Improved retrieval of land surface biophysical variables from time series of Sentinel-3 OLCI TOA spectral observations by considering the temporal autocorrelation of surface and atmospheric properties

Yang, Peiqi; Verhoef, W.; Prikaziuk, Egor; Tol, Christiaan van der

doi:10.1016/j.rse.2021.112328

Cited by 17 publications

(11 citation statements)

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“…Moreover, the spectral signal is affected in a much smaller magnitude by most atmospheric variables compared to the canopy or leaf-level variables, which have a dominant impact in particular in the visible and shortwave infrared regions ( Verrelst et al, 2019b ). While some studies have demonstrated the coupling with advanced atmospheric RTMs such as MODTRAN (e.g., Bayat et al, 2020 ; Yang et al, 2020 , 2021 ) for TOA-based retrieval approaches, equally consistent retrieval results can be obtained with simplified atmospheric RTMs such as Second Simulation of a Satellite Signal in the Solar Spectrum (6SV, Vermote et al, 1997 ) and the assumption of a Lambertian surface ( Verrelst et al, 2019b ). Estévez et al (2020) developed a hybrid retrieval workflow by combining leaf-canopy-atmosphere RTMs to retrieve leaf area index (LAI) from BOA and TOA S2 reflectance data.…”

Section: Introductionmentioning

confidence: 98%

“…On the other hand, avoiding atmospheric correction at all would introduce even larger uncertainty. To circumvent both sources of uncertainty, the alternative approach is to upscale training data simulations from canopy to atmosphere levels and derive the vegetation variables directly from TOA reflectance or radiance ( Fang and Liang, 2003 ; Lauvernet et al, 2008 ; Laurent et al, 2011b , 2011a , 2013 , 2014 ; Mousivand et al, 2015 ; Shi et al, 2016 , 2017 ; Verrelst et al, 2019b ; Bayat et al, 2020 ; Yang et al, 2021 ; Estévezet al, 2021 ). TOA retrieval methods usually rely on the coupling of a vegetation RTM with an atmosphere RTM ( Bayat et al, 2020 ; Mousivand et al, 2015 ; Verrelst et al, 2019b ; Estévez et al, 2021 ), with the latter explicitly modeling the atmospheric effects on the radiance received by the sensor.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Estévez

Salinero-Delgado

Berger

et al. 2022

Remote Sensing of Environment

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Estévez

Salinero-Delgado

Berger

et al. 2022

Remote Sensing of Environment

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“…Hence, the trained models better approximate homogeneous vegetation stands, such as crops or grassland, than heterogeneous, such as forests. Nonetheless, the influence of complex 3D canopy structures becomes less evident at the spatial resolution of S3 with 300 m [ 94 , 95 ] compared to higher spatial resolution products. Comparing our LAI and FAPAR retrievals with the MODIS MCD15A3H products, which are inferred from a 3D RTM, we even found higher consistency over (broad-leaved) forests than crops (i.e., non-irrigated arable land).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…Rather short (30 days) time series of level-1 data were used for burned area estimation with OLCI-derived NDVI [41]. Yang et al [11] made a time-series LAI retrieval algorithm from TOA OLCI data. Single-time applications of single-image OLCI data were used for biophysical properties retrieval [42] (Synergy product) and for demonstration of radiance calibration network [43].…”

Section: Time Series Applications For Landmentioning

confidence: 99%

“…Our team has explored the theoretical applications of the recent Sentinel-3 [7] products for land surface monitoring using model simulations [8][9][10]. However, at the following proof-of-concept step using the "real-world" data [11], we faced the bottlenecks mentioned above-the preparation of a time-series dataset takes several days. Fortunately, in October 2017, GEE introduced a new Image Collection of Sentinel-3 level-1 Ocean and Land Color Instrument (OLCI) products.…”

Section: Introductionmentioning

confidence: 99%

Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

2021

Self Cite

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In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata.

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Improved retrieval of land surface biophysical variables from time series of Sentinel-3 OLCI TOA spectral observations by considering the temporal autocorrelation of surface and atmospheric properties

Cited by 17 publications

References 65 publications

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

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

Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

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