气候变化领域集成服务门户(CCPortal): Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources

CCPortal

DOI	10.1016/j.rse.2020.112101
	Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources
	De Grave C.; Verrelst J.; Morcillo-Pallarés P.; Pipia L.; Rivera-Caicedo J.P.; Amin E.; Belda S.; Moreno J.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	The ESA's forthcoming FLuorescence EXplorer (FLEX) mission is dedicated to the global monitoring of the vegetation's chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. In order to properly interpret the fluorescence signal in relation to photosynthetic activity, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem with Sentinel-3 (S3), which conveys the Ocean and Land Colour Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In this work we present the retrieval models of four essential biophysical variables: (1) Leaf Area Index (LAI), (2) leaf chlorophyll content (Cab), (3) fraction of absorbed photosynthetically active radiation (fAPAR), and (4) fractional vegetation cover (FCover). These variables can be operationally inferred by hybrid retrieval approaches, which combine the generalization capabilities offered by radiative transfer models (RTMs) with the flexibility and computational efficiency of machine learning methods. The RTM SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was used to generate a database of reflectance spectra corresponding to a large variety of canopy realizations, which served subsequently as input to train a Gaussian Process Regression (GPR) algorithm for each targeted variable. Three sets of GPR models were developed, based on different spectral band settings: (1) OLCI (21 bands between 400 and 1040 nm), (2) FLORIS (281 bands between 500 and 780 nm), and (3) their synergy. Their respective performances were assessed based on simulated reflectance scenes. Regarding the retrieval of Cab, the OLCI model gave good model performances (R2: 0.91; RMSE: 7.6 μg. cm−2), yet superior accuracies were achieved as a result of FLORIS' higher spectral resolution (R2: 0.96; RMSE: 4.8 μg. cm−2). The synergy of both datasets did not further enhance the variable retrieval. Regarding LAI, the improvement of the model performances by using only FLORIS spectra (R2: 0.87; RMSE: 1.05 m2.m−2) rather than only OLCI spectra (R2: 0.86; RMSE: 1.12 m2.m−2) was less evident but merging both data sets was more beneficial (R2: 0.88; RMSE: 1.01 m2.m−2). Finally, the three data sources gave good model performances for the retrieval of fAPAR and Fcover, with the best performing model being the Synergy model (fAPAR: R2: 0.99; RMSE: 0.02 and FCover: R2: 0.98; RMSE: 0.04). The ability of the models to process real data was subsequently demonstrated by applying the OLCI models to S3 surface reflectance products acquired over Western Europe and Argentina. Obtained maps showed consistent patterns and variable ranges, and comparison against corresponding Sentinel-2 products (coarsened to a 300 m spatial resolution) led to reasonable matches (R2: 0.5–0.7). Altogether, given the availability of the multiple data sources, the FLEX tandem mission will foster unique opportunities to quantify essential vegetation properties, and hence facilitate the interpretation of the measured fluorescence levels. © 2020 Elsevier Inc.
英文关键词	Biophysical variable; Cab; fAPAR; FCover; FLEX; FLORIS; GPR; LAI; Machine learning; OLCI; Radiative transfer model; SCOPE; Synergy
语种	英语
scopus关键词	Chlorophyll; Computational efficiency; Fluorescence; Image resolution; Radiative transfer; Reflection; Vegetation; Chlorophyll fluorescence; Fractional vegetation cover; Gaussian process regression; Generalization capability; Leaf chlorophyll content; Machine learning methods; Photosynthetically active radiation; Radiative transfer model; Learning systems; accuracy assessment; algorithm; biophysics; Gaussian method; ground penetrating radar; numerical model; quantitative analysis; satellite mission; Sentinel; spectral reflectance; spectrometer; TanDEM-X; Argentina; Western Europe
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179101
作者单位	Image Processing Laboratory (IPL), Parc Científic, Universitat de València, València, Paterna 46980, Spain; CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, Tepic, Nayarit CP. 63155, Mexico
推荐引用方式 GB/T 7714	De Grave C.,Verrelst J.,Morcillo-Pallarés P.,et al. Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources[J],2020,251.
APA	De Grave C..,Verrelst J..,Morcillo-Pallarés P..,Pipia L..,Rivera-Caicedo J.P..,...&Moreno J..(2020).Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources.Remote Sensing of Environment,251.
MLA	De Grave C.,et al."Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources".Remote Sensing of Environment 251(2020).