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Potential of the sentinel-2 red edge spectral bands for estimation of eco-physiological plant parameters
Malenovský, Zbyněk ; Homolová, Lucie ; Janoutová, Růžena ; Landier, L. ; Gastelluetchegorry, J-P. ; Bertholt, B. ; Huck, A.
In this study we investigated importance of the spaceborne instrument Sentinel-2 red edge spectral bands and reconstructed red edge position (REP) for retrieval of the three eco-physiological plant parameters, leaf and canopy chlorophyll content and leaf area index (LAI), in case of maize agricultural fields and beech and spruce forest stands. Sentinel-2 spectral bands and REP of the investigated vegetation canopies were simulated in the Discrete Anisotropic Radiative Transfer (DART) model. Their potential for estimation of the plant parameters was assessed through training support vector regressions (SVR) and examining their P-vector matrices indicatingsignificance of each input. The trained SVR were then applied on Sentinel-2 simulated images and the acquired\nestimates were cross-compared with results from high spatial resolution airborne retrievals. Results showed that contribution of REP was significant for canopy chlorophyll content, but less significant for leaf chlorophyll content and insignificant for leaf area index estimations. However, the red edge spectral bands contributed strongly to the retrievals of all parameters, especially canopy and leaf chlorophyll content. Application of SVR on Sentinel-2 simulated images demonstrated, in general, an overestimation of leaf chlorophyll content and an underestimation of LAI when compared to the reciprocal airborne estimates. In the follow-up investigation, we will apply the trained SVR algorithms on real Sentinel-2 multispectral images acquired during vegetation seasons 2015 and 2016.
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Potentials of the VNIR airborne hyperspectral system AISA Eagle
Hanuš, Jan ; Malenovský, Zbyněk ; Homolová, Lucie ; Kaplan, Věroslav ; Cudlín, Pavel ; Lukeš, Petr
Airborne hyperspectral remote sensing (imaging spectroscopy) sensors acquire images of several (from tens to hundreds) narrow spectral bands in visible, near and short infrared wavelengths. Use of hyperspectral remote sensing (RS) data in scientific and even commercial applications is quite broad, starting from agriculture, forestry, and natural vegetation (precision farming, assessment of general plant status, biomass estimation, species composition mapping), through geology (mapping of minerals, land degradation assessment), up to limnology (water quality evaluation), and other domains. Since 2004 the Institute of Systems Biology and Ecology (ISBE) (Academy of Sciences of the Czech Republic) has been operating the VNIR airborne hyperspectral sensor AISA Eagle. The workgroup for remote sensing of vegetation at ISBE is currently capable to facilitate the complete flight/ground hyperspectral campaign including the standard image data pre-processing.
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