Accuracy of Satellite Land Surface Reflectance Determination

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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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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An Atmospheric Correction Method for Medium Resolution Spectral Imager Thermal IR Soundings

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.397 ◽

2012 ◽

Vol 500 ◽

pp. 397-402 ◽

Cited By ~ 1

Author(s):

Hai Lei Liu ◽

Li Sheng Xu ◽

Ji Lie Ding ◽

Ba Sang ◽

Xiao Bo Deng

Keyword(s):

Water Vapor ◽

Land Surface ◽

Atmospheric Correction ◽

Correction Method ◽

Qinghai Lake ◽

Medium Resolution ◽

Interpolation Procedure ◽

The Difference ◽

Spectral Imager

Based on the thermal radiative transfer equation (RTE), a new atmospheric correction method named Single Band Water Vapor Dependent (SBWVD) method is developed for land surface temperature (LST) retrieval for the FY-3A Medium Resolution Spectral Imager (MERSI) with only one thermal infrared (TIR) channel. Assuming that the surface emissivity is known, water vapor content (WVC) is the only one parameter for input to the SBWVD algorithm to retrieve LST from MERSI TIR observations. FY-3A MERSI Level 2 water vapor product is employed to evaluate the performance of the proposed method, and a 2-D data interpolation procedure is applied in order to match the MERSI L1B data in spatial resolution. Some tests, including numerical simulation for MERSI sensor and the synchronous measurements of MERSI and the radiosondes for the radiative calibration of the FY-3A tests in Qinghai Lake, have been carried out for the proposed algorithm, respectively. The results show that the difference between the retrieved LST and the in-situ measurements is less than 0.6 K for most situations. The comparison with the MODIS LST products (V5) shows that the root mean square error (RMSE) is under 0.72 K. Thus, our proposed new algorithm is applicable for the atmospheric correction and LST retrieval using MERSI TIR channel observations.

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

Remote Sensing ◽

10.3390/rs11222655 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2655 ◽

Cited By ~ 7

Author(s):

He ◽

Zhang ◽

Liang ◽

Yu ◽

Wang

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Infrared Imaging ◽

Atmospheric Correction ◽

Satellite Observation ◽

Model Parameters ◽

Surface Reflectance ◽

Operational Environment ◽

Mean Square Errors ◽

Directional Reflectance

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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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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The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation

10.5194/tc-2020-270 ◽

2020 ◽

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

Abstract. To evaluate the performance of eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in part 1 of the companion paper, this manuscript applies the XBAER algorithm on the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments onboard 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/aircraft measurements. Cloud screening is performed by standard XBAER algorithm synergistically using OLCI and SLSTR instruments both onboard Sentinel-3. 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 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 given 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 of the retrieved SGS with the in-situ data shows a relative difference between XBAER-derived SGS and SnowEx17 measured SGS of less than 4 %. The difference between XBAER-derived SSA and SnowEx17 measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonable-explained by the SnowEx17 observed snow particle shapes. 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 of the aircraft track of PAMARCMiP campaign. The comparison between XBAER-derived SGS results and 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 reasonable-explained by campaign-based and laboratory investigations, indicating reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities of XBAER-derived SGS and SSA over both Greenland and Arctic-wide agree with the snow metamorphism process.

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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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Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in‐situ measurements

Journal of Geophysical Research Atmospheres ◽

10.1029/2020jd034118 ◽

2021 ◽

Author(s):

Johanna Malle ◽

Nick Rutter ◽

Clare Webster ◽

Giulia Mazzotti ◽

Leanne Wake ◽

...

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Forest Canopy ◽

Canopy Structure ◽

In Situ Measurements

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Water deficit detection in sugarcane using canopy temperature from satellite images

Australian Journal of Crop Science ◽

10.21475/ajcs.20.14.03.p1647 ◽

2020 ◽

pp. 400-407

Author(s):

Rodrigo Moura Pereira ◽

Derblai Casaroli ◽

Lucas Melo Vellame ◽

José Alves Júnior ◽

Adão Wagner Pêgo Evangelista ◽

...

Keyword(s):

Surface Temperature ◽

Water Deficit ◽

Relative Error ◽

Land Surface ◽

Canopy Temperature ◽

Landsat 8 ◽

Temperature Estimation ◽

Calibration Uncertainty ◽

Limited Application ◽

The Difference

Water deficit (WD) is the main yield gap for sugarcane in Midwest Brazil. Thus, WD detection is essential to quantify yield losses, but field detection requires measurement of soil water content over large areas. In this study, we tested leaf temperature (TL) and land surface temperature (TS) to detect WD in a commercial sugarcane area. The area is located in the central region of Goiás State, Brazil. According to Köppen classification, the climate of the region is Aw (humid tropical, with rainy summer and dry winter). The soil is a Ferralsol (clayey texture). TL was measured by a portable infrared thermometer, and TS was obtained using a spectral image from Landsat 8. Both TL and TS measurements occurred between 28 Jan and 24 Aug 2014 (298-506 DAP). The water balance identified periods of water deficit (WD) and surplus (WS). The difference between TL Ta was greater than zero (7.11 °C) in WD periods and lower than zero (-2.18 °C) in WS periods. The difference between TS-Ta, in turn, ranged from -0.66 °C to 4.06 °C, but not following the tendency of WD or WS, which is associated with a relative error between TL and TS near 20% for some date. The TS Ta difference detected soil WD or WS when the relative error was low (362 and 410 DAP) and under higher WD (506 DAP) and WS (394 DAP). This way, TL was able to detect WD and WS along sugarcane growth, while TS showed limited application, requiring improvement based on surface properties to reduce the error in relation to TL. Furthermore, bands 10 and 11 are recommended for surface temperature estimation. Calibration uncertainty increases when the band 11 is used alone, being this band more affected by the absorption of radiation by the atmospheric water vapor, which implies larger errors related to the atmospheric profile in the acquisition of surface temperature.

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