scholarly journals Comparing the Köppen-Geiger, Feddema, and UNEP Climate Classifications: An Application to Iran

2021 ◽  
Author(s):  
Tayeb Raziei
Keyword(s):  
High Resolution ◽  
Satisfactory Agreement ◽  
Pairwise Comparison ◽  
Combined Effect ◽  
Sea Surface ◽  
Gridded Data ◽  
Northern Iran ◽  
Data Set ◽  
Climate Maps ◽  
Semi Arid

Abstract This study introduces the climates of Iran defined by Köppen-Geiger, Feddema’s, and UNPEP classifications that applied to a high-resolution ground-based gridded data set relative to the 1985–2017 period. Ten Köppen-Geiger climate types were found for Iran, from which Bwh, Bsk, Csa, Bsh, and Bwk cumulatively account for more than 98% of the territory. Likewise, from 36 possible Feddema’s climate types, Iran possesses fifteen climate types from which the Dry Cool, Semiarid Torrid, Semiarid Hot, Semiarid Warm, Dry warm, Semiarid Cool, and Moist Cool climates collectively occupied approximately 93% of the country. Similarly, arid, semi-arid, humid, and sub-humid UNEP climate types characterized more than 98% of Iran. A few other vertically stratified climates appeared at the highlands of Iran just because of changes in elevation and slope aspects of the mountains. The combined effect of topography and vicinity to sea also creates very distinct climate types in northern Iran. The climate maps of the three used methods reflect the joint effects of topography, latitudinal variation, and land/sea surface contrast on the climate of Iran. A pairwise comparison made between the three classifications showed a satisfactory agreement between the three schemes in representing the main climate types of Iran.

scholarly journals A European daily high-resolution observational gridded data set of sea level pressure

2011 ◽  
Vol 116 (D11) ◽  
Author(s):  
E. J. M. van den Besselaar ◽  
M. R. Haylock ◽  
G. van der Schrier ◽  
A. M. G. Klein Tank
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
Gridded Data ◽  
Data Set ◽  
Sea Level Pressure ◽  
Level Pressure

scholarly journals C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

2021 ◽  
Author(s):  
Nadia Ouaadi ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Salah Er-Raki ◽  
Adnane Chakir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Scientific Community ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Semi Arid

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

scholarly journals A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006

2008 ◽  
Vol 113 (D20) ◽  
Author(s):  
M. R. Haylock ◽  
N. Hofstra ◽  
A. M. G. Klein Tank ◽  
E. J. Klok ◽  
P. D. Jones ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Gridded Data ◽  
Data Set ◽  
Temperature And Precipitation

scholarly journals C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

2021 ◽  
Vol 13 (7) ◽  
pp. 3707-3731
Author(s):  
Nadia Ouaadi ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Salah Er-Raki ◽  
Adnane Chakir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Scientific Community ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Semi Arid

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

scholarly journals Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 500 ◽  
Author(s):  
Chee Wong ◽  
Juneng Liew ◽  
Zulkifli Yusop ◽  
Tarmizi Ismail ◽  
Raymond Venneker ◽  
...  
Keyword(s):  
High Resolution ◽  
Peninsular Malaysia ◽  
Gridded Data ◽  
Data Set ◽  
Rainfall Characteristics

scholarly journals Spatio-temporal variability of daily precipitation concentration in Spain based on a high-resolution gridded data set

2017 ◽  
Vol 38 ◽  
pp. e518-e530 ◽  
Author(s):  
R. Serrano-Notivoli ◽  
J. Martín-Vide ◽  
M. A. Saz ◽  
L. A. Longares ◽  
S. Beguería ◽  
...  
Keyword(s):  
High Resolution ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Gridded Data ◽  
Data Set ◽  
Precipitation Concentration ◽  
Spatio Temporal

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

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