scholarly journals Evaluation of RadCalNet Output Data Using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors

2019 ◽  
Vol 11 (5) ◽  
pp. 541 ◽  
Author(s):  
Xin Jing ◽  
Larry Leigh ◽  
Cibele Teixeira Pinto ◽  
Dennis Helder
Keyword(s):  
Optical Sensors ◽  
Sensor Data ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Viewing Angle ◽  
Landsat 7 ◽  
Angle Effect ◽  
Mean Differences ◽  
Sentinel 2A ◽  
Reflectance Data

In 2013, the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Infrared and Visible Optical Sensors Subgroup (IVOS) established the Radiometric Calibration Network (RadCalNet), consisting of four international test sites providing automated in situ measurements and estimates of propagated top-of-atmosphere (TOA) reflectance. This work evaluates the ‘reliability’ of RadCalNet TOA reflectance data at three of these sites—RVUS, LCFR, and GONA—using Landsat 7 ETM+, Landsat 8 operational land imager (OLI), and Sentinel 2A/2B (S2A/S2B) MSI TOA reflectance data. This work identified a viewing angle effect in the MSI data at the RVUS and LCFR sites; when corrected, the overall standard deviation in relative reflectance differences decreased by approximately 2% and 0.5% at the RVUS and LCFR sites, respectively. Overall, the relative mean differences between the RadCalNet surface data and sensor data for the RVUS and GONA sites are within 5% for ETM+, OLI, and S2A MSI, with an approximately 2% higher difference in the S2B MSI data at the RVUS site. The LCFR site is different from the other two sites, with relative mean differences ranging from approximately -10% to 1%, even after performing the viewing angle effect correction on the MSI data. The data from RadCalNet are easy to acquire and use. More effort is needed to better understand the behavior at LCFR. One significant improvement on the accuracy of the RadCalNet data might be the development of a site-specific BRDF characterization and correction.

scholarly journals Harmonizing Surface Reflectance Between Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI

2021 ◽  
Author(s):  
Hongye Cao ◽  
Ling Han ◽  
Liangzhi Li
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Landsat 7 ◽  
Sentinel 2A ◽  
Reflectance Data ◽  
Sentinel 2

Abstract Remote sensing dynamic monitoring methods often benefit from a dense time series of observations. To enhance these time series, it is sometimes necessary to integrate data from multiple satellite systems. For more than 40 years, Landsat has provided the longest time record of space-based land surface observations, and the successful launch of the Landsat-8 Operational Land Imager (OLI) sensor in 2013 continues this tradition. However, the 16-day observation period of Landsat images has challenged the ability to measure subtle and transient changes like never before. The European Space Agency (ESA) launched the Sentinel-2A satellite in 2015. The satellite carries a Multispectral Instrument (MSI) sensor that provides a 10-20m spatial resolution data source providing an opportunity to complement the Landsat data record. The collection of Sentinel-2A MSI, Landsat-7 ETM+, and Landsat-8 OLI data provide multispectral global coverage from 10m to 30m with further reduced data revisit intervals. There are many differences between sensor data that need to be taken into account to use these data together reliably. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8 and Sentinel-2 archived in the Google Earth Engine (GEE) cloud platform. To test and quantify the differences between these sensors, hundreds of thousands of surface reflectance data from sensor pairs were collected over China. In this study, some differences in the surface reflectance of the sensor pairs were identified, based upon which a cross-sensor conversion model was proposed, i.e., a suitable adjustment equation was fitted using an ordinary least squares (OLS) linear regression method to convert the Sentinel-2 reflectance values closer to the Landsat-7 or Landsat-8 values. The regression results show that the Sentinel MSI data are spectrally comparable to both types of Landsat image data, just as the Landsat sensors are comparable to each other. The root mean square error (RMSE) values between MSI and Landsat spectral values before coordinating the sensors ranged from 0.014 to 0.037, and the RMSE values between OLI and ETM + ranged from 0.019 to 0.039. After coordination, RMSE values between MSI and Landsat spectral values ranged from 0.011 to 0.026, and RMSD values between OLI and ETM + ranged from 0.013 to 0.034. The fitted adjustment equations were also compared to the HLS (Harmonized Landsat-8 Sentinel-2) global fitted equations (Sentinel-2 to Landsat-8) published by the National Aeronautics and Space Administration (NASA) and were found to be significantly different, increasing the likelihood that such adjustments would need to be fitted on a regional basis. This study believes that despite the differences in these datasets, it appears feasible to integrate these datasets by applying a linear regression correction between the bands.

scholarly journals Riesgo en estructuras hidráulicas asociado al cambio de uso del suelo en cuencas hidrográficas

2018 ◽  
Vol 26 (1) ◽  
pp. 157-162
Author(s):  
Edmundo Canchari Gutiérrez
Keyword(s):  
Landsat 8 ◽  
Landsat 7 ◽  
Sentinel 2A

La finalidad del trabajo es determinar el riesgo de las estructuras hidráulicas asociado al cambio del uso de suelo en cuencas hidrográficas, para la evaluación del cambio de uso del suelo y la variación en el tiempo se obtiene en base al registro disponibles de los proyectos LANDSAT 5, LANDSAT 7 y LANDSAT 8, además del proyecto SENTINEL 2A; como fundamento teórico se trata la teledetección, índice de vegetación de diferencia normalizada, transformación de la precipitación en escorrentía, riesgo, vulnerabilidad y resiliencia. El índice de vegetación de diferencia normalizada se asocia al cambio de uso del suelo y éste con la capacidad de abstracción de la precipitación, obteniendo así los caudales de máxima avenida para los periodos analizados.

scholarly journals Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using In Situ Measurements

2020 ◽  
Vol 12 (16) ◽  
pp. 2597
Author(s):  
Cibele Teixeira Pinto ◽  
Xin Jing ◽  
Larry Leigh
Keyword(s):  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Spectral Bands ◽  
Data Products ◽  
Landsat 7 ◽  
Level 1 ◽  
Level 2 ◽  
Reflectance Data

Landsat Level-1 products are delivered as quantized and calibrated scaled Digital Numbers (DN). The Level-1 DN data can be rescaled to Top-of-Atmosphere (TOA) reflectance applying radiometric rescaling coefficients. Currently, the Level-1 product is the standard data product of the Landsat sensors. The more recent Level-2 data products contain surface reflectance values, i.e., reflectance as it would be measured at ground level in the absence of atmospheric effects; in the near future, these products are anticipated to become the standard products of the Landsat sensors. The purpose of this paper is to present a radiometric performance evaluation of Level-1 and Level-2 data products for the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. TOA reflectance and derived surface reflectance values from both data products were evaluated and compared to in situ measurements from eight test sites located in Turkey, Brazil, Chile, the United States, France, and Namibia. The results indicate an agreement between the ETM+ and OLI Level-1 TOA reflectance data and the in situ measurements of 3.9% to 6.5% and 3.9% to 6.0%, respectively, across all spectral bands. Agreement between the in situ measurements and both Level-2 surface reflectance data products were consistently decreased in the shorter wavelength bands, and slightly better in the longer wavelength bands. The mean percent absolute error for Level-2 surface reflectance data ranged from 3.3% to 10% for both Landsat sensors. The significant decay in agreement with the data collected in situ in the short wavelength spectral bands with Level-2 data suggests issues with retrieval of aerosol concentration at some sites. In contrast, the results indicate a reasonably accurate estimate of water vapor in the mid-infrared spectrum. Lastly, despite the less reliable performance of Level-2 data product in the visible spectral region (compared with Level-1 data) in both sensors, the Landsat-8 OLI Level-2 showed an improvement of surface reflectance product over all spectral bands in common with the Landsat-7 ETM+ Level-2 data.

A fast radiometric correction method for Sentinel-2 satellite images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Elahe Moradi ◽  
Alireza Sharifi
Keyword(s):  
Polynomial Equation ◽  
Correction Method ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Radiometric Correction ◽  
Content Type ◽  
Spectral Bands ◽  
Corrected Data ◽  
Sentinel 2A

Purpose Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration. Design/methodology/approach The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation. Findings This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable. Originality/value For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

scholarly journals Utilisation De L’image Multispectrale Pour L’exploration Et La Recherche Des Ressources Minérales : État Des Connaissances Et Proposition D’un Modèle De Traitement

2018 ◽  
Vol 14 (24) ◽  
pp. 350
Author(s):  
Abdessamad El Atillah ◽  
Zine El Abidine El Morjani ◽  
Mustapha Souhassou
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Geological Mapping ◽  
Landsat 8 ◽  
Geological Maps ◽  
Landsat 7 ◽  
Sentinel 2A ◽  
Terrestrial Globe ◽  
The Cost ◽  
Methods Of Treatment

Multiband space remote sensing is an indirect tool for prospecting the Earth's surface. It is very powerful especially in its applications related to the field of geology including geological mapping, mining and oil exploration. It can also significantly reduce the cost of exploration, reach inaccessible areas, guide mining research to favorable regions and reach a large surface. In this article, we highlight in details the state of knowledge in this field of research by citing the different methods and approaches carried out by several specialists who generally define the use of remote sensing for lithostructural and mineralogical mapping and particularly for the exploration and research of mineral substances. We also create methods derived from the aforementioned methods of treatment by means of a logical analogy between the different bands of several satellites of observation of the terrestrial globe, particularly between : Landsat 7 ETM +; Landsat 8 OLI / TIRS; Aster and Sentinel 2A. At the end, we synthesize these results by proposing a multispectral image-processing model that can be applied directly. This model starts with the calculation of Optimum Index Factor (OIF), which allows us to detect only the most important colored composites; and the reports of the bands, rations, the principal component analysis, ACI and the classification that allow the realization of a lithological and mineralogical mapping as well as maps of lineaments by means of directional filters. The validity of the models is tested by comparison with field data and geological maps of the studied site.

scholarly journals Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

2019 ◽  
Vol 11 (9) ◽  
pp. 1105 ◽  
Author(s):  
Bipin Raut ◽  
Morakot Kaewmanee ◽  
Amit Angal ◽  
Xiaoxiong Xiong ◽  
Dennis Helder
Keyword(s):  
Region Of Interest ◽  
Image Data ◽  
Sensor Calibration ◽  
Absolute Calibration ◽  
Landsat 8 ◽  
Calibration Model ◽  
General Applicability ◽  
Landsat 7 ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sentinel 2A

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

scholarly journals Absolute Radiometric Calibration of KOMPSAT-3A

2016 ◽  
Vol XLI-B1 ◽  
pp. 271-274
Author(s):  
H. Y. Ahn ◽  
D. Y. Shin ◽  
J. S. Kim ◽  
D. C. Seo ◽  
C. U. Choi
Keyword(s):  
South Korea ◽  
Spectral Band ◽  
Absolute Calibration ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
National University ◽  
Band Pass ◽  
The Difference ◽  
Reflectance Data ◽  
Calibration Coefficients

This paper presents a vicarious radiometric calibration of the Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) performed by the Korea Aerospace Research Institute (KARI) and the Pukyong National University Remote Sensing Group (PKNU RSG) in 2015.The primary stages of this study are summarized as follows: (1) A field campaign to determine radiometric calibrated target fields was undertaken in Mongolia and South Korea. Surface reflectance data obtained in the campaign were input to a radiative transfer code that predicted at-sensor radiance. Through this process, equations and parameters were derived for the KOMPSAT-3A sensor to enable the conversion of calibrated DN to physical units, such as at-sensor radiance or TOA reflectance. (2) To validate the absolute calibration coefficients for the KOMPSAT-3A sensor, we performed a radiometric validation with a comparison of KOMPSAT-3A and Landsat-8 TOA reflectance using one of the six PICS (Libya 4). Correlations between top-of-atmosphere (TOA) radiances and the spectral band responses of the KOMPSAT-3A sensors at the Zuunmod, Mongolia and Goheung, South Korea sites were significant for multispectral bands. The average difference in TOA reflectance between KOMPSAT-3A and Landsat-8 image over the Libya 4, Libya site in the red-green-blue (RGB) region was under 3%, whereas in the NIR band, the TOA reflectance of KOMPSAT-3A was lower than the that of Landsat-8 due to the difference in the band passes of two sensors. The KOMPSAT-3Aensor includes a band pass near 940 nm that can be strongly absorbed by water vapor and therefore displayed low reflectance. Toovercome this, we need to undertake a detailed analysis using rescale methods, such as the spectral bandwidth adjustment factor.

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

2019 ◽  
Vol 11 (11) ◽  
pp. 1344 ◽  
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.

scholarly journals Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI

2020 ◽  
Vol 12 (6) ◽  
pp. 915 ◽  
Author(s):  
Benjamin Brede ◽  
Jochem Verrelst ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
Jan G. P. W. Clevers ◽  
Leo Goudzwaard ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Performance ◽  
Noise Model ◽  
Landsat 8 ◽  
Area Index ◽  
Landsat 7 ◽  
Sentinel 2A ◽  
The Impact ◽  
Exact Design ◽  
Two Alternatives

The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The features were varied in a full grid resulting in 960 inversion models in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction Root Mean Square Error (RMSE) by 1.08 m2 m−2 when 5% noise was added compared to inversions with 0% absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52 m2 m−2 between the best and worst model. The best inversion model achieved an RMSE of 0.91 m2 m−2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows.

Sign in / Sign up

Export Citation Format

Share Document