Estimation of Daily Sunshine Duration from Terra and Aqua MODIS Data

Some studies have shown that the estimation of global sunshine duration can be done with the help of geostationary satellites because they can record several images of the same location in a day. In this paper, images obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors of polar orbiting satellites Aqua and Terra were used to estimate daily global sunshine duration for any region in Turkey. A new quadratic correlation between daily mean cloud cover index and relative sunshine duration was also introduced and compared with the linear correlation. Results have shown that polar orbiting satellites can be used for the estimation of sunshine duration. The quadratic model introduced here works better than the linear model especially for the winter months in which very low sunshine duration values were recorded at the ground stations for many days

Calculation land surface temperature depening on becker and Li-1990 algorithm [Yer yüzey sicaicliginin becker ve Li-1990 algori·tmasina bagli hesaplanmasi]

Land surface temperature is an important factor for determination of most physical, biological, energy and water processes and cycles, weather prediction, global ocean circulation and climatic variability in the earth. Further, knowledge of land surface temperature is necessary for management of earth energy resources and environmental studies. For these aims, land surface temperature maps were constituted by using NOAA-12, 14, 15/AVHRR satellite data. Cities of Adana, Ankara, and Antalya, Artvin, İstanbul, İzmir, Kayseri, Konya, Malatya, Samsun, Sivas, Şanhurfa and Van were chosen as control points on the maps. The values which were obtained from the maps produced were compared with these ground-truth values. On monthly avereges of overall comparisons, the correlation coefficient (r) and root mean squared error (RMSE) value Were found to be 0, 989 and 1, 493 °K respectively. When the separate cities were considered, correlation coefficient and RMSE values were found to change within the intervals 0, 959-0, 990 and 1, 589-3, 332 °K respectively. These show that land surface temperatures can be determined with a high accuracy by using the data from NOAA-AVHRR satellites. ©2010 TIBTD Printed in Turkey

Estimation of monthly sunshine duration in Turkey using artificial neural networks

This paper introduces an artificial neural network (ANN) approach for estimating monthly mean daily values of global sunshine duration (SD) for Turkey. Three different ANN models, namely, GRNN, MLP, and RBF, were used in the estimation processes. A climatic variable (cloud cover) and two geographical variables (day length and month) were used as input parameters in order to obtain monthly mean SD as output. The datasets of 34 stations which spread across Turkey were split into two parts. First part covering 21 years (1980-2000) was used for training and second part covering last six years (2001-2006) was used for testing. Statistical indicators have shown that, GRNN and MLP models produced better results than the RBF model and can be used safely for the estimation of monthly mean SD. Copyright © 2014 H. M. Kandirmaz et al

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with Tmax, Tmin, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD. © 2017 Taylor & Francis Group, LLC

Estimation of Monthly Sunshine Duration in Turkey Using Artificial Neural Networks

This paper introduces an artificial neural network (ANN) approach for estimating monthly mean daily values of global sunshine duration (SD) for Turkey. Three different ANN models, namely, GRNN, MLP, and RBF, were used in the estimation processes. A climatic variable (cloud cover) and two geographical variables (day length and month) were used as input parameters in order to obtain monthly mean SD as output. The datasets of 34 stations which spread across Turkey were split into two parts. First part covering 21 years (1980–2000) was used for training and second part covering last six years (2001–2006) was used for testing. Statistical indicators have shown that, GRNN and MLP models produced better results than the RBF model and can be used safely for the estimation of monthly mean SD

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