scholarly journals Time Series MODIS and in Situ Data Analysis for Mongolia Drought

2016 ◽  
Vol 8 (6) ◽  
pp. 509 ◽  
Author(s):  
Munkhzul Dorjsuren ◽  
Yuei-An Liou ◽  
Chi-Han Cheng
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
In Situ Data

scholarly journals Legion, Domain-Specific Languages and In Situ Data Analysis & Visualization

2015 ◽  
Author(s):  
Patrick Sean McCormick ◽  
Samuel Keith Gutierrez ◽  
Christine Marie Sweeney ◽  
Nicholas David Moss ◽  
Dean A. Prichard ◽  
...  
Keyword(s):  
Data Analysis ◽  
Domain Specific Languages ◽  
Domain Specific ◽  
In Situ Data

A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data

2016 ◽  
Vol 8 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Benjamin Mack ◽  
Patrick Leinenkugel ◽  
Claudia Kuenzer ◽  
Stefan Dech
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
In Situ Data

scholarly journals Retracking Cryosat-2 Data in SARIn and LRM Modes for Plateau Lakes: A Case Study for Tibetan and Dianchi Lakes

2021 ◽  
Vol 13 (6) ◽  
pp. 1078
Author(s):  
Xiaoli Deng ◽  
Ren-Bin Wang ◽  
Fukai Peng ◽  
Yong Yang ◽  
Nan-Ming Mo
Keyword(s):  
Time Series ◽  
Lake Level ◽  
Correlation Coefficients ◽  
Dianchi Lake ◽  
The Tibetan Plateau ◽  
In Situ Data ◽  
Data Editing ◽  
Good Agreement

This paper estimates lake level variations over two small and adjacent lakes in the Tibetan plateau (TP), namely Gemang Co and Zhangnai Co, as well as the inland Dianchi Lake in China using CryoSat-2 SARIn-mode and LRM 20-Hz waveforms over the period of 2011–2018. Different retrackers and a dedicated data editing procedure have been used to process CryoSat-2 data for determining the lake level time series. The lake level estimations are indirectly validated against those from Jason-2 in TP and from in situ data in Dianchi Lake, both showing good agreement with strong correlation coefficients >0.74. The results of this paper suggest that the official ICE retracker for LRM data and APD-PPT retracker for SARIn-mode waveforms are the most appropriate retrackers over Dianchi Lake and TP lakes, respectively. The trend estimates of the time series derived by both retrackers are 61.0 ± 10.8 mm/yr for Gemang Co and Zhangnai Co in TP, and 30.9 ± 64.9 mm/yr for Dianchi Lake, indicating that the lake levels over three lakes were continuously rising over the study period. The results of this study show that CryoSat-2 SARIn-mode data can be used for monitoring many small lakes that have not been measured by other altimetry missions in TP.

scholarly journals LANL CSSE L2: Case Study of In Situ Data Analysis in ASC Integrated Codes

2013 ◽  
Author(s):  
John M. Patchett ◽  
James P. Ahrens ◽  
Boonthanome Nouanesengsy ◽  
Patricia K. Fasel ◽  
Patrick W. Oleary ◽  
...  
Keyword(s):  
Data Analysis ◽  
In Situ Data

scholarly journals Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multisensor data

2015 ◽  
Vol 12 (5) ◽  
pp. 2283-2313
Author(s):  
J. Pitarch ◽  
G. Volpe ◽  
S. Colella ◽  
H. Krasemann ◽  
R. Santoleri
Keyword(s):  
Time Series ◽  
Baltic Sea ◽  
Late Winter ◽  
The Baltic Sea ◽  
In Situ Data ◽  
Basin Scale ◽  
Gulf Of Bothnia ◽  
Summer Bloom ◽  
The Baltic

Abstract. Fifteen-year (1997–2012) time series of chlorophyll a (CHL) in the Baltic Sea, based on merged multisensor satellite data provided by the European projects Globcolour and ESA-OC-CCI were analysed. Several available CHL algorithms were sea-truthed against a large in situ CHL dataset consisting of data by Seadatanet, HELCOM and NOAA. Matchups were calculated for three separate areas (1) Skagerrak and Kattegat, (2) Baltic Proper plus gulfs of Riga and Finland, called here "Central Baltic", (3) Gulf of Bothnia, and for the three areas as a whole. Statistics showed low linearity. The OC4v6 algorithm (R2 = 0.46, BIAS = +60 %, RMS = 79 % for the whole dataset) was linearly transformed by using the best linear fit (OC4corr). By construction, the bias was corrected, but RMS was increased instead. Despite this shortcoming, we demonstrated that errors between OC4corr and in situ data were log-normally distributed and centred at zero. Consequently, unbiased estimators of the horizontally-averaged CHL could be obtained, the error of which tends to zero when a large amount of pixels is averaged. From the basin-wide time series, the climatology and the annual anomalies were separated. The climatologies revealed completely different CHL dynamics among regions: in Skagerrak and Kattegat, CHL strongly peaks in late winter, with a minimum in summer and a secondary peak in spring. In the Central Baltic, CHL follows a dynamics of a spring CHL peak, followed by a much stronger summer bloom, with decreasing CHL towards winter. The Gulf of Bothnia shows a similar CHL dynamics as the central Baltic, although the summer bloom is absent. Across years, CHL showed great variability. Supported by auxiliary satellite sea-surface temperature (SST) data, we found that phytoplankton growth was inhibited in the central Baltic Sea in the years of colder summers or when the SST happened to increase later in the season. Extremely high CHL in spring 2008 was detected and linked to an exceptionally warm preceding winter. Sharp SST changes were found to induce CHL changes in the same direction. This phenomenon was appreciated best by overlaying the time series of the CHL and SST anomalies.

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