نتایج جستجو برای: satellite data of noaa

تعداد نتایج: 21322574  

ژورنال: علوم آب و خاک 2008
نظری فر, محمدهادی , بهبهانی , سید محمود رضا , رحیمی خوب, علی ,

  Air temperature prediction models using satellite data are based on two variables of land surface temperature and vegetation cover index. These variables are obtained by atmospheric corrections in the values for the above data. Water vapor, ozone, and atmospheric aerosol optical depth are required for the atmospheric correction of visible bands. However, no measurements are available for thes...

نظری فر, محمدهادی , بهبهانی , سید محمود رضا , رحیمی خوب, علی ,

  Air temperature prediction models using satellite data are based on two variables of land surface temperature and vegetation cover index. These variables are obtained by atmospheric corrections in the values for the above data. Water vapor, ozone, and atmospheric aerosol optical depth are required for the atmospheric correction of visible bands. However, no measurements are available for thes...

Journal: :JCM 2014
Md. Z. Rahman Leonid Roytman M. Nazrul Islam

—This paper investigated the normalized difference vegetation index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data during 1982-2003, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to eliminate the long-term inaccuracy of the NDVI data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instabi...

ژورنال: علمی شیلات ایران 2019

The aim of this paper is to determine the sea surface salinity (SSS) and temperature (SST) of Persian Gulf by using the AMSU-B sensor data of NOAA-16 satellite. A multiple linear regression method was used by statistical computing software R on AMSU-B data and in-situ data. Based on the results, the correlation coefficient (R2) for salinity and temperature was 0.85 and 0.94, respectively. Also,...

2010
S. Kondragunta

S. Kondragunta, NOAA/NESDIS Center for Satellite Applications and Research, Camp Springs, MD 20746 [email protected] X. Zhang, ERT Inc., NOAA/NESDIS Center for Satellite Applications and Research, Camp Springs, MD 20746 C. Schmidt, Co-Operative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI R. B. Pierce, NOAA/NESDIS Center for Satellite Applicatio...

2009
Yunyue Yu Ming Chen Dan Tarpley Jeffrey Privette

TEMPERATURE PRODUCTS: ANALYZING DIFFERENCE BETWEEN SATELLITE AND IN SITU MEASUREMENTS Yunyue Yu*, Ming Chen, Dan Tarpley, Jeffrey Privette Center for Satellite Applications and Research, NOAA/NESDIS, 5200 Auth Rd., Camp Springs, MD, USA 20746-4304 I. M. Systems Group, Inc., Camp Springs, MD, USA 20746 Short & Associates, Camp Springs, MD, USA 20746 National Climate Data Center, NOAA/NESDIS, Ash...

A. R. Bahmanzadegan, F. Azarsina, K. Lari, M. R. Fatemi,

Remote sensing has changed modern oceanography by proving synoptic periodic data which can be processed. Since the satellite data are usually too much and nonlinear, in most cases, it is difficult to distinguish the patterns from these images. In fact, SOM (Self-Organizing Maps) model is a type of ANN (Artificial Neural Network) that has the ability to distinguish the efficient patterns from th...

ژورنال: علوم آب و خاک 2011
پریسا صابری, , سید محمود رضا بهبهانی , , علی رحیمی خوب, , محمدهادی نظری فر, ,

In this study, the remote sensing statistical approach was used to determine the global solar radiation from NOAA-AVHRR satellite data in southeast of Tehran. This approach is based on the linear correlation between a satellite derived cloud index and the atmospheric transmission measured by the clearness index on the ground. A multiple linear regression model was also used to convert the five ...

A. R. Bahmanzadegan, F. Azarsina K. Lari M. R. Fatemi

Remote sensing has changed modern oceanography by proving synoptic periodic data which can be processed. Since the satellite data are usually too much and nonlinear, in most cases, it is difficult to distinguish the patterns from these images. In fact, SOM (Self-Organizing Maps) model is a type of ANN (Artificial Neural Network) that has the ability to distinguish the efficient patterns from th...

Journal: :IEEE Transactions on Aerospace and Electronic Systems 1984

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