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Article

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

1
Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain
2
Institut Cartografic i Geologic de Catalunya (ICGC), Parc de Montjüic, 08038 Barcelona, Spain
3
Department of Applied Mathematics, University of Alicante, 03690 Alicante, Spain
4
Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
5
Secretary of Research and Graduate Studies, CONACYT-UAN, Tepic 63155, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Arturo Sanchez-Azofeifa
Remote Sens. 2022, 14(6), 1347; https://doi.org/10.3390/rs14061347
Received: 14 January 2022 / Revised: 21 February 2022 / Accepted: 26 February 2022 / Published: 10 March 2022
(This article belongs to the Special Issue New Advancements in the Field of Forest Remote Sensing)
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community. View Full-Text
Keywords: vegetation traits; Sentinel-3; TOA radiance; OLCI; Gaussian process regression; machine learning; hybrid method; time series; Google Earth Engine vegetation traits; Sentinel-3; TOA radiance; OLCI; Gaussian process regression; machine learning; hybrid method; time series; Google Earth Engine
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MDPI and ACS Style

Reyes-Muñoz, P.; Pipia, L.; Salinero-Delgado, M.; Belda, S.; Berger, K.; Estévez, J.; Morata, M.; Rivera-Caicedo, J.P.; Verrelst, J. Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine. Remote Sens. 2022, 14, 1347. https://doi.org/10.3390/rs14061347

AMA Style

Reyes-Muñoz P, Pipia L, Salinero-Delgado M, Belda S, Berger K, Estévez J, Morata M, Rivera-Caicedo JP, Verrelst J. Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine. Remote Sensing. 2022; 14(6):1347. https://doi.org/10.3390/rs14061347

Chicago/Turabian Style

Reyes-Muñoz, Pablo, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan P. Rivera-Caicedo, and Jochem Verrelst. 2022. "Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine" Remote Sensing 14, no. 6: 1347. https://doi.org/10.3390/rs14061347

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