Sun-induced Fluorescence - a New Probe of Photosynthesis: First Maps from the Imaging Spectrometer HyPlant
Overview
Environmental Health
Authors
Affiliations
Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress.
Improved air quality leads to enhanced vegetation growth during the COVID-19 lockdown in India.
Kashyap R, Kuttippurath J, Patel V Appl Geogr. 2023; 151:102869.
PMID: 36619606 PMC: 9805897. DOI: 10.1016/j.apgeog.2022.102869.
Peng H, Cendrero-Mateo M, Bendig J, Siegmann B, Acebron K, Kneer C Sensors (Basel). 2022; 22(23).
PMID: 36502141 PMC: 9740991. DOI: 10.3390/s22239443.
Tagliabue G, Panigada C, Dechant B, Baret F, Cogliati S, Colombo R Remote Sens Environ. 2022; 231:111272.
PMID: 36082142 PMC: 7613358. DOI: 10.1016/j.rse.2019.111272.
Sabater N, Vicent J, Alonso L, Verrelst J, Middleton E, Porcar-Castell A Remote Sens (Basel). 2022; 10(10):1551.
PMID: 36081617 PMC: 7613352. DOI: 10.3390/rs10101551.
Candiani G, Tagliabue G, Panigada C, Verrelst J, Picchi V, Caicedo J Remote Sens (Basel). 2022; 14(8):1792.
PMID: 36081596 PMC: 7613389. DOI: 10.3390/rs14081792.