Top PDF A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

2.2. Sensor Orbit Swath Simulation with the CEOS Visualization Environment (COVE) Tool The orbit swaths and overpass times of Landsat-8, Sentinel-2A and Sentinel-2B were derived globally for 1 January to 31 December 2016 using the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool [ 21 ]. Recently, the COVE tool was used by Whitcraft et al. [ 14 ] to simulate the number of observations provided by different combinations of medium resolution satellites (Landsat-7, Landsat-8, Sentinel-2A, Sentinel-2B, and Resourcesat-2) with respect to agricultural applications, from 60 ◦ N to 60 ◦ S, and for periods up to 180 days. The COVE tool models the orbits and coincident overpasses of multiple satellite missions using orbit and sensor models and satellite ephemeras information. For current or past missions the COVE tool uses a Simplified General Perturbation 4 (SGP4) propagator with satellite position and velocity data, and two-line elements (TLE), to predict the satellite orbit [ 21 ]. The orbit swaths can be visualized via an Internet browser, and, as in this study, can be downloaded as KML files truncated along-track into one-minute granules. At the equator, the KML granules cover about 412 km and 404 km in the track direction for Landsat-8 and Sentinel-2A/-2B, respectively. A total of 274,857, 274,797, and 273,782 one-minute granules for Sentinel-2A, Sentinel-2B, and Landsat-8 were downloaded for 2016, respectively. Only the daytime swaths were considered (defined as granules acquired with solar zenith angle <90 ◦ ). Each KML file includes the acquisition date and time and the corner latitude and longitude defined in degrees to three decimal places (d.p.).
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The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set

The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set

The HLS data set has the potential to support a wide variety of applications requiring high temporal and spatial resolutions optical data. Crop monitoring is maybe the most typical one. The 30-meter resolution is optimal for monitoring crop fields for most parts of the world (Lobell, 2013; Masek et al., 2006; Roy et al., 2014), but can be limiting for some small farming system (Bégué et al., 2018; White and Roy, 2015). The dense time series (80% of the global surface will have a potential revisit period of cloud-free observations of 8.4 days or fewer with the VC L8 + S-2A + S-2B) is adequate for detecting the sharp changes of surface, such as vegetation green-up or crop harvest. Figs. 14 to 16 show three time series examples of single pixels, highlighting the high potential of the HLS data set for crop monitoring at field scale. The dense TS (mostly for the first two examples) allows detection of rapid changes, such as a harvest event in early September 2016 in the New Mexico example (Fig. 15). The third case in France is typical of a cloudy area. While the TS is not as dense as the first two cases, most pheno- logical stages of this double cropping system are clearly identifiable.
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Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 For land cover mapping with Google Earth Engine

Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 For land cover mapping with Google Earth Engine

One of the challenges in producing land cover maps of large areas is the inconsistency in the number of cloud-free pixels available over a period of time. In this study, the differences in the number of cloud-free images per pixel over a year were analysed to evaluate the effects on the temporal aggregation of L8 and S2 data. For the data assessed in this study, to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year were possible with L8, while more than two intervals could be used for Sentinel-2. This is due to Sentinel-2’s higher revisit frequency during 2016 (10 days against 16 days for L8). With the recent launch of Sentinel-2B, the revisit frequency of both Sentinel-2A and 2B is expected to increase to an average of five days, with that number becoming larger for higher latitudes [7]. Such high revisit frequencies could enable the creation of temporal aggregations over at least five or six intervals per year. This will allow researchers to better characterise the seasonality of certain land types and to use time-series analysis approaches [23]. Differences in cloud-free coverage are also affected by the differences in satellite coverage, an issue that should be considered for large areas. For the study area, Sentinel-2 data showed a different level of cloud-free coverage for a large triangular region in the southeast, which corresponded to a different orbit path. This could result in inconsistencies in accuracy [12], and it should be acknowledged when deciding the length of the interval to be used for temporal aggregation or when evaluating the appropriateness of gap-filling methodologies.
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Fusion of VNIR and SWIR Bands of Sentinel 2A Imagery

Fusion of VNIR and SWIR Bands of Sentinel 2A Imagery

Chen Yang , Qingming Zhan, Huimin Liu and Ruiqi Ma[2] showed integrating of the multi-spectral (MS) image and panchromatic (PAN) image for pansharpening process using its spatial information. There many number of sharpening methods based on intensity-hue-saturation (IHS) transform, which gives error of spectral distortion. To address this problem , authors proposed ripplet transform and compression based on IHS pan-sharpening method . Firstly ,In MS image the IHS transform is applied to separate intensity components. Secondly, PAN image is used to obtain multi-scale sub images using discrete ripplet transform(DRT) which is implemented on the intensity component .Then using local variance algorithm high- frequency sub-images are fused and low-frequency images are compressed. Finally, to generate pan-sharpened image the inverse ripplet transform and inverse IHS transform are coupled together. The proposed method by authors is then compared with different dataset such as WorldView-2, Pleiades and Triples through visual and quantitative analysis. The author’s experimental result shows that spectral fidelity of image is obtained for higher spatial resolution images.
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Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery

Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery

position). Traditionally, the main data source for shoreline acquisition has been aerial imagery [ 2 – 4 ]. However, some authors [ 5 – 8 ] have criticized this shoreline because it may be affected by the sea level. They prefer to use the isohypse at the highest tidal position (datum-based shoreline) as it would be more reliable for beach profile changes. This 3D information is normally obtained by GNSS mapping (Global Navigation Satellite System) [ 9 , 10 ], LiDAR [ 11 – 14 ] or TLS [ 15 ] (Terrestrial Laser Scanner). These 3D resources may offer high accuracy data: up to 5 cm (horizontal and vertical) on differential GNSS surveys; and up to 10 cm (horizontal) and 20 cm (vertical) depending on each of the LiDAR flight demands. Nevertheless, mapping hundreds of kilometers at a high frequency may be difficult. Although 3D data offers more complete information, other works show that 2D sources are also useful. Video-monitoring techniques [ 16 – 18 ], given that they can record the shoreline with high frequency, are useful in dynamic areas such as sandy beaches (the variability of the beach and the most landward shoreline position during a storm can be obtained almost in real time). The mean shoreline position may be calculated on an hourly, daily, or weekly bases for trend studies. The main constraint for video-monitorization is that it only works with very limited spaces, normally urban beaches where a camera can be installed on a building. To cover wider areas (hundreds of kilometers), both optical [ 19 , 20 ] and radar [ 21 ] satellite imagery is used—but these techniques remain limited by the temporal frequency of satellite observations. This lack of frequency may cause problems when measuring natural beaches, as the shoreline position may be so affected by the sea level that its position loses geomorphological meaning. However, compiling tens of instantaneous shorelines during a specific time period may be useful for estimating mean shoreline positions and trends during the medium and long term [ 22 ].
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Enhanced Urban Sprawl Monitoring over the Entire District of Rome through Joint Analysis of ALOS AVNIR 2 and SENTINEL 2A Data

Enhanced Urban Sprawl Monitoring over the Entire District of Rome through Joint Analysis of ALOS AVNIR 2 and SENTINEL 2A Data

nel-2A has now taken up the mantle. However, one of the major problems en- countered with urban sprawl measurement in the last decades, has been dealing with the lack of classification accuracy due to differences in spatial and spectral resolution from available remote sensing imagery. For urban growth monitoring of heterogeneous areas, such as those including urban settlements spread over the district of Rome, satellites such as ALOS and SENTINEL-2A, have proven to be necessary. The analysis of the results, as shown in Figure 5, demonstrate that the urbanization trend is positive within the study area and it is filling up (com- paction of the dispersal areas) the remaining gaps left by the sprawl phenomena previously occurred. Much of the impetus for this inexorable phenomenon has been favored by a lack of coordination of land use policies adopted by the vari- ous municipalities. It’s known that measuring land use change is important on local, regional, national, and international scales, and underpins many of the in- ternational efforts to monitor and respond to man-made impacts on the envi- ronment [24]. The result of this investigation is therefore also meant to offer to Administrators and Decision Makers an additional tool for their technical offices for an improved monitoring and consequent planning of the urban expansion. Based on comparisons with our previous work, we found that ALOS AVNIR-2 [23] and Sentinel-2A data provided more accurate land cover/land use classifica- tion of the district of Rome. Sentinel-2A mission is routinely providing global high-resolution optical imagery and offering enhanced continuity of SPOT and Landsat-type data (Lefebvre, 2016). However since the ALOS satellite is no longer operational, it will be necessary for future epochs to employ data from different sensors with different specifications. In particular future extension of this research will be focused on the in depth analysis of the master plans defined
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Automatic Sub-Pixel Co-Registration of LandSat-8 OLI and Sentinel-2A MSI Images Using Phase Correlation and Machine Learning Based Mapping

Automatic Sub-Pixel Co-Registration of LandSat-8 OLI and Sentinel-2A MSI Images Using Phase Correlation and Machine Learning Based Mapping

We used a standard Landsat-8 Level-1 terrain corrected (L1T) product distributed by USGS through the EarthExplorer system (Roy et al. 2014). The product is provided in the World-wide Reference System (WRS-2) of path and row coordinates. The size of the Landsat-8 scene is approximately 185 km × 180 km. The product is provided with the corresponding metafile to convert digital numbers (DNs) into the top-of-atmosphere reflectance (TOA) values. For Sentinel-2, we used a standard Level-1C (L1C) product which is radiometrically and geometrically corrected with ortho-rectification, and provided with the TOA reflectance values (ESA 2016b). The product is delivered in tiles, or granules, of approximately 110 km × 110 km size. Both Landsat-8 L1T and Sentinel-2A L1C products are provided in the Universal Transverse Mercator (UTM) projection with the World Geodetic System 1984 (WGS84) datum. Each Sentinel-2 tile is assigned a UTM zone, and overlapping tiles covering the same geographic region might have different UTM zones assigned. The Sentinel-2 tiling grid is referenced to the U.S. Military Grid Reference System (MGRS). A tile identifier consists of five signs: two numbers and three letters, e.g. 20HNH. The first two numbers in the tile identifier correspond to the UTM zone while the remaining three letters correspond to the tile position. ESA provides a kml file with tile coverage and their identifiers
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Contribution of Sentinel-2 data for applications in vegetation monitoring

Contribution of Sentinel-2 data for applications in vegetation monitoring

the management of satellite SAR data, a valuable tool used for any satellite platform. The drawback is that it has many modules and their functionality is wide. Therefore, to get to know all products this software could generate for analysing and processing the Sentinel-1 SAR data, has been a daunting task. Another issue has been that SAR data in order to be compared with other data need to be georeferenced. In effect, both the Sentinel-1 toolbox and the PolSARPro software have the georeferencing function. The problem with PolSARPro was that by dealing only with bursts couldn’t process the whole image. Therefore we made the necessary coordinate transformation through a specific QGIS program module. After that, a classification algorithm was used. Among all classification algorithms, we used the Unsupervised Wishart provided by PolSARPro. More precisely, on the basis of a SAR image, we generated a Wishart Supervised classification after an automatic pre-setting with a Wishart Unsupervised. See [11]- [13] for more details.
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Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

gest that increased summer velocities may not be offset by later ice velocity decreases (Doyle et al., 2014; de Fleurian et al., 2016), and it is unclear whether hydrofracture can occur within these regions, due to the thicker ice and lim- ited crevassing (Dow et al., 2014; Poinar et al., 2015). These unknowns inland add to the uncertainty in predicting future mass loss from the GrIS. There is a need, therefore, to study the seasonal filling and drainage of lakes on the GrIS, and to understand its spatial distribution and inter-annual varia- tion, in order to inform the boundary conditions for GrIS hy- drology and ice-dynamic models (Banwell et al., 2012, 2016; Leeson et al., 2012; Arnold et al., 2014; Koziol et al., 2017). Remote sensing has helped to fulfil this goal (Hock et al., 2017; Nienow et al., 2017), although it usually involves trading off either higher spatial resolution for lower tempo- ral resolution, or vice versa. For example, the Landsat and ASTER satellites have been used to monitor lake evolution (Sneed and Hamilton, 2007; McMillan et al., 2007; Geor- giou et al., 2009; Arnold et al., 2014; Banwell et al., 2014; Legleiter et al., 2014; Moussavi et al., 2016; Pope et al., 2016; Chen et al., 2017; Miles et al., 2017; Gledhill and Williamson, 2018; Macdonald et al., 2018). While this work involves analysing lakes at spatial resolutions of 30 or 15 m, the best temporal resolution that can be achieved using these satellites is ∼ 4 days and is often much longer due to the satellites’ orbital geometry and/or site-specific cloud cover, which can significantly affect the observational record on the GrIS (Selmes et al., 2011; Williamson et al., 2017). This presents an issue for identifying rapid lake drainage with confidence since hydrofracture usually occurs in hours (Das et al., 2008; Doyle et al., 2013; Tedesco et al., 2013). An al- ternative approach involves tracking lakes at high temporal (sub-daily) resolution but at lower spatial resolution (∼ 250– 500 m) using MODIS imagery (Box and Ski, 2007; Sundal et al., 2009; Selmes et al., 2011, 2013; Liang et al., 2012; Johansson and Brown, 2013; Johansson et al., 2013; Mor- riss et al., 2013; Fitzpatrick et al., 2014; Everett et al., 2016; Williamson et al., 2017, 2018). However, this lower spatial resolution means that lakes < 0.125 km 2 cannot be confi- dently resolved (Fitzpatrick et al., 2014; Williamson et al., 2017) and even lakes that exceed this size are often omitted from the satellite record (Leeson et al., 2013; Williamson et al., 2017).
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Deformation Monitoring Using Sentinel-1 SAR Data

Deformation Monitoring Using Sentinel-1 SAR Data

Published: 22 March 2018 Abstract: Satellite earth observation enables the monitoring of different types of natural hazards, contributing to the mitigation of their fatal consequences. In this paper, satellite Synthetic Aperture Radar (SAR) images are used to derive terrain deformation measurements. The images acquired with the ESA satellites Sentinel-1 are used. In order to fully exploit these images, two different approaches to Persistent Scatterer Interferometry (PSI) are used, depending on the characteristics of the study area and the available images. The main processing steps of the two methods, i.e.; the simplified and the full PSI approach, are described and applied over an area of 7500 km 2 located in Catalonia (Spain). The deformation velocity map and deformation time series are analysed in the last section of the paper.
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Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

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

The evaluation presented in this article indicate that the Theia Snow product allows an accurate detection of the snow cover (and of the absence of snow). However, it remains a preliminary assessment that should be extended in the fu- ture. For example, the evaluation was based only on Sentinel- 2 products, since the integration of Landsat-8 data started during the writing of this paper and should be extended to Landsat-8 products. However, the processing is done with the same algorithm, which is based on the calculation of the NDSI from slope-corrected surface reflectance (level 2A data). In addition, the evaluation was limited to the French Alps and French Pyrenees, while the collection covers other mountain regions. Last, the spatial evaluation using higher- resolution remote-sensing data was focused on the accuracy of the snow detection using very high-resolution clear-sky images, while, as discussed above, the main difficulty is not to detect the snow when the sky is cloud-free but rather to avoid false snow detection within clouds. The reduction of the false-positive rate is the main challenge for the future de- velopments in the LIS algorithm. In the meantime, we also welcome feedback from other users.
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1 Integration of UAV, Sentinel-1 and Sentinel-2 data

1 Integration of UAV, Sentinel-1 and Sentinel-2 data

the sky: Forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang 746. Mangrove Forest Reserve, peninsular Malaysia.[r]

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Sentinel-2 web platform for REDD+ monitoring.
Online web platform for browsing and processing Sentinel-2 data for forest cover monitoring over the Tropics

Sentinel-2 web platform for REDD+ monitoring. Online web platform for browsing and processing Sentinel-2 data for forest cover monitoring over the Tropics

regional coverage products of tree cover percentages or forest / non-forest binary maps, released at yearly intervals. Key limitations in the use of Landsat sensors are the mixed nature of the measured signal, and the difficulty in identifying forest cover disturbances. The latter aspect is especially important in areas where small scale processes are significant. The introduction of Sentinel- 2 is essential for the generation of the estimation of areas under specific disturbance factors and the separation of forest specific biophysical parameters. The use of forest-specific map layers derived from Sentinel-2 will become essential time series in inter-annual trend analysis in forest area change, even if it requires a number of years of consistent observations.
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Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

. However, the SAR signals are affected by soil background and topography ( Chang and Shoshany, 2016 ). Thus, integration of optical and SAR datasets would reduce the influences of soil background and weather conditions on image data analysis for grasslands ( Naidoo et al., 2019 ). The advantages of in- tegrated SAR and optical data were found to improve AGB estimation in non-grassland ecosystems by overcoming limitations of each sensor ( Chang and Shoshany, 2016; Lu, 2006; Lu et al., 2016 ). Additional studies are needed to examine this integrated technology for mon- itoring vegetation dynamics in grazing grasslands ( Svoray et al., 2013 ). High spatio-temporal resolution imagery is required to inform management decisions because grasslands are highly sensitive to grazing and climate variability ( Christensen et al., 2004; Thornton et al., 2014 ). To date, the existing LAI products (e.g. 500-m MODIS) and most herbaceous AGB estimates are in coarse or moderate spatial re- solutions ( Naidoo et al., 2019 ). The availability of Landsat-8 (LC8, 16- day revisit), Sentinel-2 (S2, 10 or 5-day revisit), and Sentinel-1 (S1, 12 or 6-day revisit) together provides time series image data at finer spatial resolutions (10-m to 30-m) and at weekly intervals, which offers an unprecedented opportunity to study grassland LAI and AGB dynamics at the field scale. It remains to: (1) assess the performance of these sensors to estimate grassland LAI and AGB within the growing season, and (2) to evaluate the improvements in comparison to the previous efforts using coarser spatial resolution and single-instrument (e.g. MODI LAI product) approaches in grazing grasslands. The LAI and AGB of grasslands have often been assessed by parametric- (e.g., Multiple linear regression models (MLR)) and non-parametric-based (e.g., sup- port vector machines (SVM), random forest (RF)) models using ground reference data and remote sensing images ( Ullah et al., 2012; Zhang et al., 2018 ). These models were then used to upscale ground ob- servations to regional scales ( John et al., 2018; Liang et al., 2016; Yang et al., 2018; Zhang et al., 2018 ). These studies have provided important basis for understanding the spatial distribution of LAI and AGB in grasslands. However, real-time or near-real time information on grassland conditions is also critically needed for supporting stakeholder and producers to make proper management decisions. The time series data from S1, LC8, and S2 favors the achievement of continuous grassland observations which is beyond the capability of previous
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Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa

Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa

Sentinel-1A images were acquired through the Copernicus Open Access Hub using the sentinelsat software [42]. Scenes belonging to the dry season were favored based on the monthly NDVI values provided by the MODIS-based MOD13C2 dataset. Additionally, the CPC Global Unified Precipitation dataset [43], provided by the NOAA Climate Prediction Center, was used to require at least two days without any precipitation before the acquisition date. The last criterion for the final scene selection was the temporal proximity to the selected Landsat 8 scene. The scenes were acquired as Ground Range Detected (GRD) Level-1 products in the Interferometric Wide (IW) swath mode, therefore multi-looked and projected to ground range using an Earth ellipsoid model. Preprocessing was performed using the Sentinel Application Platform (SNAP) [44]. Firstly, orbit state vectors were refined with precise orbit files and GRD data was calibrated to β nought. Then, thermal noise removal was performed before applying a speckle noise reduction based on a 3 × 3 Lee filter [45], in order to reduce the speckle noise while preserving the textural information. Finally, terrain flattening [46] and Range-Doppler terrain correction [47] were applied based on the SRTM 1-sec Digital Elevation Model (DEM).
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Spatial and Temporal Analysis of Water Quality Parameter using Sentinel-2A Data; Case Study: Lake Matano and Towuti

Spatial and Temporal Analysis of Water Quality Parameter using Sentinel-2A Data; Case Study: Lake Matano and Towuti

Abstract— Lakes are very important for human life in term of ecology and hydrology as the sources of fresh water, the ecosystem of aquatic life and control system to the local micro-climate. Lake Matano and Towuti in South Sulawesi Province are two of 15 priority lakes in Indonesia that need to be preserved from degradation. For preservation purposes, a routine water quality monitoring from the satellite is needed. Currently, there are many satellite images that can be used for extracting water quality parameters such as TSS, Chl-a, and CDOM. In this research, we implemented the use of medium resolution optical satellite image to extract more detail information from lake waters. We have processed remotely sensed data acquired by Sentinal 2 sensor to obtain these water quality parameters. The result showed that water quality in Lake Matano and Towuti were in healthy condition with TSS, Chl-a and CDOM ranged from 1 - 60 mg/m 3 , 0-2.3 mg/m 3 and 0.010 -0.170 m -1 , respectively.
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Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

previous Landsat missions, L8's OLI sensor possesses several major en- hancements including an additional band for coastal and aerosol ap- plications (443 nm) and cirrus clouds (1374 nm). Designed to provide continuity with Landsat (Irons et al., 2012), the MultiSpectral Instru- ment (MSI) onboard ESA's S2 has a 5-day revisit time and 13 spectral bands in the visible to near infrared (443 nm–2190 nm) (Drusch et al., 2012) and is also available through a variety of web providers including the USGS Earth Explorer site and Google Earth Engine (Gorelick et al., 2017). Compared to L8, the S2 sensor features a higher spatial resolu- tion (10, 20 and 60 m), shorter revisit period (5 days) and three addi- tional bands in the near-infrared (703, 740, and 783 nm) region. Both instruments (Table S1) are quantized at 12-bits, and have much higher signal to noise ratios compared to previous Landsat missions and less frequent saturation over highly reflective targets (Pahlevan et al., 2014; Roy et al., 2014). While existing ocean color missions like the MOderate Resolution Imaging Spectroradiometer (MODIS) also have high radio- metric resolution (16 bits) and more frequent revisit times (1–2 days), the finer spatial (10 to 60 m) resolution of L8 and S2 is their major advantage over coarser ocean color sensors like Ocean and Land Color Instrument on board Sentinel-3 (300 m) and MODIS (250–1000 m).
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Potential of the TROPOspheric Monitoring nstrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence

Potential of the TROPOspheric Monitoring nstrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence

Correspondence to: L. Guanter (guanter@gfz-potsdam.de) Received: 9 September 2014 – Published in Atmos. Meas. Tech. Discuss.: 15 December 2014 Revised: 3 March 2015 – Accepted: 4 March 2015 – Published: 19 March 2015 Abstract. Global monitoring of sun-induced chlorophyll flu- orescence (SIF) is improving our knowledge about the photo- synthetic functioning of terrestrial ecosystems. The feasibil- ity of SIF retrievals from spaceborne atmospheric spectrome- ters has been demonstrated by a number of studies in the last years. In this work, we investigate the potential of the up- coming TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite mission for SIF retrieval. TROPOMI will sample the 675–775 nm spectral window with a spectral resolution of 0.5 nm and a pixel size of 7 km ⇥ 7 km. We use an extensive set of simulated TROPOMI data in order to assess the uncertainty of sin- gle SIF retrievals and subsequent spatio-temporal compos- ites. Our results illustrate the enormous improvement in SIF monitoring achievable with TROPOMI with respect to com- parable spectrometers currently in-flight, such as the Global Ozone Monitoring Experiment-2 (GOME-2) instrument. We find that TROPOMI can reduce global uncertainties in SIF mapping by more than a factor of 2 with respect to GOME-2, which comes together with an approximately 5-fold improve- ment in spatial sampling. Finally, we discuss the potential of TROPOMI to map other important vegetation parameters at a global scale with moderate spatial resolution and short re- visit time. Those include leaf photosynthetic pigments and
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Study of wetlands in the Ecuadorian Andes through the comparison of Landsat-8 and Sentinel-2 images

Study of wetlands in the Ecuadorian Andes through the comparison of Landsat-8 and Sentinel-2 images

En este contexto se ha utilizado la teledetección, que ha demostrado ser efectiva en análisis de di- ferentes coberturas: vegetación (Nie et al., 2018), agua (Di Vittorio y Georgakakos, 2018) y suelo (Bansal et al., 2018), ya que ofrece herramientas para caracterizar y seguir el funcionamiento de los ecosistemas, debido a que proporciona índices que informan reiteradamente de los intercambios de materia y energía que tienen lugar entre la biota y la superficie terrestre (Cabello et al., 2016); es decir, que las imágenes satelitales tienen el potencial de proveer valiosa información para la gestión y conservación de los bofedales (García y Lleellish, 2012). En este sentido las imágenes Landsat se han utilizado en distintos estudios de humedales, debido a que es un sistema de satéli- tes diseñados y operados para observar repetidas veces la cubierta de la Tierra con una resolución moderada (Halabisky et al., 2016). El satélite Sentinel-2 tiene una excelente resolución espacial Study of wetlands in the Ecuadorian Andes through the comparison of Landsat-8 and Sentinel-2 images
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Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence

Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence

Abstract. Global monitoring of sun-induced chlorophyll flu- orescence (SIF) is improving our knowledge about the photo- synthetic functioning of terrestrial ecosystems. The feasibil- ity of SIF retrievals from spaceborne atmospheric spectrome- ters has been demonstrated by a number of studies in the last years. In this work, we investigate the potential of the up- coming TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite mission for SIF retrieval. TROPOMI will sample the 675–775 nm spectral window with a spectral resolution of 0.5 nm and a pixel size of 7 km × 7 km. We use an extensive set of simulated TROPOMI data in order to assess the uncertainty of sin- gle SIF retrievals and subsequent spatio-temporal compos- ites. Our results illustrate the enormous improvement in SIF monitoring achievable with TROPOMI with respect to com- parable spectrometers currently in-flight, such as the Global Ozone Monitoring Experiment-2 (GOME-2) instrument. We find that TROPOMI can reduce global uncertainties in SIF mapping by more than a factor of 2 with respect to GOME-2, which comes together with an approximately 5-fold improve- ment in spatial sampling. Finally, we discuss the potential of TROPOMI to map other important vegetation parameters at a global scale with moderate spatial resolution and short re- visit time. Those include leaf photosynthetic pigments and
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