2020
DOI: 10.1016/j.isprsjprs.2020.07.004
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Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data

Abstract: Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybri… Show more

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Cited by 55 publications
(49 citation statements)
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References 81 publications
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“…In the last five decades, numerous retrieval methods have been proposed and developed to predict biophysical and biochemical vegetation traits from EO data, ranging from parametric and nonparametric regressions to physically-based and hybrid approaches [5][6][7]. Since these studies provide exhaustive and up-to-date taxonomies of quantitative retrieval methods, we will concentrate here on the recently promoted hybrid retrieval workflows [8][9][10][11][12]. Hybrid retrieval strategies denominate a combination of radiative transfer models (RTM), providing physical constraints and domain knowledge [13], with fast and flexible machine learning (ML) regression algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…In the last five decades, numerous retrieval methods have been proposed and developed to predict biophysical and biochemical vegetation traits from EO data, ranging from parametric and nonparametric regressions to physically-based and hybrid approaches [5][6][7]. Since these studies provide exhaustive and up-to-date taxonomies of quantitative retrieval methods, we will concentrate here on the recently promoted hybrid retrieval workflows [8][9][10][11][12]. Hybrid retrieval strategies denominate a combination of radiative transfer models (RTM), providing physical constraints and domain knowledge [13], with fast and flexible machine learning (ML) regression algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…GEE became a popular platform in facilitating research activities in the field of vegetation traits mapping and monitoring. At the same time, despite an increasing number of studies exploring GPR models in the remote sensing domain [31,49,59,[75][76][77], the integration of GPR in GEE to produce spatiotemporal information of vegetation still remained to be explored. Therefore, in this study we presented a workflow for monitoring vegetation traits at continental scale by taking advantage of the TOA S3-OLCI collection available in the GEE cloud platform.…”
Section: Discussionmentioning
confidence: 99%
“…The basic parametrization used for 6SV (v2.1) in this work is detailed in Table 2 . The variable ranges of the atmospheric models were chosen according to prior studies [ 48 , 49 ] and should reflect generic and globally valid ranges [ 54 ]. The SCOPE simulated training data set was then mixed with additional 1000 samples of spectra extracted from real S3 scenes.…”
Section: Methodsmentioning
confidence: 99%
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“… 31 33 ]. While 30 m Landsat and 10 m Sentinel-2 based LAI products 34 – 36 can be produced routinely, neither of these products provide the combination of spatial and temporal resolution required for precision agricultural applications. Using a non-continuous time-series of CubeSat-derived LAI, Houborg and McCabe 11 demonstrated the spatiotemporal advantages in monitoring alfalfa when compared to 30 m resolution Landsat-8 data.…”
Section: Introductionmentioning
confidence: 99%