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

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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Operational Validation of Space-based Albedo Products from Upscaled Tower-based Albedometer Measurements

10.5194/egusphere-egu2020-2731 ◽

2020 ◽

Author(s):

Rui Song ◽

Jan-Peter Muller

Keyword(s):

High Resolution ◽

Surface Albedo ◽

Atmospheric Correction ◽

Ground Level ◽

Correction Method ◽

Landsat 8 ◽

Surface Reflectance ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces ◽

Sentinel 2

Surface albedo is a fundamental radiative parameter which controls the Earth&#8217;s energy budget by determining the amount of solar radiation which is either absorbed by the surface or reflected back to atmosphere. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their repeated global coverage. In this work, a new method of upscaling surface albedo from ground level footprints of a few tens of metres to coarse satellite scales (&#8776;1km) is reported [1]. Tower-mounted albedometer measurements are upscaled and used to validate global space-based albedo products, including Copernicus Global Land Service (CGLS) 1km albedo data (from Proba-V and previously form VEGETATION-2), MODerate resolution Imaging Spectroradiometer (MODIS) 500m albedo data, and Multi-angle Imaging SpectroRadiometer (MISR) 1.1km albedo data. MODIS albedo retrievals show the closest agreement with tower measurements, followed by the MISR retrievals, and then followed by the CGLS retrievals. The upscaling method uses high-resolution surface reflectance retrievals (from Landsat-8, Sentinel-2) to fill the spatial gaps between the albedometer&#8217;s field-of-view (FoV) and coarse satellite scales. High-resolution surface albedo products are generated by combining high-resolution surface reflectance data and MODIS bi-directional reflectance distribution function (BRDF) climatology data. This upscaling framework also uses a novel Sensor Invariant Atmospheric Correction (SIAC) method [2] to improve the accuracy of upscaled tower albedo values. Total uncertainties of upscaled albedo products are estimated by considering uncertainties from both the tower albedometer raw measurements and SIAC atmospheric corrections. This surface albedo upscaling method can be used over both heterogenous and homogenous land surfaces, and has been examined at the SURFRAD, BSRN and FLUXNET tower sites.Keywords: surface albedo, upscale, CGLS, MODIS, MISR, SIAC[1] Song, R.; Muller, J.-P.; Kharbouche, S.; Woodgate, W. Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements. Remote Sens. 2019, 11, 644, doi:10.3390/rs11060644.[2] Yin, F., Lewis, P. E., Gomez-Dans, J., & Wu, Q. A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv 2019, https://doi.org/10.31223/osf.io/ps957.

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Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

Remote Sensing ◽

10.3390/rs14010083 ◽

2021 ◽

Vol 14 (1) ◽

pp. 83

Author(s):

Xiaocheng Zhou ◽

Xueping Liu ◽

Xiaoqin Wang ◽

Guojin He ◽

Youshui Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Atmospheric Correction ◽

Solar Spectrum ◽

In Situ Measurements ◽

Landsat 8 ◽

Surface Reflectance ◽

Landsat 8 Oli ◽

Auxiliary Data ◽

Sentinel 2

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

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A Framework of Generating 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data

10.5194/egusphere-egu21-16094 ◽

2021 ◽

Author(s):

Rui Song ◽

Jan-Peter Muller ◽

Alistair Francis

Keyword(s):

Machine Learning ◽

Deep Learning ◽

High Resolution ◽

Surface Albedo ◽

Atmospheric Correction ◽

Cloud Detection ◽

Endmember Extraction ◽

The Earth ◽

Sentinel 2

Abstract: Surface albedo is a fundamental radiative parameter as it controls the Earth&#8217;s surface energy budget and directly affects the Earth&#8217;s climate. A new method is proposed of generating 10-m high-resolution spectral surface albedo from Sentinel-2 L1C top-of-atmosphere (TOA) reflectance and MODIS bi-directional reflectance distribution function (BRDF) data. This high-resolution spectral surface albedo generation system will be described and consists of 5 parts: 1) retrieval of Sentinel-2 spectral surface reflectance using the Sensor Invariant Atmospheric Correction (SIAC) algorithm; 2) generation of Sentinel-2 cloud mask using machine learning; 3) extraction of pure pixels and their corresponding abundance values from 20-m Sentinel-2 data using an Endmember Extraction Algorithm; 4) inversion of high-resolution albedo from MODIS_albedo/Sentinel2_BRF ratio matrix; and 5) downscaling retrieved 20-m spectral and broadband albedo to 10-m. The SIAC algorithm is developed by [1], and has demonstrated to vastly improve the accuracy of Sentinel-2 atmospheric correction when compared against the use of in situ AERONET data. The machine learning cloud detection approach CloudFCN [2] is based on a Fully Convolutional Network architecture, and has become a standard Deep Learning approach to image segmentation. The CloudFCN exhibits state-of-the-art performance in picking up cloud pixels which is comparable to other methods in terms of performance, high speed, and robustness to many different terrains and sensor types. The endmember extraction uses N-FINDR along with Automatic Target Generation Process to identify the pure pixels from Sentinel-2 spectral data. The extracted pure pixels are used to relate the albedo-to-reflectance matrix with the abundance values of different pure pixels. The high-resolution albedo values are finally retrieved by solving this over-parameterised matrix. This framework also produces a MODIS BRDF prior based on 20-years of MCD43A1 and VNP43A1 daily BRDF data. This BRDF prior is produced on a daily basis, and will be used to temporally interpolate the high-resolution albedo values over pixels that are covered by clouds. The produced high-resolution albedo data will be validated over different tower sites where long-time series of in situ albedo products have been produced [3].Keywords: high-resolution, surface albedo, Sentinel-2, SIAC, machine learning, endmember[1] Yin, F.; Lewis, P.E.; Gomez-Dans, J.; Wu, Q. A sensor-invariant atmospheric correction method: Application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv, 21 Feb. 2019 web, doi:10.31223/osf.io/ps957.[2] Francis, A.; Sidiropoulos, P.; Muller, J.-P. CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning. Remote Sens. 2019, 11, 2312. https://doi.org/10.3390/rs11192312.[3] Song, R.; Muller, J.-P.; Kharbouche, S.; Yin, F.; Woodgate, W.; Kitchen, M.; Roland, M.; Arriga, N.; Meyer, W.; Koerber, G.; Bonal, D.; Burban, B.; Knohl, A.; Siebicke, L.; Buysse, P.; Loubet, B.; Leonardo, M.; Lerebourg, C.; Gobron, N. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes. Remote Sens. 2020, 12, 833. https://doi.org/10.3390/rs12050833.

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Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

Remote Sensing ◽

10.3390/rs11151744 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1744 ◽

Cited By ~ 7

Author(s):

Daniel Maciel ◽

Evlyn Novo ◽

Lino Sander de Carvalho ◽

Cláudio Barbosa ◽

Rogério Flores Júnior ◽

...

Keyword(s):

Remote Sensing ◽

Atmospheric Correction ◽

Suspended Sediments ◽

Floodplain Lakes ◽

Empirical Modeling ◽

Landsat 8 ◽

Amazon Floodplain ◽

The Impact ◽

Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

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STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

Remote Sensing ◽

10.3390/rs12193209 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3209

Author(s):

Yunan Luo ◽

Kaiyu Guan ◽

Jian Peng ◽

Sibo Wang ◽

Yizhi Huang

Keyword(s):

Time Series ◽

High Resolution ◽

Satellite Data ◽

Temporal Frequency ◽

Fusion Product ◽

High Temporal Resolution ◽

Surface Reflectance ◽

Land Surfaces ◽

Fine Resolution ◽

Sentinel 2

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product.

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Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

10.5194/essd-2018-144 ◽

2018 ◽

Cited By ~ 1

Author(s):

Simon Gascoin ◽

Manuel Grizonnet ◽

Marine Bouchet ◽

Germain Salgues ◽

Olivier Hagolle

Keyword(s):

High Resolution ◽

Snow Cover ◽

Snow Depth ◽

Western Europe ◽

Landsat 8 ◽

Mountain Regions ◽

Snow Cover Area ◽

Level 2 ◽

Sentinel 2

Abstract. The Theia Snow collection routinely provides high resolution maps of the snow cover area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide including the main mountain regions in Western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Snow collection contains four classes: snow, no-snow, cloud and no-data. We present the algorithm to generate the snow products and provide an evaluation of their accuracy using in situ snow depth measurements, higher resolution snow maps, and visual control. The results suggest that the snow is accurately detected in the Theia snow collection, and that the snow detection is more accurate than the sen2cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed in average 5 days after the image acquisition as raster and vector files via the Theia portal (http://doi.org/10.24400/329360/F7Q52MNK).

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DEVELOPMENT OF SCHEME FOR THE ATMOSPHERIC CORRECTION OF LANDSAT-8 OLI DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-671-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 671-674

Author(s):

V. N. Pathak ◽

M. R. Pandya ◽

D. B. Shah ◽

H. J. Trivedi

Keyword(s):

Atmospheric Correction ◽

Solar Spectrum ◽

Coefficient Of Determination ◽

Radiative Transfer Model ◽

Transfer Model ◽

Landsat 8 ◽

Surface Reflectance ◽

Standard Data ◽

Satellite Signal

Abstract. In the present study, a physics based method called Scheme for Atmospheric Correction of Landsat-8 (SACLS8) is developed for the Operational Land Imager (OLI) sensor of Landsat-8. The Second Simulation of the Satellite Signal in the Solar Spectrum Vector (6SV) radiative transfer model is used in the simulations to obtain the surface reflectance. The surface reflectance derived using the SACL8 scheme is validated with the in-situ measurements of surface reflectance carried out at the homogeneous desert site located in the Little Rann of Kutch, Gujarat, India. The results are also compared with Landsat-8 surface reflectance standard data product over the same site. The good agreement of results with high coefficient of determination (R2>0.94) and low root mean square error (of the order of 0.03) with in-situ measurement values as well as those obtained from the Landsat-8 surface reflectance data establishes a good performance of the SACLS8 scheme for the atmospheric correction of Landsat-8 dataset.

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Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

Earth System Science Data ◽

10.5194/essd-11-493-2019 ◽

2019 ◽

Vol 11 (2) ◽

pp. 493-514 ◽

Cited By ~ 26

Author(s):

Simon Gascoin ◽

Manuel Grizonnet ◽

Marine Bouchet ◽

Germain Salgues ◽

Olivier Hagolle

Keyword(s):

High Resolution ◽

Snow Depth ◽

Western Europe ◽

Landsat 8 ◽

Mountain Regions ◽

Snow Covered Area ◽

Covered Area ◽

Level 2 ◽

Sentinel 2

Abstract. The Theia Snow collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Theia Snow collection contains four classes: snow, no snow, cloud and no data. We present the algorithm to generate the snow products and provide an evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth measurements, higher-resolution snow maps and visual control. The results suggest that the snow is accurately detected in the Theia snow collection and that the snow detection is more accurate than the Sen2Cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed on average 5 d after the image acquisition as raster and vector files via the Theia portal (https://doi.org/10.24400/329360/F7Q52MNK).

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A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data

Remote Sensing ◽

10.3390/rs11111344 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1344 ◽

Cited By ~ 10

Author(s):

Muhammad Bilal ◽

Majid Nazeer ◽

Janet E. Nichol ◽

Max P. Bleiweiss ◽

Zhongfeng Qiu ◽

...

Keyword(s):

Vegetation Index ◽

Estimation Method ◽

Global Scale ◽

Sensor Data ◽

Landsat 8 ◽

Surface Reflectance ◽

Atmospheric Conditions ◽

Land Surfaces ◽

Sentinel 2A

Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices (ASD) from the South Dakota State University (SDSU) site, USA; (ii) cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LaSRC), are conducted over 11 urban (2013–2018), 13 vegetated (2013–2018), and 11 desert/arid (2000 to 2018) sites located over different climatic zones at a global scale; (iii) the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated; (iv) spatio-temporal cross-comparison is conducted for six Landsat paths/rows located in Asia, Africa, Europe, and the United States of America from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions; (v) cross-comparison is also performed for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Soil Adjusted Vegetation Index (SAVI) calculated from both the SREM and Landsat SR data; (vi) the SREM is also applied to the Sentinel-2A and Moderate Resolution Imaging Spectrometer (MODIS) data to explore its applicability; and (vii) errors in the SR retrievals are reported using the mean bias error (MBE), root mean squared deviation (RMSD), and mean systematic error (MSE). Results depict significant and strong positive Pearson’s correlation (r), small MBE, RMSD, and MSE for each spectral band against in situ ASD data and Landsat (LEDAPS and LaSRC) SR products. Consistency in SREM performance against Sentinel-2A (r = 0.994, MBE = −0.009, and RMSD = 0.014) and MODIS (r = 0.925, MBE = 0.007, and RMSD = 0.014) data suggests that SREM can be applied to other multispectral satellites data. Overall, the findings demonstrate the potential and promise of SREM for use over diverse surfaces and under varying atmospheric conditions using multi-sensor data on a global scale.

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Atmospheric Correction of Thermal Infrared Landsat Images Using High-Resolution Vertical Profiles Simulated by WRF Model

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10351 ◽

2021 ◽

Vol 8 (1) ◽

pp. 27

Author(s):

Lucas Ribeiro Diaz ◽

Daniel Caetano Santos ◽

Pâmela Suélen Käfer ◽

Nájila Souza da Rocha ◽

Savannah Tâmara Lemos da Costa ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Single Channel ◽

Atmospheric Correction ◽

Wrf Model ◽

Thermal Infrared ◽

Atmospheric Effects ◽

Landsat 8 ◽

Vertical Profiles

Atmospheric profiles are key inputs in correcting the atmospheric effects of thermal infrared (TIR) remote sensing data for estimating land surface temperature (LST). This study is a first insight into the feasibility of using the weather research and forecasting (WRF) model to provide high-resolution vertical profiles for LST retrieval. WRF numerical simulations were performed to downscaling NCEP climate forecast system version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the use of these profiles in the atmospheric correction of TIR data applying the radiative transfer equation (RTE) inversion single-channel approach. The MODerate resolution atmospheric TRANsmission (MODTRAN) model and Landsat 8 TIRS10 (10.6–11.2 µm) band were taken for the method application. The accuracy evaluation was performed using in situ radiosondes in Southern Brazil. We included in the comparative analysis the atmospheric correction parameter calculator (ACPC; NASA) web tool and profiles directly from the NCEP CFSv2 reanalysis. The atmospheric correction parameters from ACPC, followed by CFSv2, had better agreement with the ones calculated using in situ radiosondes. When applied into the RTE to retrieve LST, the best results (RMSE) were, in descending order: CSFv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). The findings suggest that increasing the horizontal resolution of reanalysis profiles does not particularly improve the accuracy of RTE-based LST retrieval. However, the WRF results are yet satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and also recommend the NCEP CFSv2 reanalysis profiles for TIR remote sensing atmospheric correction and LST single-channel retrieval.

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