scholarly journals Comparision of observed and theoretical amplitudes of oscillations above granules and intergranular lanes

2001 ◽  
Vol 203 ◽  
pp. 192-194
Author(s):  
E. V. Khomenko
Keyword(s):  
Time Series ◽  
Wave Propagation ◽  
Spatial Resolution ◽  
3D Model ◽  
High Spatial Resolution ◽  
Model Atmosphere ◽  
Solar Photosphere ◽  
Line Profiles ◽  
Physical Conditions ◽  
Excitation Mechanisms

We do modeling of the wave propagation in the solar photosphere. NLTE synthesis of the time series of the Fe I 5324 Å line profiles is performed using 3D model atmosphere. Velocity and intensity oscillations resulted from computations are compared with high spatial resolution observations. We conclulde that differences in oscillatory amplitudes above granules and intergranular lanes can be produced by variations of the physical conditions in these structures without invoking any excitation mechanisms.

scholarly journals Astrochemistry in external galaxies: how to use molecules as probes of their physical conditions

2015 ◽  
Vol 11 (S315) ◽  
pp. 17-25 ◽  
Author(s):  
Serena Viti
Keyword(s):  
Radiative Transfer ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Molecular Gas ◽  
Molecular Emission ◽  
Physical Conditions ◽  
External Galaxies ◽  
Molecular Ratios ◽  
Multiple Chemical ◽  
Resolution Data

AbstractIt is now well established that chemistry in external galaxies is rich and complex. In this review I will explore whether one can use molecular emissions to determine their physical conditions. There are several considerations to bear in mind when using molecular emission, and in particular molecular ratios, to determine the densities, temperatures and energetics of a galaxy, which I will briefly summarise here. I will then present an example of a study that uses multiple chemical and radiative transfer analyses in order to tackle the too often neglected ‘degeneracies’ implicit in the interpretation of molecular ratios and show that only via such analyses combined with multi-species and multi-lines high spatial resolution data one can truly make molecules into powerful diagnostics of the evolution and distribution of molecular gas.

scholarly journals Estimation of grassland use intensities based on high spatial resolution LAI time series

2015 ◽  
Vol XL-7/W3 ◽  
pp. 285-291 ◽  
Author(s):  
S. Asam ◽  
D. Klein ◽  
S. Dech
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Management Practices ◽  
High Spatial Resolution ◽  
Vegetation Period ◽  
Grassland Management ◽  
Transfer Model ◽  
Land Use Patterns ◽  
Area Index ◽  
Intensively Managed

The identification and surveillance of agricultural management and the measurement of biophysical canopy parameters in grasslands is relevant for environmental protection as well as for political and economic reasons, as proper grassland management is partly subsidized. An ideal monitoring tool is remote sensing due to its area wide continuous observations. However, due to small-scaled land use patterns in many parts of central Europe, a high spatial resolution is needed. In this study, the feasibility of RapidEye data to derive leaf area index (LAI) time series and to relate them to grassland management practices is assessed. The study area is the catchment of river Ammer in southern Bavaria, where agricultural areas are mainly grasslands. While extensively managed grasslands are maintained with one to two harvests per year and no or little fertilization, intensive cultivation practices compass three to five harvests per year and turnover pasturing. <br><br> Based on a RapidEye time series from 2011 with spatial resolution of 6.5 meters, LAI is derived using the inverted radiation transfer model PROSAIL. The LAI in this area ranges from 1.5 to 7.5 over the vegetation period and is estimated with an RMSE between 0.7 and 1.1. The derived LAI maps cover 85 % of the study area’s grasslands at least seven times. Using statistical metrics of the LAI time series, different grassland management types can be identified: very intensively managed meadows, intensively managed meadows, intensively managed pastures, and extensively managed meadows and moor. However, a precise identification of the mowing dates highly depends on the coincidence with satellite data acquisitions. Further analysis should focus therefor on the selection of the temporal resolution of the time series as well as on the performance of further vegetation parameters and indices compared to LAI.

scholarly journals Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850–2100)

2021 ◽  
Author(s):  
Xikun Wei ◽  
Guojie Wang ◽  
Donghan Feng ◽  
Zheng Duan ◽  
Daniel Fiifi Tawia Hagan ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Resolution ◽  
Bias Correction ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Future Climate Change ◽  
Observation Data ◽  
Near Surface ◽  
Spatial Downscaling

Abstract. Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850–1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015–2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a ‘tertiary class echelon’ condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021).

scholarly journals A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 817-820
Author(s):  
B. Liu ◽  
J. Chen ◽  
H. Xing ◽  
H. Wu ◽  
J. Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Image Data ◽  
Resolution Time ◽  
Simulated Experiment ◽  
Land Cover Data ◽  
Multi Temporal

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

scholarly journals Long-term changes of tropospheric NO<sub>2</sub> over megacities derived from multiple satellite instruments

2012 ◽  
Vol 12 (12) ◽  
pp. 31767-31828 ◽  
Author(s):  
A. Hilboll ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Middle East ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
The United States ◽  
Central China ◽  
Full Time ◽  
Data Set ◽  
Tropospheric No2

Abstract. Tropospheric NO2, a key pollutant in particular in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term data set of tropospheric pollution. However, the measurements differ in spatial resolution, local time of measurement, and measurement geometry. All these factors can severely impact the retrieved NO2 columns, which is why they need to be taken into account when analysing time series spanning more than one instrument. In this study, we present several ways to explicitly account for the instrumental differences in trend analyses of the NO2 columns derived from satellite measurements, while preserving their high spatial resolution. Both a physical method, based on spatial averaging of the measured earthshine spectra and extraction of a resolution pattern, and statistical methods, including instrument-dependent offsets in the fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series and to an extended data set comprising also GOME-2 and OMI measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution and significantly reducing the uncertainties of the retrieved trend estimates compared to previous studies. We show that measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over East Central China triplicating from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as +27 ± 3.7% yr−1 and +20 ± 1.9% yr−1 in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.0 ± 0.37% yr−1. Most megacities in China, India, and the Middle East show increasing NO2 columns of +5–10% yr−1, leading to a doubling to triplication within the observed period. While linear trends derived with the different methods are consistent, comparison of the GOME and SCIAMACHY time series as well as inspection of time series over individual areas shows clear indication of non-linear changes in NO2 columns in response to rapid changes in technology used and the economical situation.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

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