Technology and atmospheric mission platform - OPerations (TOP)

2020 ◽  
Author(s):  
Stefano Natali ◽  
Clemens Rendl ◽  
Gerhard Triebnig ◽  
Daniel Santillan ◽  
Marcus Hirtl ◽  
...  
Keyword(s):  
Web Application ◽  
Data Cube ◽  
Data Availability ◽  
Multidimensional Data ◽  
Full Potential ◽  
Time Model ◽  
Atmospheric Sciences ◽  
In Situ Data ◽  
European Environmental Agency

<p>The ongoing rise in missions to observe Earth from space, especially the various Copernicus’ Sentinel systems not only increases the volume of data daily, but also contributes to the variety of data, the velocity of data availability, and its veracity. In this scenario, Sentinel 5P has already changed the way in which chemical atmospheric components are monitored daily, providing data with global coverage and a very detailed spatial resolution.</p><p>The discipline of atmospheric sciences poses an additional difficulty in efficiently accessing and analysing all available data: the variety is high as the source of atmospheric data is threefold with data coming from EO systems, models as well as in-situ measurements. The heterogeneity and multidimensionality of the so-called data triangle (EO, model, and in-situ data) make an efficient exploitation of the full potential of the available information even more challenging.</p><p>Following the successful experience of the Technology and Atmospheric Mission Platform (TAMP), TOP (http://top-platform.eu/ ) implements the concept of operational Virtual Research Environment (VRE), allowing data users to access, visualize, process, and download heterogeneous, multidimensional data.</p><p>Based on the ADAM datacube technology (https://adamplatform.eu), TOP allows exploiting the following datasets: Sentinel 5P Level 2 products (NO<sub>2</sub> and O<sub>3</sub> tropospheric columns, SO<sub>2</sub>, CO, and CH<sub>4</sub> total columns, and aerosol index); Copernicus Atmosphere Monitoring Service (CAMS) global (surface PM<sub>10</sub>, total column NO<sub>2</sub>, SO<sub>2</sub>, and O<sub>3</sub>) and regional (surface PM<sub>10</sub>, NO<sub>2</sub>, SO<sub>2</sub> and O<sub>3</sub>) analysis and forecast fields; European Environmental Agency (EEA) measurements (CO, NO<sub>2</sub>, PM<sub>10</sub>, SO<sub>2</sub>).</p><p>Users can visualize and process all available data through a web application user interface (Data Analysis and Visualization Environment – DAVE), through a Jupyter notebook interface, and using the ADAM APIs and libraries to directly access available data.</p><p>TOP is deployed on the Mundi DIAS infrastructure (https://mundiwebservices.com/). This allows accessing always most recent satellite products (reprocessed, offline, near real time), model output (analyses and forecasts – up to 5 days) and station measurements (full archive, updated daily).</p><p>TOP is the first operational platform with the data triangle implemented. By creating an atmospheric multi-source data cube, it stimulates a multi-disciplinary scientific approach and significantly facilitates scientific professional life.</p>

Atmospheric Mission Data Packaging (AMiDA)

2020 ◽  
Author(s):  
Stefano Natali ◽  
Clemens Rendl ◽  
Daniel Santillan ◽  
Marcus Hirtl ◽  
Barbara Scherllin-Pirscher ◽  
...  
Keyword(s):  
Web Application ◽  
Numerical Models ◽  
Heterogeneous Data ◽  
Data Cube ◽  
Data Sources ◽  
Full Potential ◽  
Remotely Sensed Data ◽  
Local Data ◽  
Atmospheric Sciences ◽  
Single Data

<p>The scientific and industrial communities are handling continuously increasing amounts of data from Earth Observation (EO) satellite missions and related instruments. This is in particular the case for the atmospheric sciences communities, with the recently launched Copernicus Sentinel-5 Precursor, the upcoming Sentinel-4, -5, and ESA’s Earth Explorers scientific satellites ADM-Aeolus and EarthCARE, but also heritage missions such as ENVISAT, MetOp and OMI Aura. However, the challenge is not only to manage the large volume of data generated by each mission / sensor, but also to manage the data variety. Tools are needed to be able to rapidly and trustfully identify, from all available datasets of a specific region for a specific timeframe, all available products for a selected field (e.g. ozone, trace gases) and prepare these data into a format that is ready to be extracted and used /analyzed (Analysis-Ready Data, ARD). Exploiting potential synergies to maximise the use of data from various sources will be key to harness the full potential of the available information. In summary, there is a need of an “intelligent” packaging of subsets of the available data tailored to the users’ needs.</p><p>The scope of the “Atmospheric Mission Data Packaging” (AMiDA) project is to design, implement, and demonstrate the functionalities of an infrastructure for access and distribution of a wide variety of EO data in the field of atmospheric sciences: heritage, current, and future missions will be managed by the platform, to allow the users accessing, visualizing, and downloading a meaningful subset of this growing data stream.</p><p>AMiDA (https://amida.adamplatform.eu/en/) makes use of the baseline functionalities provided by the TOP platform (http://top-platform.eu/) that already allows accessing and manipulating a large variety of satellite, model, and non-satellite remotely sensed data. TOP is empowered with spatial and temporal homogenization and packaging capabilities to create, from heterogeneous data sources (e.g., SO<sub>2</sub> total column data from different satellites and numerical models) a single data structure (local data cube) for simultaneous exploitation of various data sources. The data cube can be exploited through the TOP tools (web application, Jupyter notebook and APIs) and downloaded by the user.</p><p>A comprehensive demonstration campaign will be performed through five main use cases to demonstrate the capability of AMiDA.</p><p>AMiDA is currently in its final development phase, thus the scope of the contribution is to present the initiative, preliminary results, and stimulate the discussion with potential users, analyzing their needs and see if and how they can use AMiDA to facilitate their everyday professional life.</p>

scholarly journals Paving the Way towards an Armenian Data Cube

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 117 ◽  
Author(s):  
Shushanik Asmaryan ◽  
Vahagn Muradyan ◽  
Garegin Tepanosyan ◽  
Azatuhi Hovsepyan ◽  
Armen Saghatelyan ◽  
...  
Keyword(s):  
Environmental Issues ◽  
Data Cube ◽  
New Paradigm ◽  
Band Ratio ◽  
Earth Observations ◽  
In Situ Data ◽  
Global Concern ◽  
Sentinel 2 ◽  
The Way

Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

NOAA/AVHRR Satellite Data-Based Indices for Monitoring Agricultural Droughts

2005 ◽  
Author(s):  
Felix N. Kogan
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Thermal Conditions ◽  
Early Years ◽  
Data Availability ◽  
Sources Of Information ◽  
In Situ Data ◽  
Marginal Areas ◽  
The World

Operational polar-orbiting environmental satellites launched in the early 1960s were designed for daily weather monitoring around the world. In the early years, they were mostly applied for cloud monitoring and for advancing skills in satellite data applications. The new era was opened with the series of TIROS-N launched in 1978, which has continued until present. These satellites have such instruments as the advanced very high resolution radiometer (AVHRR) and the TIROS operational vertical sounder (TOVS), which included a microwave sounding unit (MSU), a stratospheric sounding unit (SSU), and high-resolution infrared radiation sounder/2 (HIRS/2). These instruments helped weather forecasters improve their skills. AVHRR instruments were also useful for observing and monitoring earth surface. Specific advances were achieved in understanding vegetation distribution. Since the late 1980s, experience gained in interpreting vegetation conditions from satellite images has helped develop new applications for detecting phenomenon such as drought and its impacts on agriculture. The objective of this chapter is to introduce AVHRR indices that have been useful for detecting most unusual droughts in the world during 1990–2000, a decade identified by the United Nations as the International Decade for Natural Disasters Reduction. Radiances measured by the AVHRR instrument onboard National Oceanic Atmospheric Administration (NOAA) polar-orbiting satellites can be used to monitor drought conditions because of their sensitivity to changes in leaf chlorophyll, moisture content, and thermal conditions (Gates, 1970; Myers, 1970). Over the last 20 years, these radiances were converted into indices that were used as proxies for estimating various vegetation conditions (Kogan, 1997, 2001, 2002). The indices became indispensable sources of information in the absence of in situ data, whose measurements and delivery are affected by telecommunication problems, difficult access to environmentally marginal areas, economic disturbances, and political or military conflicts. In addition, indices have advantage over in situ data in terms of better spatial and temporal coverage and faster data availability. The AVHRR-based indices used for monitoring vegetation can be divided into two groups: two-channel indices, and three-channel indices.

scholarly journals Assessing historic water extents in rapidly changing lakes: a hybrid remote sensing classification approach

2020 ◽  
Author(s):  
Connor Mullen ◽  
Marc F. Muller
Keyword(s):  
Unsupervised Classification ◽  
Data Availability ◽  
Satellite Monitoring ◽  
Remote Sensing Classification ◽  
Inundation Frequency ◽  
In Situ Data ◽  
Hydrologic Change ◽  
The One ◽  
Source Of Information

Abstract. The empirical attribution of rapid hydrologic change presents a unique data availability challenge in terms of establishing baseline prior conditions. On the one hand, one cannot go back in time to collect the necessary in situ data if it were not serendipitously collected when the change was taking place. On the other hand, modern satellite monitoring missions are often too recent to capture changes that are ancient enough to provide sufficient observations for adequate statistical inference. In that context, the four decades of continuous global high resolution monitoring enabled by the Landsat missions are an unrivaled source of information to study hydrologic change globally. However, extracting the relevant time series information in a systematic way across Landsat missions remains a monumental challenge. Cloud masking and inconsistent image quality often complicate the automatized interpretation of optical imagery. Focusing on the monitoring of lake water extents, we address this challenge by coupling supervised and unsupervised image classification techniques. Unsupervised classification is first used to detect water on unmasked (cloudless and high quality) pixels. Classification results are then compiled across images to estimate the inundation frequency of each pixel, hinging on the assumption that different pixels will be masked at different times. Inundation frequency is then leveraged to infer the inundation status of masked pixels on individual images through supervised classification. Applied to a representative set of global and rapidly changing lakes, the approach successfully captured water extent fluctuations obtained from in situ gauges (when applicable), or from other Landsat missions during overlapping time periods.

The potential of satellite derived surface state to empirically estimate pan-arctic ground temperature at specific depths and the essential role of in-situ data

2020 ◽  
Author(s):  
Christine Kroisleitner ◽  
Annett Bartsch ◽  
Birgitt Heim ◽  
Mareike Wiezorek
Keyword(s):  
Surface State ◽  
High Arctic ◽  
Ground Temperature ◽  
Data Availability ◽  
Ground Temperatures ◽  
In Situ Data ◽  
Arctic Regions ◽  
Northern Russia

<p>Surface state information, derived from ASCAT microwave sensors (C-band scatterometer), were empirically linked to in-situ arctic ground temperature measurements. The resulting FT2T-regressionmodel was established using the sum of days of year frozen and in-situ mean annual ground temperatures, both at specific depths and years. Regionally, the model showed the best results in Scandinavia and northern Russia with less than 1°C difference to the in-situ data. Overall, the results were valid for most depths and regions, with a slight tendency for underestimation of the ground temperatures on the Eurasian continent (about -1°C) and an overestimation on the American continent up to 2 °C.  The most northern parts of Greenland, the Canadian High Arctic Islands and Alaska, however, showed a high positive bias of more than 10°C. Reasons for this overshooting include the limited amount of measurements in those regions, the oceanic influence and possibly snow cover effects.  <br>Due to the inaccessibility of many arctic regions, in-situ data availability is still sparse and if available not harmonized. We used the currently revised annual ground temperature dataset from CCI+ Permafrost, which combines in-situ data from the GTNP-database, RosHydroMet and additional regional arctic ground temperature datasets (e.g. Nordicana). The resulting determination coefficients of the FT2T-model showed 55% explained variance at shallow borehole-depths below 80cm and decrease with depth to around 25% at 20 meters. This suggests that the sum of frozen days of year delivers better result at shallow depths in the active layer than at the actual permafrost table. The RMSE showed a dependency on the spread of measurement stations considered in the model calibration step. The more input regions we could use, the larger the RMSE got due to the increase of variability in the input data. Inevitably, it’s the in-situ information which enables the translation between ground temperatures and microwave backscatter and thus it fundamentally affects the accuracy of the result.</p>

scholarly journals Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements

2016 ◽  
Author(s):  
Jitendra Kumar ◽  
Forrest M. Hoffman ◽  
William W. Hargrove ◽  
Nathan Collier
Keyword(s):  
Carbon Cycle ◽  
Eddy Covariance ◽  
Temporal Dynamics ◽  
Data Availability ◽  
Full Potential ◽  
Data Set ◽  
Ecosystem Properties ◽  
Eddy Covariance Measurements ◽  
Spatio Temporal

Abstract. Eddy covariance data from regional flux networks are direct in situ measurement of carbon, water, and energy fluxes and are of vital importance for understanding the spatio-temporal dynamics of the the global carbon cycle. FLUXNET links regional networks of eddy covariance sites across the globe to quantify the spatial and temporal variability of fluxes at regional to global scales and to detect emergent ecosystem properties. This study presents an assessment of the representativeness of FLUXNET based on the recently released FLUXNET2015 data set. We present a detailed high resolution analysis of the evolving representativeness of FLUXNET through time. Results provide quantitative insights into the extent that various biomes are sampled by the network of networks, the role of the spatial distribution of the sites on the network scale representativeness at any given time, and how that representativeness has changed through time due to changing operational status and data availability at sites in the network. To realize the full potential of FLUXNET observations for understanding emergent ecosystem properties at regional and global scales, we present an approach for upscaling eddy covariance measurements. Informed by the representativeness of observations at the flux sites in the network, the upscaled data reflects the spatio-temporal dynamics of the carbon cycle captured by the in situ measurements. This study presents a method for optimal use of the rich point measurements from FLUXNET to derive an understanding of upscaled carbon fluxes, which can be routinely updated as new data become available, and direct network expansion by identifying regions poorly sampled by the current network. Data from this study are available at http://dx.doi.org/10.15486/NGT/1279968

scholarly journals Phytoplankton Phenology in the Coastal Zone of Cyprus, Based on Remote Sensing and In Situ Observations

2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Monica Demetriou ◽  
Dionysios E. Raitsos ◽  
Antonia Kournopoulou ◽  
Manolis Mandalakis ◽  
Spyros Sfenthourakis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Phytoplankton Biomass ◽  
Trophic Levels ◽  
Data Availability ◽  
Ocean Colour ◽  
Chl A ◽  
In Situ Data

Alterations in phytoplankton biomass, community structure and timing of their growth (phenology), are directly implicated in the carbon cycle and energy transfer to higher trophic levels of the marine food web. Due to the lack of long-term in situ datasets, there is very little information on phytoplankton seasonal succession in Cyprus (eastern Mediterranean Sea). On the other hand, satellite-derived measurements of ocean colour can only provide long-term time series of chlorophyll (an index of phytoplankton biomass) up to the first optical depth (surface waters). The coupling of both means of observations is essential for understanding phytoplankton dynamics and their response to environmental change. Here, we use 23 years of remotely sensed, regionally tuned ocean-colour observations, along with a unique time series of in situ phytoplankton pigment composition data, collected in coastal waters of Cyprus during 2016. The satellite observations show an initiation of phytoplankton growth period in November, a peak in February and termination in April, with an overall mean duration of ~4 months. An in-depth exploration of in situ total Chl-a concentration and phytoplankton pigments revealed that pico- and nano-plankton cells dominated the phytoplankton community. The growth peak in February was dominated by nanophytoplankton and potentially larger diatoms (pigments of 19’ hexanoyloxyfucoxanthin and fucoxanthin, respectively), in the 0–20 m layer. The highest total Chl-a concentration was recorded at a station off Akrotiri peninsula in the south, where strong coastal upwelling has been reported. Another station in the southern part, located next to a fish farm, showed a higher contribution of picophytoplankton during the most oligotrophic period (summer). Our results highlight the importance of using available in situ data coupled to ocean-colour remote sensing, for monitoring marine ecosystems in areas with limited in situ data availability.

scholarly journals Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years

2019 ◽  
Vol 11 (5) ◽  
pp. 479 ◽  
Author(s):  
Maria Martin ◽  
Darren Ghent ◽  
Ana Pires ◽  
Frank-Michael Göttsche ◽  
Jan Cermak ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Quantitative Information ◽  
Data Availability ◽  
Data Sets ◽  
In Situ Data

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.

scholarly journals An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2584
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Ghaith Amin ◽  
Ibrahim Fayad ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Time Series ◽  
Selection Criteria ◽  
Vegetation Index ◽  
Data Availability ◽  
Training Data ◽  
Classification Model ◽  
In Situ Data ◽  
Ndvi Time Series ◽  
Sentinel 2

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

2017 ◽  
Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  
Keyword(s):  
Satellite Data ◽  
Wind Wave ◽  
Arctic Region ◽  
Web Gis ◽  
The Arctic ◽  
Flexible Tool ◽  
In Situ Data ◽  
Current State ◽  
Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

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