A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing

Pfitzner, K.; Bollhöfer, A.; Carr, Gabrielle

doi:10.1080/14498596.2006.9635083

Cited by 41 publications

(23 citation statements)

References 28 publications

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“…Cloud coverage and photos of the sky condition are indicators of the prevailing illumination conditions and thus relevant metadata for the hyperspectral data acquired with the FieldSpec 3 spectroradiometer (Pfitzner et al, 2006). Overall cloud coverage (Cloud cov) expressed in oktas was assessed visually and documented once per ESU.…”

Section: Sky Conditionsmentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

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Section: Sky Conditionsmentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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“…Jong (2001) and Pfitzner et al (2005Pfitzner et al ( , 2006) sum up to a total of 57 parameters. Most of the additional variables not accounted for in the SPECCHIO data model are connected with enhanced target information such as ground cover, soil, phenology or plant height.…”

Section: Discussionmentioning

confidence: 99%

“…The metadata variables implemented in the SPECCHIO system are based on Bojinski et al (2003) and Pfitzner et al (2005Pfitzner et al ( , 2006. Table 1 …”

Section: Metadata Of Spectral Data Collectionsmentioning

confidence: 99%

The spectral database SPECCHIO for improved long-term usability and data sharing

Hueni¹,

Nieke²,

Schopfer³

et al. 2009

Computers & Geosciences

131

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The spectral database SPECCHIO for improved long-term usability and data sharing AbstractThe organised storage of spectral data described by metadata is important for long-term use and data sharing with other scientists. Metadata describing the sampling environment, geometry and measurement process serves to evaluate the suitability of existing data sets for new applications. There is a need for spectral databases that serve as repositories for spectral field campaign and reference signatures, including appropriate metadata parameters. Such systems must be (a) highly automated in order to encourage users entering their spectral data collections and (b) provide flexible data retrieval mechanisms based on subspace projections in metadata spaces. The recently redesigned SPECCHIO system stores spectral and metadata in a relational database based on a non-redundant data model and offers efficient data import, automated metadata generation, editing and retrieval via a Java application. RSL is disseminating the database and software to the remote sensing community in order to foster the use and further development of spectral databases. The organised storage of spectral data described by according metadata is important for long term use and data sharing with other scientists. Metadata describing the sampling environment, geometry and measurement process serves to evaluate the suitability of existing data sets for new applications. There is a need for spectral databases that serve as repositories for spectral field campaign and reference signatures including appropriate metadata parameters. Such systems must be (a) highly automated in order to encourage users entering their spectral data collections and (b) provide flexible data retrieval mechanisms based on subspace projections in metadata spaces. The recently redesigned SPECCHIO system stores spectral and metadata in a relational database based on a nonredundant data model and offers efficient data import, automated metadata generation, editing and retrieval via a Java application. RSL is disseminating the database and software to the remote sensing community in order to foster the use and further development of spectral databases.

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“…The time invested in metadata collection is outweighed by its benefits in enabling detailed quality assessment of the data, and its multiple reuses [1]. Although standard protocols exist for some environments there is currently no internationally standardized methodology for collecting in situ spectroscopy data or metadata protocols for remote sensing applications across terrestrial, marine and atmospheric environments.…”

Section: Introductionmentioning

confidence: 99%

Approaches to establishing a metadata standard for field spectroscopy datasets

Rasaiah

Malthus

Bellman

et al. 2013

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS

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There is an urgent need within the international remote sensing community to establish a metadata standard for field spectroscopy that ensures high quality, interoperable metadata sets that can be archived and shared efficiently within Earth observation data sharing systems. Careful examination of all stages of metadata collection and analysis can inform a robust standard that is applicable to a range of field campaigns. This paper presents approaches towards a standard that encompasses in situ metadata collection and initiatives towards sharing metadata within intelligent archiving systems.

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A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing

Cited by 41 publications

References 28 publications

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

The spectral database SPECCHIO for improved long-term usability and data sharing

Approaches to establishing a metadata standard for field spectroscopy datasets

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