Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

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ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-227-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 227-234

Author(s):

J. Susaki ◽

H. Sato ◽

A. Kuriki ◽

K. Kajiwara ◽

Y. Honda

Keyword(s):

Land Surface ◽

Regression Models ◽

Surface Albedo ◽

Model Parameters ◽

Climate Change Research ◽

Barren Land ◽

Multi Temporal ◽

Angular Reflectance ◽

Observation Geometry ◽

Mean Square Errors

Abstract. This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.

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Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

Remote Sensing ◽

10.3390/rs12152500 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2500

Author(s):

Kyeong-Sang Lee ◽

Sung-Rae Chung ◽

Changsuk Lee ◽

Minji Seo ◽

Sungwon Choi ◽

...

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Monsoon Season ◽

Atmospheric Correction ◽

Optimization Method ◽

Retrieval Algorithm ◽

High Temporal Resolution ◽

Temporal Consistency ◽

Moderate Resolution Imaging Spectroradiometer ◽

Mean Square Errors

The Korea Meteorological Administration successfully launched Korea’s next-generation meteorological satellite, Geo-KOMPSAT-2A (GK-2A), on 5 December 2018. It belongs to the new generation of GEO (Geostationary Elevation Orbit) satellite which offers capabilities to disseminate high spatial- (0.5–2 km) and high temporal-resolution (10 min) observations over a broad area, herein a geographic disk encompassing the Asia–Oceania region. The targeted objective is to enhance our understanding of climate change, owing to a bulk of coherent observations. For such, we developed an algorithm to map the land surface albedo (LSA), which is a major Essential Climate Variable (ECV). The retrieval algorithm devoted to GK-2A/Advanced Meteorological Imager (AMI) data considered Japan’s Himawari-8/Advanced Himawari Imager (AHI) data for prototyping, as this latter owns similar specifications to AMI. Our proposed algorithm is decomposed in three major steps: atmospheric correction, bidirectional reflectance distribution function (BRDF) modeling and angular integration, and narrow-to-broadband conversion. To perform BRDF modeling, the optimization method using normalized reflectance was applied, which improved the quality of BRDF modeling results, particularly when the number of observations was less than 15. A quality assessment was performed to compare our results to those of Moderate Resolution Imaging Spectroradiometer (MODIS) LSA products and ground measurement from Aerosol Robotic Network (AERONET) sites, Australian and New Zealand flux tower network (OzFlux) site and the Korea Flux Network (KoFlux) site from throughout 2017. Our results show dependable spatial and temporal consistency with MODIS broadband LSA data, and rapid changes in LSA due to snowfall and snow melting were well expressed in the temporal profile of our results. Our outcomes also show good agreement with the ground measurements from AERONET, OzFlux and KoFlux ground-based network with root mean square errors (RMSE) of 0.0223 and 0.0306, respectively, which is close to the accuracy of MODIS broadband LSA. Moreover, our results reveal still more reliable LSA products even when clouds are frequently present, such as during the summer monsoon season. It shows that our results are useful for continuous LSA monitoring.

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Determination of land surface reflectance using the AATSR dual-view capability

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-7451-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 7451-7494

Author(s):

L. Sogacheva ◽

P. Kolmonen ◽

T. H. Virtanen ◽

E. Rodriguez ◽

A.-M. Sundström ◽

...

Keyword(s):

Land Surface ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

Absolute Error ◽

Surface Reflectance ◽

Imaging Spectrometer ◽

Comparison Results ◽

Moderate Resolution ◽

The Difference ◽

Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995–2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite.

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Accuracy of Satellite Land Surface Reflectance Determination

Journal of Applied Meteorology ◽

10.1175/1520-0450-30.7.960 ◽

1991 ◽

Vol 30 (7) ◽

pp. 960-972 ◽

Cited By ~ 9

Author(s):

O. Arino ◽

G. Dedieu ◽

P. Y. Deschamps

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Maximum Error ◽

Surface Reflectance ◽

Relative Uncertainty ◽

Calibration Uncertainty ◽

Maximum Bias ◽

Calibration Accuracy ◽

The Difference

Abstract An accuracy budget of the surface reflectance determination from Meteosat geostationary satellite data is performed. Error analysis allows identification of three main problems: calibration uncertainty of the Meteosat instrument, atmospheric corrections, and surface effects (spectral and directional). Calibration accuracy is 10%, leading to a 10% relative uncertainty on reflectance. Spectral effects of the surface lead to a maximum bias of 0.01 for a vegetated surface as sensed by Meteosat, while directional effects can lead to a bias of 0.035 between two measurements taken at two different sun zenith and azimuth angles at the same view angle over savannas. The maximum error due to the atmosphere is estimated to be of the order of 0.03 in reflectance for a surface reflectance of 0.40 and 0.01 for, a surface reflectance of 0.10. Validation with in situ measurement is within the expected error over savanna. But the difference is still high over the southwest France site of HAPEX-MOBILHY, certainly due to the joint spectral and directional errors. Comparisons with surface albedo maps from literature show the same spatial and spatial evolutions with a better spatial and temporal determination in our results.

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The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

The Cryosphere ◽

10.5194/tc-15-2781-2021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 2781-2802

Author(s):

Linlu Mei ◽

Vladimir Rozanov ◽

Evelyn Jäkel ◽

Xiao Cheng ◽

Marco Vountas ◽

...

Keyword(s):

Grain Size ◽

Land Surface ◽

Model Simulation ◽

Atmospheric Correction ◽

Aircraft Measurements ◽

Airborne Measurements ◽

Snow Properties ◽

Snow Particle ◽

The Difference ◽

Mean Square Errors

Abstract. To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.

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First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

Remote Sensing ◽

10.3390/rs11242990 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2990 ◽

Cited By ~ 6

Author(s):

Shuang Li ◽

Weile Wang ◽

Hirofumi Hashimoto ◽

Jun Xiong ◽

Thomas Vandal ◽

...

Keyword(s):

Land Surface ◽

High Frequency ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

Product Evaluation ◽

Low Earth Orbit ◽

Geostationary Satellite ◽

Surface Reflectance ◽

High Consistency ◽

New Generation

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47µm and 0.51µm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET.

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Determination of land surface reflectance using the AATSR dual-view capability

Atmospheric Measurement Techniques ◽

10.5194/amt-8-891-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 891-906 ◽

Cited By ~ 6

Author(s):

L. Sogacheva ◽

P. Kolmonen ◽

T. H. Virtanen ◽

E. Rodriguez ◽

A.-M. Sundström ◽

...

Keyword(s):

Land Surface ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

Absolute Error ◽

Surface Reflectance ◽

Imaging Spectrometer ◽

Comparison Results ◽

Moderate Resolution ◽

The Difference ◽

Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using the radiances measured at the top of the atmosphere for the two views provided by the Advanced Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual-view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated versus surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (Moderate Resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with somewhat higher values (up to 0.05) obtained by ADV over bright surfaces. The difference between the ADV- and MODIS-retrieved surface reflectances is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation in the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for both the AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover a 17-year period of measurements (1995–2012), as well as a prototype for the Sea and Land Surface Temperature Radiometer (SLSTR) planned to be launched in the fall of 2015 onboard the Sentinel-3 satellite.

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Estimation of Land Surface Incident and Net Shortwave Radiation from Visible Infrared Imaging Radiometer Suite (VIIRS) Using an Optimization Method

Remote Sensing ◽

10.3390/rs12244153 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4153

Author(s):

Yi Zhang ◽

Shunlin Liang ◽

Tao He ◽

Dongdong Wang ◽

Yunyue Yu

Keyword(s):

Land Surface ◽

Infrared Imaging ◽

Optimization Method ◽

Surface Radiation ◽

Radiation Budget ◽

Shortwave Radiation ◽

Modis Data ◽

Surface Radiation Budget ◽

Mean Square Errors ◽

Key Parameter

Incident surface shortwave radiation (ISR) is a key parameter in Earth’s surface radiation budget. Many reanalysis and satellite-based ISR products have been developed, but they often have insufficient accuracy and resolution for many applications. In this study, we extended our optimization method developed earlier for the MODIS data with several major improvements for estimating instantaneous and daily ISR and net shortwave radiation (NSR) from Visible Infrared Imaging Radiometer Suite observations (VIIRS), including (1) an integrated framework that combines look-up table and parameter optimization; (2) enabling the calculation of net shortwave radiation (NSR) as well as daily values; and (3) extensive global validation. We validated the estimated ISR values using measurements at seven Surface Radiation Budget Network (SURFRAD) sites and 33 Baseline Surface Radiation Network (BSRN) sites during 2013. The root mean square errors (RMSE) over SURFRAD sites for instantaneous ISR and NSR were 83.76 W/m2 and 66.80 W/m2, respectively. The corresponding daily RMSE values were 27.78 W/m2 and 23.51 W/m2. The RMSE at BSRN sites was 105.87 W/m2 for instantaneous ISR and 32.76 W/m2 for daily ISR. The accuracy is similar to the estimation from MODIS data at SURFRAD sites but the computational efficiency has improved by approximately 50%. We also produced global maps that demonstrate the potential of this algorithms to generate global ISR and NSR products from the VIIRS data.

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Global-Scale Comparison of MISR and MODIS Land Surface Albedos

Journal of Climate ◽

10.1175/2010jcli3709.1 ◽

2011 ◽

Vol 24 (3) ◽

pp. 732-749 ◽

Cited By ~ 31

Author(s):

Bernard Pinty ◽

Malcolm Taberner ◽

Vance R. Haemmerle ◽

Susan R. Paradise ◽

Eric Vermote ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Robust Statistics ◽

Atmospheric Correction ◽

Global Scale ◽

Model Parameters ◽

Bidirectional Reflectance Factor ◽

Moderate Resolution Imaging Spectroradiometer ◽

Highly Correlated ◽

Mean Differences

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky surface albedos are compared with similar products generated on the basis of the Multiangle Imaging SpectroRadiometer (MISR) surface bidirectional reflectance factor (BRF) model parameters available for the year 2005. The analysis is achieved using global-scale statistics to characterize the broad patterns of these two independent albedo datasets. The results obtained in M. Taberner et al. have shown that robust statistics can be established and that both datasets are highly correlated. As a result, the slight but consistent biases and trends identified in this paper, derived from statistics obtained on a global basis, should be considered sufficiently reliable to merit further investigation. The present paper reports on the zonal- and seasonal-mean differences retrieved from the analysis of the MODIS and MISR surface albedo broadband products. The MISR − MODIS differences exhibit a systematic positive bias or offset in the range of 0.01–0.03 depending on the spectral domain of interest. Results obtained in the visible domain exhibit a well-marked and very consistent meridional trend featuring a “smile effect” such that the MISR − MODIS differences reach maxima at the highest latitudes in both hemispheres. The analysis of seasonal variations observed in MISR and MODIS albedo products reveals that, in the visible domain, the MODIS albedos generate weaker seasonal changes than MISR and that the differences increase poleward from the equatorial regions. A detailed investigation of MODIS and MISR aerosol optical depth retrievals suggests that this large-scale meridional trend is probably not caused by differences in the aerosol load estimated by each instrument. The scale and regularity of the meridional trend suggests that this may be due to the particular sampling regime of each instrument in the viewing azimuthal planes and/or approximations in the atmospheric correction processes. If this is the case, then either MODIS is underestimating, or MISR overestimating, the surface anisotropy or both.

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Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes

Remote Sensing ◽

10.3390/rs12050833 ◽

2020 ◽

Vol 12 (5) ◽

pp. 833

Author(s):

Rui Song ◽

Jan-Peter Muller ◽

Said Kharbouche ◽

Feng Yin ◽

William Woodgate ◽

...

Keyword(s):

High Resolution ◽

Surface Albedo ◽

Spatial Scales ◽

Atmospheric Correction ◽

Landsat 8 ◽

Surface Reflectance ◽

Hemispherical Reflectance ◽

Land Surfaces ◽

Sentinel 2

Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).

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