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000891684 1001_ $$00000-0002-9327-9420$$aDe Grave, Charlotte$$b0$$eCorresponding author
000891684 245__ $$aRetrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor
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000891684 520__ $$aESA’s Eighth Earth Explorer mission “FLuorescence EXplorer” (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX’s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 μg cm−2). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 μg cm−2 for the new model versus RMSE = 11.9 μg cm−2 for the former model).
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000891684 7001_ $$00000-0003-0759-4422$$aPipia, Luca$$b1
000891684 7001_ $$0P:(DE-Juel1)172711$$aSiegmann, Bastian$$b2$$ufzj
000891684 7001_ $$00000-0002-0970-9492$$aMorcillo-Pallarés, Pablo$$b3
000891684 7001_ $$00000-0003-3188-1448$$aRivera-Caicedo, Juan Pablo$$b4
000891684 7001_ $$00000-0002-5283-3333$$aMoreno, José$$b5
000891684 7001_ $$00000-0002-6313-2081$$aVerrelst, Jochem$$b6
000891684 773__ $$0PERI:(DE-600)2513863-7$$a10.3390/rs13081419$$gVol. 13, no. 8, p. 1419 -$$n8$$p1419 -$$tRemote sensing$$v13$$x2072-4292$$y2021
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