The estimation of solar radiation for different time periods

In this study, the method of Becker and Li was proposed for the estimation of monthly global land surface temperature values from meteorological satellite (NOAA-AVHRR) data. This study introduces generalized regression neural network for the estimation of solar radiation. In order to train the neural network, meteorological satellite and geographical data for the period from 2002 for short term (Adana) and 1998-2002 for long term (Izmir) in Turkey was used. Meteorological satellite and geographical data (latitude, longitude, altitude, month, and mean land surface temperature) are used in the input layer of the network. Solar radiation is the output. Root mean squared and correlation coefficient data between estimated and ground values are found with artificial neural networks values. These values have been found to be 0.0144 MJm -2 and 99.75% (short term) and 0.1381 MJm-2 and 99.26% (long term), respectively. In recent studies, there are some effective techniques about prediction solar radiation data, which is useful to the designers of solar energy systems. Nevertheless, there is no study about the prediction of solar radiation, which has used the artificial neural networks method with land surface temperature data provided from meteorological satellite data. Copyright © Taylor & Francis Group, LLC

Forecasting of air temperature based on remote sensing

The aim of this research is to forecast air temperature based on remote sensing data. So, land surface temperature and air temperature values which were measured by Republic of Turkey Ministry of Forestry and Water Affairs (Turkish State Meteorological Service) during the period 1995-2001 at seven stations (Adana, Ankara, Bali{dotless}kesir, D{stroke}zmir, Samsun, Şanli{dotless}urfa, Van) were compared. The monthly land surface temperature and air temperature were used to have correlation coefficients over Turkey. An empirical method was obtained from equation of correlation coefficients. Separately, Price algorithm was used for the estimation of land surface temperature values to get air temperatures. Then as statistical, air temperature values, belongs to meteorological data in Turkey (26-45°E and 36-42°N) throughout 2002, were evaluated. The research results showed that accuracy of estimation of the air temperature changes from 2.453°K to 2.825°K by root mean square error

Determination of the land surface temperature of the çukurova region using NOAA APT data

Many studies have indicated that the estimation of the land surface temperature using the NOAA satellite analog picture transmission (APT) images is an alternative and easy method compared with other classical methods. In the present work, land surface temperatures of the Çukurova Region in Turkey were estimated during the months of April and July 1998 by using NOAA APT data. DARTCOM hardware and Winsat Pro32 software were used to receive and rectify the APT data. These rectified APT images were used to calculate the surface temperature, and then the results were compared with the meteorological ground based measurements. Comparison of both sets of data indicated a correlation coefficient of 0.97. The rms error for the calculated temperature was evaluated as 1.2°C. A surface temperature map of the Çukurova Region was obtained for 12 April, 1998. As a result of this study, it was concluded that the land surface temperature can be determined by using the NOAA APT data with reasonable precision. © 2004 THE PHYSICAL SOCIETY OF THE REPUBLIC OF CHINA

Estimation of the vapour pressure deficit using NOAA-AVHRR data

In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration. © 2013 Copyright Taylor and Francis Group, LLC.Two different data sets received from the Scientific and Technological Research Council of Turkey and the Turkish State Meteorological Service were used to obtain VPD. First, raw NOAA12-14-15/AVHRR data were translated into a Level 1b format using Quorum Software, and in the second step, the brightness temperatures of channel 4 and channel 5 (range 10.3–11.3 µm and range 11.5–12.5 µm, respectively) were obtained from Level 1b data by employing the Envi 4.3 image-processing program and data received from the Scientific and Technological Research Council of Turkey during 2002

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26-45°E and 36-42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eskişehir, İstanbul, İzmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST ANN) and Becker and Li temperature(LST B-L) method values have been found as 0.077 K and 0.091 K (training stations), 0.045 K and 0.003 K (testing stations), respectively. © 2011 Indian Society of Remote Sensing

Daily global solar radiation mapping of Turkey using Meteosat satellite data

Many studies have indicated that the estimation of solar irradiation at ground level using meteorological satellite data has been an alternative and easy method compared to classical methods. In the present work, the incident of solar radiation over Turkey has been estimated at ground level between July 1997 and December 1998. Statistical regressions between ground data and digital satellite data, measured in the visible band (0.4-1.1 µm) by Meteosat radiometer, have been determined and these regression parameters have been used to estimate solar radiation at ground level. This is the so-called statistical method, which uses a simple model because satellites measure only a few parameters among the many that govern radiative transfers. The visible image (C3D) data used in the present work was Meteosat Wefax type. While pursuing our studies the mean daily sum of global solar radiation over Turkey has been determined to be 18.44 MJ m-2 d-1 with a correlation coefficient of 0.96. The rms error for the mean daily sum has been evaluated as 1.92 MJ m-2 d-1. The monthly mean daily sum of solar radiation has been determined with an rms error of 1.82 MJ m-2 d-1 in two years. During this period the maximum value of the daily sum has been found to occur in June 1998 as 28.47 MJ m-2 d-1, whereas the minimum has been found to occur in December 1998 as 7.35 MJ m-2 d-1. The evaluation procedure, results and possible sources of error are suggested and possible ways of improving the method are described and discussed. © 2004 Taylor & Francis Ltd

Acreage estimation of wheat and barley fields in the province of Adana, Turkey

In this study, the wheat (triticum) and barley (hordeum) planted areas in the province of Adana were determined by using Landsat-5 TM data in 1991. To classify the wheat and barley fields in this region, Landsat bands 3, 4 and 5 were used. Reflectance distribution in these bands has been expected to have an ellipsoidal shape, and a method was developed to make classification for such distribution. To check the accuracy of the classification, test areas in the province were selected and the classification results were compared with ground-truth. Consequently, it was found that the error estimated wheat and barley planted areas was around 15% and the results of the acreage estimation for wheat and barley fields were 218000 ± 32000 hectare in 1991. © 1995 Taylor & Francis Ltd