifying the first day of the respective 8-day period <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Annual</strong> gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>year: </em>YYYY <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Spatial</strong> gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> yr<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below. <strong>Mean seasonal cycle</strong> gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv: <em>sitename</em>: A character specifying the site ID following the naming given by FLUXNET 2015. <em>doy: </em>day-of-year <em>gpp</em>: Simulated gross primary production, in units of g C m<sup>-2</sup> d<sup>-1</sup> <em>setup</em>: A character specifying the model setup name used in Stocker et al. (2019). See also below.", "keywords": ["FLUXNET", "GPP", "Carbon cycle", "Photosynthesis", "Remote sensing"], "contacts": [{"organization": "Stocker, Benjamin D", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3559850"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3559850", "name": "item", "description": "10.5281/zenodo.3559850", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3559850"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-18T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FLUXNET&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FLUXNET&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FLUXNET&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FLUXNET&offset=7", "hreflang": "en-US"}], "numberMatched": 7, "numberReturned": 7, "distributedFeatures": [], "timeStamp": "2026-09-20T21:00:14.596830Z"}