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The High-Performance Airborne Imaging Spectrometer HyPlant—From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products: Introduction of an Automatized Processing Chain

Authors :
Rascher, Bastian Siegmann
Luis Alonso
Marco Celesti
Sergio Cogliati
Roberto Colombo
Alexander Damm
Sarah Douglas
Luis Guanter
Jan Hanuš
Kari Kataja
Thorsten Kraska
Maria Matveeva
Jóse Moreno
Onno Muller
Miroslav Pikl
Francisco Pinto
Juan Quirós Vargas
Patrick Rademske
Fernando Rodriguez-Morene
Neus Sabater
Anke Schickling
Dirk Schüttemeyer
František Zemek
Uwe
Source :
Remote Sensing; Volume 11; Issue 23; Pages: 2760
Publication Year :
2019
Publisher :
Multidisciplinary Digital Publishing Institute, 2019.

Abstract

The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400–2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670–780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps.

Details

Language :
English
ISSN :
20724292
Database :
OpenAIRE
Journal :
Remote Sensing; Volume 11; Issue 23; Pages: 2760
Accession number :
edsair.multidiscipl..ce0a0b8480590dd21c6354a9c96d8bca
Full Text :
https://doi.org/10.3390/rs11232760