Stationary field spectrometer measurements

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Provided by GKSt GovData Freie und Hansestadt Hamburg

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Dataset information

Catalog
Country of origin
Updated
2023.03.06 11:04
Created
2021.10.20
Available languages
German
Keywords
mfund-projekt-mdrones4rivers, mcloud_ide88218f5-ee6f-414f-be46-35003e6c1b89, hyperspektral, emmericher-ward, vegetation, sedimente, mfund-fkz-bmvi-19f2054a-d, nonnenwerth, zeitreihen, mcloud_category_waters
Quality scoring
250

Dataset description

Processed stationary field spectroscopy measurementes. Time series were collected with solar powered long-term installations (LTI) using the RoX field spectrometer within the frame of the mDRONES4RIVERS project in the years 2019, 2020 and 2021 respectively. Measurement were recorded at the southern tip of the island Nonnenwerth in 2019 and 2020, over a side arm of the Rhine in Emmericher Ward 2019, 2020, 2021 (as Emmerich I) and in a ditch at the side arm in Emmericher Ward 2020 and 2021 (as Emmerich II) in Germany. LTI Nonnenwerth was investigating dewberry (Rubus caesius), LTI Emmerich I was investigating sediment dynamics and mixed pioneering vegetation and LTI Emmerich II was investigating mainly mixed pioneering vegetation. The incoming radiance is measured with optics of hemispherical (~180°) field of view and the reflected radiance with conical (~25°) field of view. Zip repositories contain folders named according to LTI place and year of recording. They include human radable.csv files for raw RoX data, calibrated radiance and reflectance in subfolders named after date of recording, a folder with time-lapse photographs of the footprint (only for Emmerich I and Emmerich II) and an ALL_INDEX file, each. For reflectance and radiance files, the first collumn contains the associated central wavelengths, the first row the time of recording, each subsequent field of the table contains the calibrated radiance (in W m-2 sr-1 nm-1) or reflectance (unitless) values. The ALL_INDEX file contains meta data, computed vegetation indices(VI) and quality flags for the time series.
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