Air temperature retrieval from remote sensing data based on thermodynamics

Sun, Yuhan; Wang, Jinfeng; Zhang, R.-H.; Gillies, Robert R.; Xue, Yong; Yuan, Bo

doi:10.1007/s00704-004-0079-y

Cited by 148 publications

(81 citation statements)

References 22 publications

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“…On the basis of 51 samples, the accuracy of the model as given by the coefficient of determination was equal to 0.80, while in the case of Poznań wherein 15 imageries were used, the accuracy was slightly higher (0.84). In a study conducted by Wloczyk et al (2011) based on Landsat 7ETM+ for more homogenous green areas, the coefficients of determination were slightly higher (r 2 = 0.89), with an average root mean square error of about 3 K. The same order of accuracy was obtained in a research based on MODIS thermal products conducted by Sun et al (2005), wherein over 80 % of the analyzed samples had an accuracy of 3 K. A strong relationship between LST (derived from Landsat TM) and air temperature was also found in a recent study by Martin et al (2015) for Montreal and by Klok et al (2012) for Rotterdam, which obtained coefficients of determination of up to 0.81.…”

Section: Discussionsupporting

confidence: 61%

“…Voogt and Oke (2003) pointed out that the UHI observed from thermal remote sensing data is none other than the surface urban heat island (SUHI). In the North China Plane, an attempt at air temperature retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) data was made by Sun et al (2005). The coefficient of determination of the linear model in predicting the air temperature based on MODIS data is about 0.80.…”

Section: Introductionmentioning

confidence: 99%

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The urban heat island in the city of Poznań as derived from Landsat 5 TM

Majkowska

Kolendowicz

Półrolniczak

et al. 2016

Theor Appl Climatol

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To study urban heat island (UHI), Landsat 5 TM data and in situ measurements of air temperature from nine points in Poznań (Poland) for the period June 2008-May 2013 were used. Based on data from measurement points located in different types of land use, the surface urban heat island (SUHI) maps were created. All available and qualitycontrolled Landsat 5 TM images from 15 unique days were used to obtain the characteristics of land surface temperature (LST) and UHI intensity. In addition, spatial analysis of UHI was conducted on the basis of Corine Land Cover 2006 dataset. In situ measurements at a height of 2 m above ground level show that the UHI is a common occurrence in Poznań with a mean annual intensity of 1.0°C. The UHI intensity is greater during the warm half of the year. Moreover, results based on the remote sensing data and the Corine Land Cover 2006 indicate that the highest value of the mean LST anomalies (3.4°C) is attained by the continuous urban fabric, while the lowest value occurs within the broad-leaved forests (−3.1°C). To re-count from LST to the air temperature at a height of 2 m above ground level (T agl ), linear and non-linear regression models were created. For both models, coefficients of determination equal about 0.80, with slightly higher value for the non-linear approach, which was applied to estimate the T agl spatial variability over the city of Poznań.

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Section: Discussionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

The urban heat island in the city of Poznań as derived from Landsat 5 TM

Majkowska

Kolendowicz

Półrolniczak

et al. 2016

Theor Appl Climatol

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“…TVX approach has usually a RMSE of 3-4˝C for AVHRR data [23,36]. Sun et al [39] employed a surface energy balance approach and applied it for a region located in the North China Plain for two MODIS datasets (deviations smaller than 3˝C for 80% of the cases). Ho et al [33] used an advanced statistical approach to map Vancouver's (Canada) maximum urban TA using Landsat data with a RMSE of 2.3˝C, while Kloog et al [26,30] assessed the minimum and mean TA for Massachusetts (USA) using MODIS data and achieved a R 2 equal to 0.95.…”

Section: Discussionmentioning

confidence: 99%

“…In total, three major categories of relevant algorithms can be identified: the statistical approaches that correlate LST with TA [18,20,[24][25][26][27][28][29][30][31][32][33][34][35]; the temperature-vegetation index (TVX) approaches [23,36,37]; and the surface energy-balance parameterizations [38,39]. The first category can be divided further into the simple statistical approaches, which are usually based on a linear regression between the LST and TA, and the advanced statistical approaches.…”

Section: Introductionmentioning

confidence: 99%

An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

et al. 2016

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Abstract:The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities and impacts human health. The study of this effect for monitoring and mitigation purposes is crucial, but it is hampered by the lack of high spatiotemporal temperature data. This article presents the work undertaken for the implementation of an operational real-time module for monitoring 2 m air temperature (TA) at a spatial resolution of 1 km based on the Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This new module has been developed in the context of an operational system for monitoring the urban thermal environment. The initial evaluation of TA products against meteorological in situ data from 15 cities in Europe and North Africa yields that its accuracy in terms of Root Mean Square Error (RMSE) is 2.3˝C and Pearson's correlation coefficient (Rho) is 0.95. The temperature information made available at and around cities can facilitate the assessment of the UHIs in real time but also the timely generation of relevant higher value products and services for energy demand and human health studies. The service is available at

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“…The discovery that the pixel envelope in Ts/NDVI (or Fr) space tends to form a triangle or a trapezoid is very important since the triangular shape of the graph dispersion Ts / IV arises from Ts being less sensitive to water content at the surface in vegetated areas, than in areas of exposed soil Gillies et al (1995;1997), Symanzik et al (2000), Goward et al (2002), Sun et al (2005), Arvor et al (2007), and Brunsell and Anderson (2011). These authors used different spatial datasets to demonstrate that the limits of the triangular shape (the pixel envelope depicted as the small circles in Figure 1) can be used to FUZZO, D. F. S.…”

Section: Introductionmentioning

confidence: 99%

Spectral Agrometeorological Modeling Adapted by Means of Simplified Triangle Method for Soybean in Paraná State – Brazil

Fuzzo

2018

Revista Ra'e Ga

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Agriculture is an economic activity with high dependence on weather and climate. Special geotechnology and agrometeorological modeling can be used to optimize productivity in regional and national systems, while minimizing costs. The aim was to test the agrometeorological model for estimating crop soybean yield proposed by Doorenbos and Kassam (1979), using only spectral data as input variable in the model obtained by a simplified triangle method applied in Paraná state, for crop years 2002/03 to 2011/12. A high accuracy of the data was found, the model values for the parameter d 1 ("d 1 " modified Willmott) were between 0.8 and 0.95, whereas the root mean squared error showed that there was low variation between 30.81 to 116.88 (kg ha -1 ) and the p-value was used as the indicator significance of the model at the level of 5%, indicating that there was no statistically significant difference between the estimated and observed data, this means that the average of the data estimated by the model were statistically equal the average of the observed data. Thus, we can say that images of remote sensing can be used as tools in the absence of surface information, in agrometeorological modeling to estimate crop soybean yield.Key-words: crop yield; MODIS image; remote sensing; vegetation index. RESUMOA agricultura é uma das atividades econômica com maior dependência do tempo e do clima. Geotecnologia espacial e a modelagem agrometeorológica associadas, podem ser utilizadas na otimização da produtividade agrícola tanto em escalas regionais e nacionais, minimizando custos. O objetivo deste trabalho foi testar o modelo agrometeorológico de estimativa da soja proposto por Doorenbos e Kassam (1979), utilizando apenas dados espectrais como variável de entrada, por meio do método do triângulo simplificado. Foram analisadas regiões do Estado do Paraná e os anos-safra de 2002/03 a 2011/12. Os dados mostraram alta precisão, sendo que os valores do parâmetro d1 ( "d1" Willmott modificado) foram de 0,8 e 0,95, enquanto o erro médio quadrático mostrou que houve baixa variação entre 30,81 a 116,88 (kg ha -1 ). Em relação ao p-valor, utilizado como indicador de significância do modelo ao nível de 5%, mostrou que não houve diferença estatisticamente significativa entre os dados estimados e observados, isto significa que a média dos dados estimados pelo modelo foi igual à média dos dados observados. Dessa forma, podemos dizer que as imagens de sensoriamento remoto podem ser utilizadas como ferramentas na ausência de informações superficiais, na modelagem agrometeorológica para estimar o rendimento da soja cultivada.Palavras-chave: produtividade, imagem MODIS, sensoriamento remoto; índice de vegetação.

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Air temperature retrieval from remote sensing data based on thermodynamics

Cited by 148 publications

References 22 publications

The urban heat island in the city of Poznań as derived from Landsat 5 TM

The urban heat island in the city of Poznań as derived from Landsat 5 TM

An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

Spectral Agrometeorological Modeling Adapted by Means of Simplified Triangle Method for Soybean in Paraná State – Brazil

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