scholarly journals Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

2019 ◽  
Vol 11 (2) ◽  
pp. 122 ◽  
Author(s):  
Zhongbin Li ◽  
David Roy ◽  
Hankui Zhang ◽  
Eric Vermote ◽  
Haiyan Huang
Keyword(s):  
Aerosol Optical Depth ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Urban Environments ◽  
Landsat 8 ◽  
Urban Aerosol ◽  
Medium Resolution ◽  
Aerosol Monitoring ◽  
Sentinel 2A ◽  
Sentinel 2

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

scholarly journals Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas

2021 ◽  
Vol 13 (20) ◽  
pp. 4140
Author(s):  
Hao Lin ◽  
Siwei Li ◽  
Jia Xing ◽  
Jie Yang ◽  
Qingxin Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Beijing City ◽  
Spatial Coverage ◽  
Sentinel 2

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor.

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

2020 ◽  
Vol 12 (4) ◽  
pp. 741 ◽  
Author(s):  
Luigi Saulino ◽  
Angelo Rita ◽  
Antonello Migliozzi ◽  
Carmine Maffei ◽  
Emilia Allevato ◽  
...  
Keyword(s):  
Fire Effects ◽  
Mediterranean Forest ◽  
Burn Severity ◽  
Landsat 8 ◽  
Mediterranean Countries ◽  
Medium Resolution ◽  
Severity Levels ◽  
The Mediterranean ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

scholarly journals Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

2020 ◽  
Vol 12 (23) ◽  
pp. 3958
Author(s):  
Parwati Sofan ◽  
David Bruce ◽  
Eriita Jones ◽  
M. Rokhis Khomarudin ◽  
Orbita Roswintiarti
Keyword(s):  
Spatial Resolution ◽  
Fire Detection ◽  
Ground Truth ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Spectral Instrument ◽  
Tropical Peatland ◽  
Tropical Environments ◽  
Sentinel 2

This study establishes a new technique for peatland fire detection in tropical environments using Landsat-8 and Sentinel-2. The Tropical Peatland Combustion Algorithm (ToPeCAl) without longwave thermal infrared (TIR) (henceforth known as ToPeCAl-2) was tested on Landsat-8 Operational Land Imager (OLI) data and then applied to Sentinel-2 Multi Spectral Instrument (MSI) data. The research is aimed at establishing peatland fire information at higher spatial resolution and more frequent observation than from Landsat-8 data over Indonesia’s peatlands. ToPeCAl-2 applied to Sentinel-2 was assessed by comparing fires detected from the original ToPeCAl applied to Landsat-8 OLI/Thermal Infrared Sensor (TIRS) verified through comparison with ground truth data. An adjustment of ToPeCAl-2 was applied to minimise false positive errors by implementing pre-process masking for water and permanent bright objects and filtering ToPeCAl-2’s resultant detected fires by implementing contextual testing and cloud masking. Both ToPeCAl-2 with contextual test and ToPeCAl with cloud mask applied to Sentinel-2 provided high detection of unambiguous fire pixels (>95%) at 20 m spatial resolution. Smouldering pixels were less likely to be detected by ToPeCAl-2. The detected smouldering pixels from ToPeCAl-2 applied to Sentinel-2 with contextual testing and with cloud masking were only 35% and 56% correct, respectively; this needs further investigation and validation. These results demonstrate that even in the absence of TIR data, an adjusted ToPeCAl algorithm (ToPeCAl-2) can be applied to detect peatland fires at 20 m resolution with high accuracy especially for flaming. Overall, the implementation of ToPeCAl applied to cost-free and available Landsat-8 and Sentinel-2 data enables regular peatland fire monitoring in tropical environments at higher spatial resolution than other satellite-derived fire products.

Multi-scale ocean colour synergy products for coastal water quality monitoring

2020 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Aida Alvera-Azcárate ◽  
Charles Troupin ◽  
João Cardoso Dos Santos ◽  
Dries Van den Eynde
Keyword(s):  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Empirical Orthogonal Functions ◽  
High Temporal Resolution ◽  
Ocean Colour ◽  
Orthogonal Functions ◽  
Multi Scale ◽  
Medium Resolution ◽  
Sentinel 2

&lt;p&gt;High-quality satellite-based ocean colour products can provide valuable support and insights in the management and monitoring of coastal ecosystems. Today&amp;#8217;s availability of Earth Observation (EO) data is unprecedented including medium resolution ocean colour systems (e.g. Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI) and geostationary satellites (e.g. MSG/SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics. In the Multi-Sync project, we developed advanced ocean colour products (i.e. remote sensing reflectance, turbidity, and chlorophyll a concentration) through the synergetic use of these multi-scale EO data taking advantage of spectral characteristics of traditional medium resolution sensors, the high spatial resolution of some land sensors and the high temporal resolution of geostationary sensors.&lt;/p&gt;&lt;p&gt;To achieve this goal a multi-scale DINEOF (Data Interpolating Empirical Orthogonal Functions) approach was developed to reconstruct missing data using empirical orthogonal functions (EOF), reduce noise and exploit spatio-temporal coherency by joining several spatial and temporal resolutions. Here we present the capacity of DINEOF to extract multi-scale information through the integration of Sentinel-3, Sentinel-2 and SEVIRI datasets.&lt;/p&gt;&lt;p&gt;The functionality of the advanced multi-scale products will be demonstrated in a case study for the Belgian Coastal Zone (BCZ) highly relevant to the user community: sediment transport modelling near the harbour of Zeebrugge in support of dredging operations. As stated in the OSPAR treaty (1992), Belgium is obliged to monitor and evaluate the effects of all human activities on the marine ecosystem. Dredging activities in and near Belgian harbors fall under this treaty and are performed daily to ensure accessibility of the port by ships. Optimization of these dredging activities requires monitoring data which is typically acquired through in situ observations or modelling data. In this case study we take advantage of Sentinel-3, Sentinel-2 and SEVIRI data characteristics to provide a satellite product that meets the end user requirements in terms of product quality and temporal/spatial resolution.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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