You are viewing Version 2, the most recent version of this dataset.
2 version(s) available
Date of publication: July 18, 2023

Version 2

Date of publication: July 18, 2023

Type of change: Metadata

Description: Update of description, adding some new scenarios.

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Canopy Chlorophyll Content for the Netherlands

by Elnaz Neinavaz

The canopy chlorophyll content for the Netherlands at 10 meters resolution on monthly basis for the year 2020. Dataset generated under Showcase. 4, pilot 4.3 myVARIABLE of the e-shape project. Canopy Chlorophyll Content (CCC) is the total amount of chlorophyll a and b pigments in a contiguous group of plants per unit ground area often expressed in mg/m2 (Gitelson et al., 2005). It is a product of leaf chlorophyll content (i.e., the chlorophyll co ...(continue reading)

DOI 10.25829/zgc464
Citation
Neinavaz, E., Nieuwenhuis, W. (2023). Canopy Chlorophyll Content for the Netherlands (Version 2) [Dataset]. German Centre for Integrative Biodiversity Research. https://doi.org/10.25829/zgc464

Canopy Chlorophyll ContentSentinel-2Netherlandse-shapeRemote Sensing-EBVs

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The title of the dataset. Canopy Chlorophyll Content for the Netherlands
The date on which this version of the data was created in YYYY-MM-DD format.
A paragraph describing the dataset.
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The canopy chlorophyll content for the Netherlands at 10 meters resolution on monthly basis for the year 2020. Dataset generated under Showcase. 4, pilot 4.3 myVARIABLE of the e-shape project. Canopy Chlorophyll Content (CCC) is the total amount of chlorophyll a and b pigments in a contiguous group of plants per unit ground area often expressed in mg/m2 (Gitelson et al., 2005). It is a product of leaf chlorophyll content (i.e., the chlorophyll content of a fresh green leaf divided by its one-side area (µmg/cm2)) and the leaf area index. CCC is a terrestrial ecosystem function EBV that describes the distribution of chlorophyll pigments within the 3D canopy surface. This defines the total photosynthetically active radiation absorbed by the canopy (Gitelson et al., 2003; Ustin et al., 2009). The chlorophyll content is one of a constellation of co-evolved traits that vary together across species in relation to contrasting environmental conditions (Reich et al., 2003). Monitoring the dynamics of the CCC helps to understand the adaptation of forest, crop, and other plant canopies to such factors (Feret et al. 2017). As a result, the amount and spatial distribution of chlorophyll content is vital for measuring and understanding plant growth, ecosystem primary productivity and ecosystem dynamics. References can be found in the comment field.
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The method of production of the original data.
Hover to see a suggestion for a minimum description. Sentinel-2A images with 10-meter resolution acquired in 2020 (from January to November) on the monthly basis, on a relatively cloud-free date(s), (< 10% cloud cover), and were used to predict the CCC for the Netherlands. For retrieval of the CCC, two simple ratio vegetation indices optimized for forests and non-forest vegetation were applied (Ali et al., 2020a; Ali et al., 2020b). Ali, A. M., Darvishzadeh, R., Skidmore, A., Heurich, M., Paganini, M., Heiden, U., & Mücher, S. (2020a). Evaluating prediction models for mapping canopy chlorophyll content across biomes. Remote Sensing, 12(11), 1788. Ali, A. M., Darvishzadeh, R., Skidmore, A., Gara, T. W., O’Connor, B., Roeoesli, C., ... & Paganini, M. (2020b). Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, 87, 102037. Geotiff dataset download links: https://dikke.itc.utwente.nl:5001/sharing/XhROrVF8C, https://dikke.itc.utwente.nl:5001/sharing/dxpmUlfuo, https://dikke.itc.utwente.nl:5001/sharing/RdBnKbdFD, https://dikke.itc.utwente.nl:5001/sharing/FMalUW5qF, https://dikke.itc.utwente.nl:5001/sharing/RvrQrrhVg, https://dikke.itc.utwente.nl:5001/sharing/EBG9k7cb1, https://dikke.itc.utwente.nl:5001/sharing/77pz5Puc5, https://dikke.itc.utwente.nl:5001/sharing/z2wGyLyns, https://dikke.itc.utwente.nl:5001/sharing/mkNTzdjL2, https://dikke.itc.utwente.nl:5001/sharing/KxinWzVUi, https://dikke.itc.utwente.nl:5001/sharing/iH6z73q8K, https://dikke.itc.utwente.nl:5001/sharing/9TqcI5l5T
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The name of the Project.
The URL from the project website. https://e-shape.eu/
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The email of the person or other creator type principally responsible for creating this data. e.neinavaz@utwente.nl

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e.neinavaz@utwente.nl

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Please select the CC license from the list. We recommend the use of CC BY 4.0

Essential Biodiversity Variables

Select the EBV class and the EBV name for the dataset. For cross-cutting use the comment at the bottom of the page for further information.
Genetic composition
Intraspecific genetic diversity
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Species populations
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Species traits
Morphology
Physiology
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Community composition
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Trait diversity
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Ecosystem functioning
Primary productivity
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Ecosystem structure
Live cover fraction
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Cross-cutting

Entity

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Scenario

Spatial domain

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Name of the continent/region/country/area, separated by comma
meter
southWest lat: 5622120, lon: 523740
northEast lat: 5942330, lon: 798180

Temporal domain

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decadal
annually
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weekly
daily
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Environmental domain *

Terrestrial
Marine
Freshwater
Miscellaneous information about the data, not captured elsewhere. E-shape is a unique initiative that brings together decades of public investment in Earth Observation and in cloud capabilities into services for the decision-makers, the citizens, the industry and the researchers. It allows Europe to position itself as global force in Earth observation by leveraging Copernicus, making use of existing European capacities and improving user uptake of the data from GEO assets. In the e-shape initiative, 27 cloud-based pilot applications under the seven thematic areas address societal challenges, foster entrepreneurship and support sustainable development, in alignment to the three main GEO priorities (e.g., SDGs, Paris Agreement and Sendaï Framework). References for the summary: Féret, J, B., A.A. Gitelson, S.D. Noble, S. Jacquemoud PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle Remote Sens. Environ., 193 (2017), pp. 204-215 Gitelson, A.A, Andrés Viña Verónica Ciganda Donald C. Rundquist Timothy J. Arkebauer (2005), Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32(8). https://doi.org/10.1029/2005GL022688 Gitelson, A. A., Vina, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), 52–54. Retrieved from http://www.sciencedirect.com/science/article/B6WPY-49H70PP-497/2/4cd9159e3f39a07a15c0f430abd8bbd4 Reich, P. B., Buschena, C., Tjoelker, M. G., Wrage, K., Knops, J., Tilman, D. & Machado, J. L. 2003. Variation in growth rate and ecophysiology among 34 grassland and savanna species under contrasting N supply: a test of functional group differences. New Phytologist, 157, 617-631. Ustin, S. L., Gitelson, A. A., Jacquemoud, S., Schaepman, M., Asner, G. P., Gamon, J. A., & Zarco-Tejada, P. (2009). Retrieval of foliar information about plant pigment systems from high-resolution spectroscopy. Remote Sensing of Environment, 113(Supplement 1), S67–S77. Retrieved from http://www.sciencedirect.com/science/article/B6V6V-4W7RJYR-1/2/58a4cb652674c7aea4caed7df189aacc.
Entities
Vegetation