This article describes a study that estimates vapour pressure deficit (VPD) using NOAA-AVHRR satellite data. VPD is an important meteorological variable that influences many fields such as plant growth, disease transmission, and evapotranspiration. The study calculates land surface temperature and total precipitable water using NOAA-AVHRR data in order to estimate VPD, which is then statistically compared to VPD estimated from meteorological station data. The results show that VPD can be estimated from satellite data with a root mean square error of around 6 mb or less compared to ground measurements.
Groundwater and River Water Interaction at Ciromban and Cibeureum Riverbank, ...Dasapta Erwin Irawan
Author:
Pratama, A., Abdulbari, N ., Nugraha, MI., Prasetio, Y., Tulak, GP., Darul, A., and Irawan, DEa).
Affiliation:
Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Institut Teknologi Bandung,
Jl. Ganesa No. 10, Bandung, 40132
Abstract
Water shortage is a common problem in the high density settlement along the riverbank of Ciromban and Cibeureum River, Tasikmalaya, as the quality of the water also decreases. One of the solution is to maximize the use of river water. This study aims to investigate the interaction between river and groundwater along the riverbank as a function of land use impact. A river water and unconfined groundwater level mapping has been conducted to make water flow map, assuming both waters are in the same flow system. Physical parameters, temperature, TDS, and pH were measured at each stations to understand water characteristics. Based on observations at 50 dug wells and 12 river stations on July-August 2014, a close interaction between both water bodies has been identified with two flow systems: effluent flow (or gaining stream) at Cibereum river segment and influent flow (losing stream) at Ciromban river segment. Physical parameters show a high correlation in temperature, pH, and TDS. Hence, further evaluation should be taken before using river water as raw water supply in Tasikmalaya area.
Groundwater and River Water Interaction at Ciromban and Cibeureum Riverbank, ...Dasapta Erwin Irawan
Author:
Pratama, A., Abdulbari, N ., Nugraha, MI., Prasetio, Y., Tulak, GP., Darul, A., and Irawan, DEa).
Affiliation:
Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Institut Teknologi Bandung,
Jl. Ganesa No. 10, Bandung, 40132
Abstract
Water shortage is a common problem in the high density settlement along the riverbank of Ciromban and Cibeureum River, Tasikmalaya, as the quality of the water also decreases. One of the solution is to maximize the use of river water. This study aims to investigate the interaction between river and groundwater along the riverbank as a function of land use impact. A river water and unconfined groundwater level mapping has been conducted to make water flow map, assuming both waters are in the same flow system. Physical parameters, temperature, TDS, and pH were measured at each stations to understand water characteristics. Based on observations at 50 dug wells and 12 river stations on July-August 2014, a close interaction between both water bodies has been identified with two flow systems: effluent flow (or gaining stream) at Cibereum river segment and influent flow (losing stream) at Ciromban river segment. Physical parameters show a high correlation in temperature, pH, and TDS. Hence, further evaluation should be taken before using river water as raw water supply in Tasikmalaya area.
Интернет-бизнес Как увеличить продажи с помощью партнерской программы» (Основ...Александр Круглов
Сайт сервиса АвтоВебОфис (АвтоОфис): https://autoweboffice.ru/?r=acs&id=118&lg=ru
Из видео Вы узнаете:
- Как увеличить продажи с помощью партнерской программы?
- Что такое партнерская программа?
- Типы партнерских программ
- Общий механизм работы партнерской программы
- Особенности партнерской программы в Интернет
- Как партнер может Вас подставить (обмануть)?
- Тонкости настройки партнерской программы
- Как работать с партнерами?
- Как осуществлять email-рассылки по партнерам?
Fp me reporte aplicaci+¦n aamtic_gxx 2 lll sesionGIOMAR Jimenez
este documento muestra la continuidad del ambiente de aprendizaje mediado por las tic. El cual se esta llevando a cabo en la sede Magdalena Ortega de Nariño Barrio Alto Polvorines.
Интернет-бизнес Как увеличить продажи с помощью партнерской программы» (Основ...Александр Круглов
Сайт сервиса АвтоВебОфис (АвтоОфис): https://autoweboffice.ru/?r=acs&id=118&lg=ru
Из видео Вы узнаете:
- Как увеличить продажи с помощью партнерской программы?
- Что такое партнерская программа?
- Типы партнерских программ
- Общий механизм работы партнерской программы
- Особенности партнерской программы в Интернет
- Как партнер может Вас подставить (обмануть)?
- Тонкости настройки партнерской программы
- Как работать с партнерами?
- Как осуществлять email-рассылки по партнерам?
Fp me reporte aplicaci+¦n aamtic_gxx 2 lll sesionGIOMAR Jimenez
este documento muestra la continuidad del ambiente de aprendizaje mediado por las tic. El cual se esta llevando a cabo en la sede Magdalena Ortega de Nariño Barrio Alto Polvorines.
Did you miss the HASPS Alumni Reunion? Did you attend the reunion and have a great time seeing your old colleagues? Either way, click here to view photos from the event!
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Influence of Row Covers on Soil Loss & Plant Growth in White Cabbage Cultivation; Gardening Guidebook for Stuttgart, Germany ~ University of Hohenheim~ For more information, Please see websites below:
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Organic Edible Schoolyards & Gardening with Children =
http://scribd.com/doc/239851214 ~
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Double Food Production from your School Garden with Organic Tech =
http://scribd.com/doc/239851079 ~
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Free School Gardening Art Posters =
http://scribd.com/doc/239851159 ~
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Increase Food Production with Companion Planting in your School Garden =
http://scribd.com/doc/239851159 ~
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Healthy Foods Dramatically Improves Student Academic Success =
http://scribd.com/doc/239851348 ~
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City Chickens for your Organic School Garden =
http://scribd.com/doc/239850440 ~
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Huerto Ecológico, Tecnologías Sostenibles, Agricultura Organica
http://scribd.com/doc/239850233
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Simple Square Foot Gardening for Schools - Teacher Guide =
http://scribd.com/doc/239851110
Abstract— Airborne pollen calendars are useful to estimate the flowering season of the different plants as well as to indicate the allergenic potential present in the atmosphere at a given time. In this study, a 1-year survey (from January 2013 to February 2014) is presented of the atmospheric concentration of pollen types in Guarda (Portugal), using a 7-day Hirst volumetric trap. The daily mean concentration of both the number of pollen grains and the main pollen season was determined as well as the bi-hourly variations. The highest airborne pollen concentration was found during early spring and early summer. Contrastingly, December was the month with the lowest pollen concentration. The major pollens sampled were Quercus, Pinaceae, Poaceae, Cupressaceae, Urticaceae, Apiaceae, Oleaceae and Polygonaceae. Some differences were found in the intradiurnal distribution patterns of the pollen types studied, with some taxa types being predominantly sampled in the morning (8:00-10:00 a.m.) while others were more evident in the late evening hours (8-10 p.m.). Finally, these results were compared with the forecast made by the Portuguese Aerobiology Network for the central region of Portugal, revealing some significant differences in the pollination periods.
Influence of Climatic Factors on the Δ13c Values of the C3, C4 And CAM Dicot ...QUESTJOURNAL
ABSTRACT: Species of the Centrospermeae occurring at different altitudes were analyzed for δ13C values and assigned for graphical representation. The aridity of the study area was evident as defined using the Klimadiagramm. Climatic data was studied and represented on graphs for interpretation. The frequency ofδ 13C values of the species at different altitudes, namely 500m a.s.l., 1000m a.s.l., 1500m a.s.l., 2000m a.s.l., 2500m a.s.l., 3000m a.s.l., 3500m a.s.l. and 4000m a.s.l., are presented on graphs. The data show thatδ13C values is a good predictor of spatial diversity and shift of the species along the altitudinal gradient of environmental factors.There is phenomenal trend such that δ13C values distribution along altitudinal differentiation the values of -10.60‰, to -16.65‰, -17.75‰ to -18.87‰, and -18.89‰ to -32.42‰ correspond to the species at low altitudes (0m a.s.l. – 1500m a.s.l.), intermediate altitude (1,550m a.s.l.-1,700m a.s.l.) and high altitude (1,800m a.s.l. – 4200m a.s.l.0, respectively. The inverse correlation between temperature and rainfall defines the causal climatic factors affecting C3 and C4 species along the altitudinal gradient. The occurrence of the transition zone between temperature and rainfall mirror that between the relative abundance of the C3 and C4 species along the altitude. This floristic data predict NAD-ME, NADP-ME AND PEP-CK types of monocot-dicot transition along the altitude with respect to bioproductivity in the tropics.
Examination of Total Precipitable Water using MODIS measurements and Comparis...inventionjournals
In this research, precipitable water vapor, as the most effective character in the production of biomass is estimated using remote sensing techniques. Total Precipitable Water (TPW) was estimated using measurements in the Near Infrared bands of the MODIS. To examine the level of confidence in TPW deriving, a simultaneous in situ measurement by Radiosonde and ground-based Global Positioning System (GPS) was carried out. The TPW as results in Radiosonde and GPS was accomplished using the relevant physical equations and base on wet delay troposphere, respectively. Results showed a high correlation among the values of TPW derived from MODIS banding ratio, Radiosonde and GPS data at the Mehrabad station. Also, Using the ratio of the apparent reflectance in the water vapor absorption band to reflectance in non-absorbing band, the atmospheric water vapor transparency was mapped, that the maps showed a high correlation between apparent reflectance and TPW MODIS as their statistical results showed an inverse negative relationship(R²= -0.97).
Assessing the Monthly Variation of Reference Evapotranspiration of Nsukka, En...ijtsrd
The application of suitable water quantity to the crops in irrigation farming is very important for effective crop yields as less or more than the required quantity of water when applied will affect the crop output negatively. The objective of this work was to determine the monthly reference Evapotranspiration of Nsukka, Enugu state of Nigeria, as well as how its variation is affected by temperature change. This work made use of Hargreaves-Samani model of evapotranspiration prediction, using the data of minimum and maximum temperature obtained from NASAs earthdata database. It was shown from the results that highest evapotranspirationoccured in February, and lowest occurred in July. It was further observed that reference Evapotranspiration of the study area has a positive correlation coefficient of 0.9927 with the maximum temperature and a negative correlation coefficient of -0.1879 with the minimum temperature. Conclusively, it was stated that reference evapotranspiration was higher in the study area in dry season than in rainy season, and that it was directly proportional to the maximum temperature and inversely to the minimum temperature. Oyibo Muazu | Abdullahi Ayegba"Assessing the Monthly Variation of Reference Evapotranspiration of Nsukka, Enugu State, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11409.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/11409/assessing-the-monthly-variation-of-reference-evapotranspiration-of-nsukka-enugu-state-nigeria/oyibo-muazu
Sinusoidal Model Development for the Study of Diurnal Variation of Surface Ai...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Similar to Estimation of the vapour pressure deficit using noaa avhrr data (20)
Yapay sinir ağı ve noaaavhrr uydu verilerini kullanarak hava sıcaklığının tah...mehmet şahin
iklim değişikliği, çeşitli ısı ve radyasyon akılarının belirlenmesinde, buhar basınç açığı, su potansiyeli, kentsel arazi kullanımı
ve ısı adası, kısa dalga ve uzun dalga radyasyon, stoma direnci, ekoloji, hidroloji ve atmosfer bilimleri de dahil olmak üzere bir
çok uygulama için kullanılmaktadır. Ayrıca, hava sıcaklığı bilgisi insan sağlığı için gereklidir. Bu kadar önemli olan hava
sıcaklığı, meteorolojik istasyonlarda ölçülmektedir. Fakat istasyon dağılımları yeryüzünde yeterli düzeyde olmadığı gibi yeterli
sayıda istasyon da bulunmamaktadır. Bu nedenle, uydular kullanılmaya başlanmıştır. Literatürde yer yüzey sıcaklığı tahmini
yapmak için oldukça fazla algoritma geliştirilmesine rağmen doğrudan hava sıcaklığını tahmin eden algoritmalar yeterince
geliştirilememiştir. Bu nedenle bu çalışmada yapay sinir ağı kullanılarak hava sıcaklığı tahmini yapılmıştır. Yapay sinir ağda
ay, yükseklik, enlem, boylam, aylık ortalama yer yüzey sıcaklıkları girdi olarak kullanılırken, aylık ortalama hava sıcaklığı çıktı
olarak elde edilmiştir. Girdi parametrelerinden yer yüzey sıcaklığı, NOAA/AVHRR datalarından sağlanmıştır. Ağda öğrenme
algoritmaları olarak; tarinlm, trainscg, trainoss kullanılırken transfer fonksiyonu olarak tansig, logsig ve lineer kullanılmıştır.
Ocak 1995’den Aralık 2005’e kadar olarak zaman aralığı, çalışma periyodu olarak belirlenmiştir. Ağın eğitilmesi için 1995-
2004 yılları arası veriler kullanılırken, test verisi olarak 2005 yılı verileri kullanılmıştır. Tahmin sonuçlarının, gerçek datalarla
istatistiksel olarak değerlendirilmesi yapılmış olup hata değeri oldukça az çıkmıştır. El edilen en iyi modellemede, korelasyon
katsayısı ve kök ortalama kare hatası sırasıyla 0.996 ve 1.253 K olarak hesaplanmıştır.
Siirt ilinin yer yüzey sıcaklığının belirlenmesi için farklı split window alg...mehmet şahin
Land surface temperature is an important parameter to control the energy exchange between earth's surface and atmosphere.
In addition, land surface temperature is used in the development of computer modelling of many environmental quantity as
climate change, numerical weather prediction, global water cycle, drought index, solar radiation and frost. In this study,
visible and near infrared channels (channels 1 and 2) and thermal channels (channels 4 and 5) of NOAA / AVHRR satellite
images were used to obtain land surface temperature data. Price-1984, Becker and Li–1990 and Ulivieri et al.-1994 algorithms
were determined to calculate the land surface temperature. Results of land surface temperature obtained from satellite
algorithms were compared in terms of statistics based on the location of Siirt with the actual land surface temperatures
obtained from General Directorate of Meteorology. According to the results; Root Mean Square Error values of Price-1984,
Becker and Li-1990, and Ulivieri et al.-1994 algorithms were calculated as 3.308K, 2.681K and 2.171K, respectively. In the
same order, the correlation coefficients of algorithms were obtained as 0.972, 0974 and 0984. It has been reached to
conclude that using Ulivieri et al.-1994 algorithm is appropriate to estimate land surface temperature that has been obtained
from Ulivieri et al.-1994 algorithm for getting with the lowest error in satellite-based solar energy calculations.
Forecasting of air temperature based on remotemehmet şahin
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, Balıkesir, Đzmir, Samsun, Sanlıurfa, 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 (26–45ºE and 36–42ºN) throughout 2002, were evaluated. The research results showed that
accuracy of estimation of the air temperature changes from 2.453ºK to 2.825ºK by root mean square error.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Estimation of wind power density with artificial neural networkmehmet şahin
Industry and technology are rapidly developing with each passing day. They need energy to sustain this evolution. The demand of energy is mainly provided from fossil fuels. Unfortunately, this kind of energy reserves are consumed away day by day. Therefore, there is a need to use alternative energy sources to supply energy needs. Alternative energy sources can be listed as; solar, wind, wave, biomass, geothermal and hydro-electric power. Our country has significant potential for wind energy. Wind power density estimation is required to determine the wind potential. In this study, the wind power density was estimated by using artificial neural network (ANN) method. Forty meteorological stations were used for ANN training, while eighteen meteorological stations were used to test the trained network. Network has trained according to, respectively; trainlm, trainbfg, trainscg, traincgp traincgb, traincgf ve trainoss learning algorithms. The correlation coefficient (R) and Mean bias error (MBE) of the best developed model were calculated as 0,9767 and -0,3124 W/m2 respectively. Root Mean Square Error (RMSE) was calculated as 1,4786 W/m2. In conclusion, the obtained results demonstrate that the developed model can be used to estimate the wind power density.
Calculation of solar radiation by using regression methodsmehmet şahin
Abstract. In this study, solar radiation was estimated at 53 location over Turkey with
varying climatic conditions using the Linear, Ridge, Lasso, Smoother, Partial least, KNN
and Gaussian process regression methods. The data of 2002 and 2003 years were used to
obtain regression coefficients of relevant methods. The coefficients were obtained based on
the input parameters. Input parameters were month, altitude, latitude, longitude and landsurface
temperature (LST).The values for LST were obtained from the data of the National
Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer
(NOAA-AVHRR) satellite. Solar radiation was calculated using obtained coefficients in
regression methods for 2004 year. The results were compared statistically. The most
successful method was Gaussian process regression method. The most unsuccessful method
was lasso regression method. While means bias error (MBE) value of Gaussian process
regression method was 0,274 MJ/m2, root mean square error (RMSE) value of method was
calculated as 2,260 MJ/m2. The correlation coefficient of related method was calculated as
0,941. Statistical results are consistent with the literature. Used the Gaussian process
regression method is recommended for other studies.
Estimation of solar radiation by different machine learning methodsmehmet şahin
In this study, solar radiation was estimated depend on 34 locations. While doing distribution
of the locations on Turkey, they were taken into consideration to be different climatic
conditions. In the study, meteorological data was used between the years of 2007 and 2015. The
meteorological data which were used, were land surface temperature at 5 cm, air temperature,
sunshine duration and solar radiation. As geographical data, locations of latitude, longitude
and altitude were used. Three different methods were used for estimating the solar radiation.
These are the methods of multilayer perceptron neural network, multiple regression and radial
basis function network. The estimation results obtained from the methods were compared with
the actual values obtained from the meteorological station, statistically. The mean bias error
(MBE), root mean square error (RMSE) and correlation coefficient (R) were used as evaluation
criteria. According to statistical results, multilayer perceptron neural networks has given the
most successful result in the three methods. The MBE, RMSE and R values of method were
calculated as 1.5321 MJ/m2, 2.4295 MJ/m2, and 0.9428, respectively. It is recommended to
researchers to use multilayer perceptron neural networks method for studies in this area.
Determination of wind energy potential of campus area of siirt universitymehmet şahin
In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
Forecasting long term global solar radiation with an ann algorithmmehmet şahin
and energy-efficient buildings, solar concentrators, photovoltaic-systems and a site-selection of sites for future
power plants). To establish long-term sustainability of solar energy, energy practitioners utilize versatile
predictive models of G as an indispensable decision-making tool. Notwithstanding this, sparsity of solar sites,
instrument maintenance, policy and fiscal issues constraint the availability of model input data that must be
used for forecasting the onsite value of G. To surmount these challenge, low-cost, readily-available satellite
products accessible over large spatial domains can provide viable alternatives. In this paper, the preciseness of
artificial neural network (ANN) for predictive modelling of G is evaluated for regional Queensland, which
employed Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) as an
effective predictor. To couple an ANN model with satellite-derived variable, the LST data over 2012–2014 are
acquired in seven groups, with three sites per group where the data for first two (2012–2013) are utilised for
model development and the third (2014) group for cross-validation. For monthly horizon, the ANN model is
optimized by trialing 55 neuronal architectures, while for seasonal forecasting, nine neuronal architectures are
trailed with time-lagged LST. ANN coupled with zero lagged LST utilised scaled conjugate gradient algorithm,
and while ANN with time-lagged LST utilised Levenberg-Marquardt algorithm. To ascertain conclusive results,
the objective model is evaluated via multiple linear regression (MLR) and autoregressive integrated moving
average (ARIMA) algorithms. Results showed that an ANN model outperformed MLR and ARIMA models
where an analysis yielded 39% of cumulative errors in smallest magnitude bracket, whereas MLR and ARIMA
produced 15% and 25%. Superiority of an ANN model was demonstrated by site-averaged (monthly) relative
error of 5.85% compared with 10.23% (MLR) and 9.60 (ARIMA) with Willmott's Index of 0.954 (ANN), 0.899
(MLR) and 0.848 (ARIMA). This work ascertains that an ANN model coupled with satellite-derived LST data
can be adopted as a qualified stratagem for the proliferation of solar energy applications in locations that have
an appropriate satellite footprint.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
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Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
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Estimation of the vapour pressure deficit using noaa avhrr data
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Estimation of the vapour pressure
deficit using NOAA-AVHRR data
Mehmet Şahin
a
, Bekir Yiğit Yıldız
b
, Ozan Şenkal
c
& Vedat
Peştemalci
d
a
Engineering Faculty, Siirt University, Siirt, Turkey
b
Karaisalı Vocational School, Çukurova University, Adana, Turkey
c
Faculty of Education, Department of Computer Education and
Instructional Technology, Çukurova University, Adana, Turkey
d
Department of Physics, Çukurova University, Adana, Turkey
Version of record first published: 15 Jan 2013.
To cite this article: Mehmet Şahin , Bekir Yiğit Yıldız , Ozan Şenkal & Vedat Peştemalci (2013):
Estimation of the vapour pressure deficit using NOAA-AVHRR data, International Journal of Remote
Sensing, 34:8, 2714-2729
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3. International Journal of Remote Sensing 2715
VPD affects plants. For example, the water stress index was recently developed using
VPD by employing a radiometric measurement of foliage temperature and a psychometric
measurement of air VPD. It is necessary to find the relationship between the foliage-air
temperature differential and the air VPD for plants (Idso 1982). Strawberry plants were
monitored for water stress by measuring the foliage temperature with a hand-held infrared
thermometer. In addition to the foliage temperature measurement, weather variables, the
difference between leaf and air temperature, derived crop water stress index, soil matric
potential, leaf water potential, photosynthetic gas exchange rates, transpiration rates, photo-
synthetic pigments, sugars, starch, canopy structure, and accumulated yield were measured.
A regression analysis showed that the air VPD contributed significantly to variations in leaf
water potential under very mild water stress conditions tested in a wet treatment. However,
the contribution was not significant under mild water stress of dry treatment (Peñuelas et al.
1992). Leonardi, Guichard, and Bertin (2000) investigated how a high VPD influences the
growth, transpiration, and quality of tomato fruits. In this study, plants were grown in two
glasshouse compartments under two VPD levels: a low VPD was obtained by increasing
air humidity with a fogging system, and a high VPD was obtained during sunny hours in
a greenhouse where the air humidity was not controlled. The mean value of the six driest
hours of the day during the considered growing period of the fruits was 16 mb under low
VPD and 22 mb under high VPD conditions. The study showed that as VPD increases from
16 to 22 mb during summer, effects can be observed on both tomato growth and quality.
During daylight hours, the relative fruit growth rate was significantly reduced for plants
grown under higher VPDs. The same trend was not observed at lower VPDs, where fruit
growth varied more regularly during daylight hours. Habermann et al. (2003) found that
healthy sweet orange plants measured at a VPD of 25 mb showed a 50% decrease in the
transpiration rate and an 80% decrease in stomatal conductance when compared to mea-
surements at 12 mb. The amount of proportion between photosynthesis and transpiration
rates and stomatal conductance of leaves from healthy plants was measured at both VPDs.
Reducing VPD has no long-term effect on the growth of temperate and tropical
rainforest trees. Therefore, large reductions in stomatal conductance and net photosyn-
thesis were measured with increasing VPDs in tropical rainforest trees. Earlier studies did
not show significant reductions in growth (Cunningham 2004, 2005). Possible explana-
tions for the lack of effect of VPD on growth include avoidance of water stress due to
adequate soil moisture, increased nutrient uptake under ambient conditions due to higher
transpiration rates, and other factors such as temperature and light, and limiting the growth
of tropical species. In temperate rainforests, unlike in this experiment, higher VPD val-
ues in summer were associated with a limited water supply. Therefore, a more important
difference between temperate and tropical rainforest trees is the ability of the temperate
species to tolerate a mild soil drought. Temperate rainforest trees may possess adaptations
such as deeper, more extensive root systems and increased resistance to xylem cavitation
and osmotic adjustments that allow them to maintain photosynthesis and growth for longer
periods under low-soil moisture than wet, tropical rainforest trees (Cunningham 2006).
Williams and Baeza (2007) studied Vitis vinifera grown at different locations to deter-
mine the relationship among temperature, VPD, and leaf water potential under clear skies
during midday. Temperature and VPD were determined at the time of measurement. The
highest and lowest leaf water potential values measured on well-watered grapevines were
51.102
and 115.102
mb, respectively. According to the results, the leaf and stem water
potentials were linearly related to VPD. The coefficient of determination was greater for
the relationship between the leaf water potential and VPD (r2
= 0.74) than temperature and
VPD (r2
= 0.58). The leaf water potential and stem water potential values as a function
Downloadedby[SiirtUniversitesi]at00:4116January2013
4. 2716 M. ¸Sahin et al.
of VPD or temperature could serve as baselines, indicating whether the grapevines are
fully irrigated or are not water stressed under the environmental conditions found in semi-
arid grape-growing regions. Lendzion and Leuschner (2008) studied the effects of elevated
atmospheric water VPD on the European beech. They tested the hypothesis that an increas-
ing VPD negatively affects the growth and development of European beech saplings. Their
results show that the beech sapling growth and development strongly depend not only
on soil moisture but also on the prevailing VPD level. Glenn et al. (2008) found that
transpiration highly depended on the leaf area and was controlled by VPD in the atmo-
sphere. Sarcobatus vermiculatus tended to have higher transpiration rates than Atriplex
canescens and had a steeper response to VPD.
VPD is one of the critical variables that drives evapotranspiration (ET) and is funda-
mentally important to many models. The estimation of VPD can be used in several ET
estimation equations that are used to estimate regional ET patterns in which only tem-
perature, precipitation, and insolation measurements are available (Castellvi et al. 1996,
1997). Furthermore, VPD is one of the key controls in opening the stomata in plants and
is thus an important force for ET, plant respiration, biomass production, and the uptake of
harmful pollutants such as ozone through the stomata (Andersson-Sköld, Simpsonb, and
Ødegaard 2008). To survive in adverse environments subject to drought, high-salt con-
centration, or low temperature, some plants seem to be able to synthesize biochemical
compounds, including proteins, in response to changes in water activity or osmotic pres-
sure. Water activity or osmotic pressure measurements of simple aqueous solutions have
been based on freezing point depression or VPD. Osmotic pressure measurements of plants
under water stress have been mainly based on VPD (Kiyosawa 2003).
As observed in recent studies, VPD plays an important role in various fields.
Additionally, it was estimated using meteorological stations that are limited in the regions
studied. For these reasons, new methods to estimate VPD are necessary. One of these meth-
ods uses satellite data. Choudhury (1998) estimated VPD over land surfaces from satellite
observations. This study presented a method for estimating the monthly mean VPD from
satellite observations and then evaluated the accuracy of the estimated values by compar-
ing them with globally distributed ground measurements. The square of the correlation
coefficient and standard error of estimation were found to be 0.85 and 4 mb, respec-
tively. Prince et al. (1998) conducted a study to obtain VPD and then compared the values
with NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very
High Resolution Radiometer) and field observations. The comparison resulted in a VPD
root mean square error (RMSE) of 10.9 mb over a range of 58 mb. Hay and Lennon
(1999) studied meteorological variables and control of vector-borne disease across Africa
and compared remote sensing and climate spatial interpolation using VPD obtained
from the NOAA/AVHRR data set. According to the result, the mean accuracy for the
year was an RMSE of 6 mb (range 4.91–6.43) with a mean adjusted r2
= 0.63 (range
0.40–0.71) and an RMSE of 5.3 mb (range 3.65–7.64) with a mean adjusted r2
= 0.78
(range 0.67–0.86). Hashimoto et al. (2008) developed simple linear models to predict VPD
using saturated vapour pressure calculated from MODIS (Moderate Resolution Imaging
Spectroradiometer) – LST (land surface temperature) at a number of different temporal and
spatial resolutions. They estimated model parameters for VPD estimation both regionally
and globally with RMSE values ranging from 3.2 to 3.8 mb. VPD was overestimated along
coastlines and underestimated in arid regions with low-vegetation cover. Additionally, the
residuals were larger with higher VPDs because of the non-linear relationship between sat-
uration vapour pressure and LST. Linear relationships were observed at multiple scales and
appeared useful for estimation within the range 0–25 mb.
Downloadedby[SiirtUniversitesi]at00:4116January2013
5. International Journal of Remote Sensing 2717
In this study, land surface temperature (Ts), total precipitable water in the atmospheric
column (U), and dew point temperature (Td) were calculated to estimate VPD using satel-
lite data. Meanwhile, VPD was estimated using meteorological data. Then, the results were
statistically compared.
2. Data
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.
Land meteorological values were necessary to determine whether the VPD estimates
obtained from the satellites are indeed adequate. To achieve this, air temperature and vapour
pressure (VPair) values were received from the Turkish State Meteorological Service.
3. Estimation of land-surface temperature
Land-surface temperature is important because it is one of the key factors in determin-
ing the exchange of energy and matter between the Earth’s surface and atmosphere.
Simultaneously, it is an important measurement for energy-balance applications and can
be especially useful when determined by thermal infrared remote sensing (Seguin and
Itier 1983). An approach based on the differential absorption in two adjacent infrared
channels, called the ‘Split-Window’ technique, is used for determining the surface temper-
ature. The AVHRR channels 4 and 5 are widely used for deriving the surface temperature
(Kant and Badarinath 2000). Many algorithms have been proposed by Price (1984), Becker
(1987), Becker and Li (1990), Vidal (1991), Prata (1993, 1994), Sobrino et al. (1994,
1996), Coll et al. (1994), Becker and Li (1995), Coll and Caselles (1997), Ouaidrari et al.
(2002), Pinheiro et al. (2006), and Katsiabani, Adaktilou, and Cartalis (2009). These stud-
ies indicated that it is possible to retrieve LST at a reasonable accuracy (RMSE of 1–3 K)
from current operational and research satellite-borne visible/infrared radiometers. In this
study, the Price (1984), Becker and Li (1990), Vidal (1991), and Ulivieri et al. (1994)
split-window algorithm techniques were used to obtain land-surface temperatures.
3.1. Price (1984) algorithm
By reducing the effect of atmosphere and using radioactive transfer theory, Price (1984)
developed a split-window algorithm technique that has been used extensively. The basic
split-window algorithm can be written as
Ts = T4 + a(T4 − T5) + b, (1)
where coefficients a and b account for atmospheric conditions (related to spectral radi-
ance and transmission) and surface emissivity, respectively. However, linear empirical
formulations do not always hold. Hence, the water vapour dependence was subsequently
incorporated into a non-linear quadratic equation (Coll et al. 1994; François and Ottle
1996). Coefficient a in Equation (1) was given by a = ((a5/a4) – 1)−1
, where a5/a4 was
determined from T5 ( T4)−1
(the brightness temperature spatial variations in AVHRR
Downloadedby[SiirtUniversitesi]at00:4116January2013
6. 2718 M. ¸Sahin et al.
channels 4 and 5) for the small study area. The a5/a4 value was calculated to be 1.30,
a = 3.33, and b was linked to the emissivity difference. The emissivity difference, ε =
ε4 − ε5 = –0.005, and ε depend on ε4 and ε5 as in the relation, ε =
ε4 + ε5
2
= 0.975
(Caselles, Coll, and Valor 1997; Chrysoulakis and Cartalis 2002), where ε4 and ε5 are
the emissivities of channels 4 and 5; and T4 and T5 are the brightness temperatures of
NOAA/AVHRR channels 4 and 5, respectively (Dash et al. 2002). The final form of the
equation is
Ts = [T4 + 3.33 (T4 − T5)]
5.5 − ε4
4.5
− 0.75T5 ε. (2)
3.2. Becker and Li (1990) algorithm
Based on radioactive transfer theory and numerical simulations, Becker and Li (1990)
proposed a local split-window algorithm for AVHRR channels 4 and 5:
Ts = 1.274 + P
T4 + T5
2
+ M
T4 − T5
2
, (3)
where temperature is in K, and the coefficients P and M are given by
P = 1 + 0.15616
1 − ε
ε
− 0.482
ε
ε2
, (4)
M = 6.26 + 3.98
1 − ε
ε
+ 38.33
ε
ε2
, (5)
where P and M are local coefficients that depend on the surface emissivity, but are indepen-
dent of atmospheric effects. ε = ε4 − ε5 = –0.005, and ε = 0.975 (Caselles, Coll, and
Valor 1997; Chrysoulakis and Cartalis 2002). The coefficient 1.274 in Equation (3) was
calculated from numerical simulations and local atmospheric effects (Becker and Li 1990).
3.3. Vidal (1991) algorithm
Ts = T4 + 2.78 (T4 − T5) + 50
1 − ε
ε
− 300
ε
ε
. (6)
The coefficients related to the emissivity in this algorithm were obtained from a study by
Becker (1987). ε = ε4 − ε5 = –0.005, and ε = 0.975 (Caselles, Coll, and Valor 1997;
Chrysoulakis and Cartalis 2002). This algorithm was generated from a large number of
satellite data and land-surface temperature calculations (Vidal 1991).
3.4. Ulivieri et al. (1994) algorithm
This algorithm was developed by Ulivieri et al. (1994) for its simplicity, robustness, and
superior performance in independent tests. Becker and Li (1995) and Vazquez, Reyes, and
Arboledas (1997) tested the algorithm with different data sets and different split-widow
algorithms. In all cases, the Ulivieri et al. (1994) algorithm performed well. The Ulivieri
et al. algorithm can be written as
Downloadedby[SiirtUniversitesi]at00:4116January2013
7. International Journal of Remote Sensing 2719
Ts = T4 + 1.8 (T4 − T5) + 48(1 − ε) − 75 ε, (7)
where ε = ε4 – ε5 = –0.005 and ε = 0.975 (Caselles, Coll, and Valor 1997; Chrysoulakis
and Cartalis 2002). This equation was developed for cases of column atmospheric water
vapour less than 3 g cm−2
, a reasonable condition for many of the semi-arid areas of
continental Africa (Pinheiro et al. 2006).
4. Estimation of VPD
Generally, researchers use two sources to find VPD: meteorological station data and satellite
data. Therefore, these two sources were used in this study to perform comparison.
4.1. Estimation of VPD using meteorological station data
The vapour pressure (VPair) is a measure of how much water vapour is in the air, i.e. how
much water in the gas phase is present in the air. The presence of more water vapour in the
air leads to a greater water vapour pressure. When the air reaches its maximum water con-
tent, the vapour pressure is called the saturation vapour pressure (VPsat), which is directly
related to temperature. Thus, the difference between the saturation vapour pressure and
the real air vapour pressure is called VPD. The magnitude of VPD gives an indication
of how close the air is to condensation (Choudhury 1998; Prenger and Ling 2000). Very
simply, VPD is a measure of the lack of moisture equilibrium between an object and the
surrounding atmosphere (Hay and Lennon 1999). VPD is given by Unwin (1980) as
VPD = VPsat − VPair, (8)
where the saturation vapour pressure, VPsat (mb), is given by
log10 VPsat = 9.24349 −
2305
Tair
−
500
T2
air
−
100000
T3
air
, (9)
where Tair is the air temperature in Kelvin.
4.2. Estimation of VPD using satellite data
Determining VPD, and the difference between saturated and actual atmospheric vapour
pressures, involves estimating the precipitable water in the atmospheric column using
the thermal infrared channels 4 and 5 of AVHRR, from which the surface humidity is
derived. The total precipitable water in the atmospheric column, U (kg m−2
), is estimated
as Equation (10) (Eck and Holben 1994):
U = A + B (T4 − T5) , (10)
where A and B are constants equal to 1.337 and 0.837, respectively. The total precipitable
water is expressed in units of pressure and is converted to the amount of water in centime-
tres that would be precipitated from the atmospheric column by dividing by 10, because the
density of water is 1 g cm−3
. The estimated precipitable water content U is used to obtain
the surface dew temperature Td (◦
F); surface dew temperature can be calculated using the
following equation (Smith 1966):
Downloadedby[SiirtUniversitesi]at00:4116January2013
8. 2720 M. ¸Sahin et al.
Td(◦
F) =
ln U − (0.113 − ln(λ + 1))
0.0393
. (11)
The λ values given by Smith for different latitudinal zones were used. In this analysis, a
mean value of λ = 2.99 was calculated from the annual mean λ presented by Smith for
locations between 0 and ±40 degrees of latitude. Then, the Td values should be converted
into kelvin (Smith 1966).
Finally, VPD in kilopascals (kPa) can be calculated from Td and Ts, as given by
Equation (12), following Prince and Goward (1995):
VPD = 0.611 exp 17.27
Ts − 273
Ts − 36
− exp 17.27
Td − 273
Td − 36
. (12)
5. Evaluation of the estimation results
The choice of the relevant criteria allowing the estimation methods’ performance evalua-
tion is an important issue. Various statistical parameters can be used to measure the strength
of the statistical relationship between the estimated and reference values. We assume that vi
(i = 1, n) is the set of n reference values and ei (i = 1, n) is the set of estimates; ¯v and ¯e are
mean reference and estimate value, respectively. The bias, linear correlation coefficient (r),
and RMSE can be calculated using the standard deviations of the reference (σv) and esti-
mate (σe) values, means of the reference and estimate values, and estimated and reference
values. The bias is the difference between the mean estimate ¯e and the mean reference value
¯v. The statistical criterion formula of the linear correlation coefficient r is the following:
r =
n
i=1 (vi − ¯v) (ei − ¯e)
nσvσe
, (13)
where r measures the proximity between estimate and reference and is not sensitive to a
bias (Kendall and Stuart 1963). The formula of RMSE is
RMSE =
1
n
n
i=1
(ei − vi)2
1
2
. (14)
In statistics, RMSE is a frequently used measure of the differences between values pre-
dicted by a model or an estimator and the values actually observed from the subject being
modelled or estimated (Laurent, Jobard, and Toma 1998).
6. Results
6.1. Land-surface temperature
After obtaining brightness temperatures of NOAA/AVHRR channels 4 and 5, split-window
algorithms were used to obtain the land-surface temperature. The Price (1984) algorithm
was calculated first using Equation (2) (see Figure 1). Then, the Becker and Li (1990), Vidal
(1991), and Ulivieri et al. (1994) algorithms were calculated using Equations (3), (6), and
(7), respectively (see Figures 2–4). When the maps in Figures 1–4 were examined through
image-processing programs, the land-surface temperature from the Price (1984) algorithm
was measured at a minimum of 299 K, maximum of 310 K, and an average of 302.75 K.
Downloadedby[SiirtUniversitesi]at00:4116January2013
9. International Journal of Remote Sensing 2721
293.88
<273.57
276.47
279.37
282.27
285.18
288.08
290.98
296.78
299.69
302.59
305.49
308.39
311.29
314.20
317.10
320.00
N
Figure 1. Map of land-surface temperature obtained as based on the Price (1984) algorithm at
06.51 local time on 6 July 2002 (K).
N
<273.00
275.94
281.81
284.75
290.63
293.56
296.50
299.44
302.38
305.31
308.25
311.19
314.13
317.06
320.00
287.69
278.88
Figure 2. Map of land-surface temperature obtained as based on the Becker and Li (1990) algorithm
at 06.51 local time on 6 July 2002 (K).
In the Becker and Li (1990) algorithm, the minimum value was determined to be 296.91 K,
the maximum was 307.78 K, and the average was 301.16 K; in the Vidal (1991) algorithm,
the minimum value was found to be 299.28 K, the maximum was 309.30 K, and the average
was 302.84 K; in the Ulivieri et al. (1994) algorithm, the minimum value was calculated as
297.56 K, the maximum was 307.47 K, and the average was 300.83 K.
Accordingly, the land-surface temperature calculation was completed using
24 NOAA/AVHRR satellite images for each split-window algorithm. The values obtained
from the split-window algorithms had to be evaluated with the meteorological data from
chosen control point cities on Turkey’s map. Therefore, it was important to choose the
cities on the map by taking into consideration Turkey’s different geographical regions and
at least one city from each region had to be included. The cities were chosen according to
the geographical regions of Turkey and the city locations on the map (see Figure 5).
As observed on the map, Adana, Ankara, Antalya, Balıkesir, Denizli, Erzurum, ˙Izmir,
Kayseri, Malatya, Samsun, Sivas, Rize, and Van were used as the control points to
Downloadedby[SiirtUniversitesi]at00:4116January2013
10. 2722 M. ¸Sahin et al.
N
<273.00
320.00
275.94
281.81
284.75
290.63
293.56
296.50
299.44
302.38
305.31
308.25
311.19
314.13
317.06
287.69
278.88
Figure 3. Map of land-surface temperature obtained as based on the Vidal (1991) algorithm at
06.51 local time on 6 July 2002 (K).
N
<273.00
320.00
275.94
281.81
284.75
290.63
293.56
296.50
299.44
302.38
305.31
308.25
311.19
314.13
317.06
287.69
278.88
Figure 4. Map of land-surface temperature obtained as based on the Ulivieri et al. (1994) algorithm
at 06.51 local time on 6 July 2002 (K).
determine land-surface temperature accuracy. The minimum, maximum, and average
values of land-surface temperature obtained from control points were compared to
meteorological values on a monthly basis (see Table 1). Although the averages of the
meteorological and algorithmic values in January were the same (281.05 K), the land-
surface temperature values ranged from 268.70 to 293.21 K. In February, the average of
the meteorological values was 285.78 K, and the nearest estimate was 288.94 K, which
was from Ulivieri et al. (1994). The four algorithms gave results that were somewhat
similar to the meteorological values in March, October, and December. The average of
the meteorological values in April was 283.95 K, and the closest estimates belonged to
the Becker and Li (1990) and Ulivieri et al. (1994) algorithms. In May, the meteoro-
logical values were 270.20–282.30 K, and the average was 291.60 K. The best results
in terms of the algorithm average values belonged to Ulivieri et al. (1994) and Becker
and Li (1990) with 283.34 and 283.63 K, respectively. In June and July, the Ulivieri
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11. International Journal of Remote Sensing 2723
Kayseri
Samsun
Ankara
Antalya
Denizli
Afyonkarahisar
izmir
Bahkesir
Istanbul
Konya
Adana
Malatya
Sivas
Rize
Artvin
Van
Kars
Erzurum
Figure 5. Locations of the cities used to estimate land-surface temperature and VPD.
et al. (1994) algorithm gave the results most consistent with the meteorological val-
ues. In August, the average error values of the Becker and Li (1990) and Ulivieri
et al. (1994) algorithms were approximately 2 K compared to the meteorological val-
ues. In September, the average error temperature values of all algorithms had a deviation
ratio with the meteorological values in the range of 0.07–1.9 K. In November, the clos-
est value to the meteorological value was from the Vidal (1991) and Ulivieri et al. (1994)
algorithms.
Additionally, statistical evaluation was performed by considering satellite and meteoro-
logical data with Equations (13) and (14). According to the evaluation result, the correlation
coefficients (r) were found to be 0.958, 0.961, 0.967, and 0.970 according to Price (1984),
Becker and Li (1990), Ulivieri et al. (1994), and Vidal (1991), respectively (see Figure 6).
The correlation coefficient results show a strong relationship between the satellite and
meteorological data.
The other statistical result was the RMSE values of the algorithms. The RMSE values
ranged from 2.7 K, which was calculated using the Ulivieri et al. (1994) algorithm, to nearly
4 K, which was calculated from the Price (1984) algorithm. The algorithm with the smallest
RMSE value was that of Ulivieri et al. (1994); thus, this algorithm is suggested to estimate
the land-surface temperature among the Price (1984), Becker and Li (1990), Vidal (1991),
and Ulivieri et al. (1994) algorithms.
6.2. Vapour pressure deficit
Two approaches (the first for the meteorological data, and the second for the satellite data)
were followed to estimate VPD. The VPD values for the meteorological data were cal-
culated using Equations (8) and (9), whereas the VPD values for the satellite data were
calculated using Equations (7), (10), (11), and (12) over the satellite images (see Figure 7).
When Figure 7 was examined through an image-processing program, it was observed
that VPD values were in the ranges 0–10 mb and 10–20 mb, which were considerably
low. Moreover, the VPD values in the range 20–30 mb were rather frequent over Turkey.
It was found that the VPD values were between 30 and 40 mb over the following regions
of Turkey: Central Anatolia, South-Eastern, Mediterranean, and the coastal lines of the
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12. 2724 M. ¸Sahin et al.
Table 1. Min, max, and average temperature values of meteorological and algorithm data (K).
Month
Minimum/
maximum/
average (K)
Meteorological
value
Ulivieri et al.
(1994)
Becker and
Li (1990)
Vidal
(1991) Price (1984)
Min 271.40 268.70 269.69 271.10 270.00
January Max 289.40 291.19 291.39 293.21 293.00
Ave 281.05 281.05 281.05 281.05 281.05
Min 277.90 282.40 283.49 284.49 284.00
February Max 291.60 293.32 293.79 295.65 296.00
Ave 285.78 288.94 289.15 290.85 290.69
Min 270.20 267.50 267.63 268.85 268.00
March Max 289.30 289.77 289.58 291.14 290.00
Ave 283.78 283.46 283.37 284.82 283.85
Min 276.20 277.07 277.05 278.43 277.00
April Max 287.00 290.11 290.53 293.28 292.00
Ave 283.95 286.59 286.85 287.93 288.21
Min 270.20 267.50 267.63 268.85 268.00
May Max 291.60 293.32 293.79 295.65 296.00
Ave 282.30 283.34 283.63 285.07 284.48
Min 290.40 293.61 295.00 295.00 296.00
June Max 302.60 305.00 305.19 307.00 306.00
Ave 298.26 300.51 301.17 302.30 301.38
Min 293.60 297.56 296.91 299.06 299.00
July Max 303.50 307.47 308.92 309.84 310.00
Ave 298.04 300.73 301.62 302.48 303.01
Min 294.30 296.96 296.67 297.82 298.00
August Max 315.00 315.35 311.89 316.56 315.00
Ave 300.49 302.68 302.19 304.34 304.04
Min 283.40 284.26 284.22 285.03 285.00
September Max 308.00 304.38 304.22 307.47 306.00
Ave 293.19 293.26 293.83 295.08 295.09
Min 280.50 277.99 277.83 279.31 279.00
October Max 292.00 292.26 291.41 292.37 293.00
Ave 285.66 285.14 284.84 286.10 286.48
Min 273.40 269.70 260.92 271.00 260.00
November Max 284.00 285.81 282.67 284.70 285.00
Ave 276.57 274.35 272.37 275.24 273.10
Min 270.40 269.00 269.86 271.54 271.00
December Max 282.00 280.00 280.41 281.94 282.00
Ave 275.93 274.20 274.90 276.52 276.17
Aegean Sea. The VPD values in Central Anatolia and South-Eastern being between 30 and
40 mb was attributed to the heating weather and, because of that heat, deficit humidity in
the atmosphere. Although enough water was present in the regions of the Mediterranean
and Aegean Sea coastal line, the cause of the VPD values being between 30 and 40 mb
was the heating weather in the early times of the day and, in spite of the humidity holding
capacity increase, there was not enough evaporation due to the lack of warming of the sea-
water. Even if the VPD values between 50 and 80 mb were not frequently observed over
Turkey, these rates were observed frequently over Iraq and Syria. In a similar way, VPD
values were calculated over all 24 satellite images.
Then, the cities of Adana, Ankara, Afyonkarahisar, Artvin, Antalya, Balıkesir, Denizli,
Erzurum, Eski¸sehir, ˙Istanbul, ˙Izmir, Kars, Kayseri, Konya, Malatya, Samsun, Sivas,
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13. International Journal of Remote Sensing 2725
320
(a) (b)
(c) (d)
320
310
310
300
300
290
290
Meteorologic temperature (K)
280
280
270
270
Temperature(K)
r = 0.958
Meteorologic temperature (K)
320
310
300
290
280
270
320310300290280270
Temperature(K)
r = 0.961
Meteorologic temperature (K)
320
310
300
290
280
270
320310300290280270
Temperature(K)
r = 0.967
Meteorologic temperature (K)
320
310
300
290
280
270
320310300290280270
Temperature(K)
r = 0.970
Figure 6. Correlation coefficients of the algorithms. (a) Price (1984), (b) Becker and Li (1990),
(c) Vidal (1991), and (d) Ulivieri et al. (1994) algorithms.
0 10 20 30 40 50 60 70 80 (mb)
Figure 7. Map of VPD obtained at 06.51 local time on 6 July 2002 (mb).
¸Sanlıurfa, Rize, and Van were chosen as control points for the satellite prediction accuracy
(see Figure 5).
Upon examining the monthly average VPD values at the control points, the following
were found.
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14. 2726 M. ¸Sahin et al.
100
90
90
80
80
70
70
60
60
50
50
40
40
30
30
Meteorologic VPD (mb)
r = 0.957
SatelliteVPD(mb)
20
20
10
10
0
0
Figure 8. Correlation coefficient of VPD.
• In January, the meteorological and satellite values were 1.9 and 4.2 mb, respectively.
• In February, the meteorological and satellite values were 6.35 and 8.72 mb,
respectively.
• In March, the meteorological and satellite values were 3.16 and 8.23 mb, respectively.
• In April, the meteorological and satellite values were 17 and 20.01 mb, respectively.
• In May, the meteorological and satellite values were 28.33 and 30.91 mb, respectively.
• In June, the meteorological and satellite values were 52.73 and 53.62 mb, respectively.
• In July, the meteorological and satellite values were 50.64 and 50.16 mb, respectively.
• In August, the meteorological and satellite values were 43.96 and 46.89 mb,
respectively.
• In September, the meteorological and satellite values were 26.19 and 28.29 mb,
respectively.
• In October, the meteorological and satellite values were 11.95 and 15.21 mb,
respectively.
• In November, the meteorological and satellite values were 2.49 and 5.45 mb,
respectively.
• In December, the meteorological and satellite values were 3.22 and 3.38 mb,
respectively.
Equations (13) and (14) tested the satellite prediction accuracy by calculating the cor-
relation coefficient (r) and RMSE. While the correlation coefficient on a monthly average
basis was 0.991, the RMSE value was 2.67 mb. When the meteorological and satellite VPD
values were not measured on a monthly average basis but were directly compared, the cor-
relation coefficient amongst the VPD values was found to be 0.957, and the value of RMSE
was 5.665 mb (see Figure 8). According to statistical rules, the VPD RMSE value can be
written as 6 mb instead of 5.665 mb.
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15. International Journal of Remote Sensing 2727
7. Discussion and conclusion
In the literature, Price (1984), Becker (1987), Becker and Li (1990, 1995), Vidal (1991),
Prata (1993, 1994), Sobrino et al. (1994, 1996), Coll et al. (1994), and Caselles, Coll, and
Valor (1997) attempted to obtain LST at reasonable accuracies (RMSE of 1–3 K) from
current operational and research NOAA/AVHRR satellite-borne visible/infrared radiome-
ters. In our study, the accuracies of split-window algorithms resulted in an average RMSE
value of 3 K (range 2.733–3.731 K). The Ulivieri et al. (1994) algorithm was found to be
very successful compared to studies from the literature. Because of this result, the Ulivieri
et al. (1994) algorithm was used to estimate the VPD formula. The VPD accuracy was
determined by the RMSE value and the correlation coefficient, which were calculated to
be 6 mb and 0.957, respectively. Furthermore, on a monthly average basis, while the VPD
correlation coefficient was found to be 0.991, RMSE was found to be 2.67 mb. These val-
ues are rather compatible with studies from the literature, which range between 3.2 mb and
10.9 mb.
As a result, we conclude that the VPD values obtained using satellite data can be used
in studies related to plants (germination, growth, and harvest), outbreak control of illness,
drought determination, and ET over wide areas in which the meteorological station network
density is normally not sufficient.
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