The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA)

Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth's surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of ve...

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Bibliographic Details
Published in:Earth system science data 2020-01, Vol.12 (1), p.177-196
Main Authors: Moesinger, Leander, Dorigo, Wouter, de Jeu, Richard, van der Schalie, Robin, Scanlon, Tracy, Teubner, Irene, Forkel, Matthias
Format: Article
Language:English
Subjects:
Advanced Very High Resolution Radiometer
Anomalies
Archives & records
Autocorrelation
C band
Climate
Climate and vegetation
Climatic data
Cloud cover
Consistency
Datasets
Distribution functions
Earth
Earth surface
Ecological monitoring
Ecosystems
Environmental changes
Instruments
Interpolation
Leaf area
Leaf area index
Leaves
Long-term changes
Matching
Microwave imagery
Microwave radiometers
Microwave sensors
Moisture content
Optical analysis
Optical thickness
Outliers (statistics)
Plant cover
Products
Radiation
Radiation measurement
Radiometers
Random errors
Scaling
Sensors
Soil
Soil moisture
Soil surfaces
Special Sensor Microwave Imager
Spectral bands
Structural members
Superhigh frequencies
Tropical environments
Vegetation
Video-on-demand
Water content
Water depth
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Description
Summary:Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth's surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome.
ISSN:1866-3516
1866-3508
1866-3516
DOI:10.5194/essd-12-177-2020