Abstract
ESA’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).
Highlights
The world’s continued demographic growth together with the climate crisis put increasing pressure on the global food supply [1,2]
In the framework of the future FLuorescence EXplorer (FLEX) mission, Ocean and Land Color Instrument (OLCI) vegetation products were developed, and this work presents the first validation exercise based on in situ data coming from the 2018 FLEXSense campaign
In the framework of the FLEX/S3 tandem mission, hybrid retrieval models for estimating vegetation products from OLCI reflectance data were developed, and were subjected to a first validation exercise using in situ measurements collected during the 2018 FLEXSense campaign in croplands around Jülich in the western part of Germany
Summary
The world’s continued demographic growth together with the climate crisis put increasing pressure on the global food supply [1,2] In this context, determining the photosynthetic potential and the primary production of vegetation is crucial, as it underlies the Remote Sens. Changes in LCC produce broad variations in the leaf reflectance and transmittance, which in turn influence the canopy reflectance The latter is strongly affected by other factors, such as the canopy architecture, the Chl distribution in the canopy, the leaf area index (LAI), and the soil background, which mask and confound the changes caused by LCC [7]. CCC is often approximated by the product of LAI and LCC [8,9], with LAI being defined as the total one-sided foliage area per unit of soil surface area [10]
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