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|Title:||Evaluation of Agreement between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series|
|Authors:||MERONI MICHELE; ATZBERGER Clement; VANCUTSEM CHRISTELLE; GOBRON Nadine; BARET Frederic; LACAZE Roselyne; EERENS Herman; LEO Olivier|
|Citation:||IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING|
|Publisher:||IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC|
|Type:||Articles in periodicals and books|
|Abstract:||Satellite-derived time series of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are widely used to monitor vegetation dynamics and to detect vegetation anomalies. Several global datasets are available for this purpose. They are produced using different algorithms and/or satellite sensors. This paper compares and analyses three multitemporal fAPAR datasets derived from SPOT-VEGETATION instrument by explicitly distinguishing between spatial and temporal agreement. The first two datasets are currently used by JRC-MARS for operational yield forecasting and food security assessments. The third time series (named GEOV1) is from a new processing algorithm developed within the European FP7 Geoland2 project. The comparative analysis was conducted for the years 2003 and 2004 over three 10° x 10° regions with different eco-climatic characteristics (Niger, Brazil and France). Our study revealed that GEOV1 fAPAR estimates were systematically higher than those of JRC-MARS. The spatial analysis showed moderate to high agreement between datasets with specific seasonality in the three study regions. The temporal agreement showed spatial (and land cover related) variability spanning from very low to almost perfect. Large differences were observed in regions and periods with large cloud occurrence where GEOV1 provides more reliable and smooth temporal profiles due to improved cloud screening and longer compositing periods. Other sources of disagreement between datasets were identified in differences in the fAPAR retrieval algorithm definitions.|
|JRC Institute:||Institute for Environment and Sustainability|
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