scholarly journals Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

2017 ◽  
Vol 9 (12) ◽  
pp. 1319 ◽  
Author(s):  
Joan-Cristian Padró ◽  
Xavier Pons ◽  
David Aragonés ◽  
Ricardo Díaz-Delgado ◽  
Diego García ◽  
...  
Keyword(s):  
Time Series ◽  
Landsat 8 ◽  
Radiometric Correction ◽  
Landsat 7 ◽  
Sentinel 2A

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 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.

scholarly journals Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

2018 ◽  
Vol 10 (11) ◽  
pp. 1751 ◽  
Author(s):  
Abderrahim Nemmaoui ◽  
Manuel A. Aguilar ◽  
Fernando J. Aguilar ◽  
Antonio Novelli ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Free Access ◽  
Landsat 8 ◽  
Horticultural Crops ◽  
Command Line Tool ◽  
Multi Temporal ◽  
Tree Models ◽  
Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

scholarly journals Using SAR satellite data time-series for regional glacier mapping

2017 ◽  
Author(s):  
Solveig H. Winsvold ◽  
Andreas Kääb ◽  
Christopher Nuth ◽  
Liss M. Andreassen ◽  
Ward van Pelt ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Time Series Data ◽  
Series Representation ◽  
Series Data ◽  
Landsat 8 ◽  
Change Analysis ◽  
Surface Mass ◽  
Sentinel 2A ◽  
Optical Satellite Images

Abstract. With dense SAR satellite data time-series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and Radarsat-2 backscatter images over mainland Norway and Svalbard, we have used descriptive methods for outlining the possibilities of using SAR time-series for mapping glaciers. We present five application scenarios, where the first shows potential for tracking transient snow lines with SAR backscatter time-series, and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time-series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time-series together with a coupled energy balance and multi-layer firn model. We find strong correlation between backscatter signals with both the modeled firn air-content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time-series, revealing important information about the area extent of internal accumulation. Finally, in the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time-series data. Our results reveal the potential of satellite imagery for automatically derived products as important input in modeling assessments and glacier change analysis.

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 Using SAR satellite data time series for regional glacier mapping

2018 ◽  
Vol 12 (3) ◽  
pp. 867-890 ◽  
Author(s):  
Solveig H. Winsvold ◽  
Andreas Kääb ◽  
Christopher Nuth ◽  
Liss M. Andreassen ◽  
Ward J. J. van Pelt ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Time Series Data ◽  
Series Representation ◽  
Series Data ◽  
Landsat 8 ◽  
Surface Mass ◽  
Rain Events ◽  
Sentinel 2A ◽  
Optical Satellite Images

Abstract. With dense SAR satellite data time series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and RADARSAT-2 backscatter time series images over mainland Norway and Svalbard, we outline how to map glaciers using descriptive methods. We present five application scenarios. The first shows potential for tracking transient snow lines with SAR backscatter time series and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time series, revealing important information about the area extent of internal accumulation. In the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time series 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.

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