Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products wi...

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Veröffentlicht in:	Remote sensing of environment Jg. 118; S. 127 - 139
Hauptverfasser:	Verrelst, Jochem, Muñoz, Jordi, Alonso, Luis, Delegido, Jesús, Rivera, Juan Pablo, Camps-Valls, Gustavo, Moreno, José
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY Elsevier Inc 15.03.2012 Elsevier
Schlagworte:	algorithms Animal, plant and microbial ecology Applied geophysics artificial intelligence Biological and medical sciences Biophysical parameter retrieval chlorophyll confidence interval Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology Gaussian Processes regression (GPR) General aspects. Techniques Internal geophysics Kernel ridge regression (KRR) Landsat leaf area index leaves Machine learning monitoring neural networks reflectance Regression algorithms remote sensing Sentinel-2 Sentinel-3 Spain Support vector regression (SVR) Teledetection and vegetation maps vegetation cover Iberian Peninsula Southern Europe Europe Spain Biophysical parameter retrieval Support vector regression (SVR) Regression algorithms Kernel ridge regression (KRR) Sentinel-3 Sentinel-2 Machine learning Gaussian Processes regression (GPR) Parameter estimation data Spot adaptation accuracy vegetation Image artificial intelligence Test method Learning algorithm Ability Availability time resolution Fitting Space remote sensing Landsat Biophysics monitoring Real time Landsat satellite regression Nonlinearity Parameter Reflectance
ISSN:	0034-4257, 1879-0704
Online-Zugang:	Volltext
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