An inter-sensor calibration and atmospheric correction system for long-term time series of AVHRR imagery for coastal waters

2013 ◽  
Vol 50 (2) ◽  
pp. 184-195 ◽  
Author(s):  
Lihong Su ◽  
James C. Gibeaut
Keyword(s):  
Time Series ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Sensor Calibration ◽  
Avhrr Imagery ◽  
Correction System

scholarly journals Climatic variability in the Skagerrak and coastal waters of Norway

2011 ◽  
Vol 69 (5) ◽  
pp. 758-763 ◽  
Author(s):  
Jon Albretsen ◽  
Jan Aure ◽  
Roald Sætre ◽  
Didrik S. Danielssen
Keyword(s):  
Time Series ◽  
Coastal Waters ◽  
Climatic Variability ◽  
Circulation Pattern ◽  
Regional Warming ◽  
Marine Research ◽  
Norwegian Coast ◽  
The North ◽  
Salinity Increase

Abstract Albretsen, J., Aure, J., Sætre, R., and Danielssen, D. S. 2012. Climatic variability in the Skagerrak and coastal waters of Norway. – ICES Journal of Marine Science, 69: 758–763. The Institute of Marine Research in Norway collects marine data from all national waters. Data are primarily collected from vessels, but observation buoys, manual measurements, and oceanographic gliders are also used. The most valuable long-term data for elucidating decadal hydrographic variability in the Skagerrak and along the Norwegian coast are the time-series from the transect between Norway and Denmark, and observations carried out at eight fixed coastal stations in the region. The time-series date back to the 1950s and the 1930s, respectively, and the observation frequencies range from approximately once a month to 3–4 times per month. The hydrographic data and their long-term fluctuations have been used in several studies, with particular emphasis on the increased salinity and temperature of the 1990s. Trends during the past decade, however, indicate that warming has continued but that a salinity increase is less evident, implying signs of regional warming in parallel with the global warming observed both on land and in the sea. The overall temperature increase off the Norwegian coast at deeper layers from the 1961–1990 period to the 2000–2009 decade is ∼0.8°C, but some of this can be related to natural variability in the North Atlantic circulation pattern.

scholarly journals Consistency of Radiometric Satellite Data over Lakes and Coastal Waters with Local Field Measurements

2020 ◽  
Vol 12 (4) ◽  
pp. 616 ◽  
Author(s):  
Krista Alikas ◽  
Ilmar Ansko ◽  
Viktor Vabson ◽  
Ave Ansper ◽  
Kersti Kangro ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Ground Truth ◽  
Local Data ◽  
Ground Truth Data ◽  
In Situ Data ◽  
Global Coverage

The Sentinel-3 mission launched its first satellite Sentinel-3A in 2016 to be followed by Sentinel-3B and Sentinel-3C to provide long-term operational measurements over Earth. Sentinel-3A and 3B are in full operational status, allowing global coverage in less than two days, usable to monitor optical water quality and provide data for environmental studies. However, due to limited ground truth data, the product quality has not yet been analyzed in detail with the fiducial reference measurement (FRM) dataset. Here, we use the fully characterized ground truth FRM dataset for validating Sentinel-3A Ocean and Land Colour Instrument (OLCI) radiometric products over optically complex Estonian inland waters and Baltic Sea coastal areas. As consistency between satellite and local data depends on uncertainty in field measurements, filtering of the in situ data has been made based on the uncertainty for the final comparison. We have compared various atmospheric correction methods and found POLYMER (POLYnomial-based algorithm applied to MERIS) to be most suitable for optically complex waters under study in terms of product accuracy, amount of usable data and also being least influenced by the adjacency effect.

Oithona davisae: naturalization in the Black sea, inter-annual and seasonal dynamics, effect on the structure of the planktonic copepod community

2019 ◽  
Vol 59 (6) ◽  
pp. 1008-1015
Author(s):  
A. D. Gubanova ◽  
O. A. Garbazey ◽  
D. A. Altukhov ◽  
V. S. Mukhanov ◽  
E. V. Popova
Keyword(s):  
Black Sea ◽  
Coastal Waters ◽  
Native Species ◽  
Significant Decline ◽  
Acartia Tonsa ◽  
The Black Sea ◽  
Copepod Community ◽  
Oithona Davisae ◽  
Sevastopol Bay

Long-term (20032014) routine observations of zooplankton in Sevastopol Bay (the Black Sea) have allowed the naturalization of the invasive copepod Oithona davisae to be studied in the Black Sea coastal waters. Inter-annual and seasonal variability of the species and their impact on the native copepod community have been analyzed. The invasion of O. davisae and their undoubted dominance in terms of abundance were shown to alter the community structure but, at the same time, the abundances of the native species did not decrease, excepting the Black Sea earlier invader Acartia tonsa. A significant decline in A. tonsa numbers over the stages of O. davisae establishment and naturalization provided evidence of competition between the species. O. davisae have been demonstrated to gain competitive advantage over A. tonsa, that ensured their fast dispersal in the Black Sea, acclimatization in the new habitat and the successful competition over native species.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Sign in / Sign up

Export Citation Format

Share Document