Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site

Sobrino, José A.; Jiménez-Muñoz, Juan C.; Kharraz, Jauad El; Gómez, M.; Romaguera, M.; Sòria, G.

doi:10.1080/0143116031000115210

Cited by 73 publications

(40 citation statements)

References 19 publications

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“…[94,96,103,157,[167][168][169][170][171][172][173][174], for assessments in actual conditions by reducing spatial heterogeneity effects. Few validations were conducted using spaceborne observations with hectometric resolutions [175][176][177][178][179]; and with kilometric ones over areas almost homogeneous [180][181][182].…”

Section: Assessing Modeling Tools and Inversion Methodsmentioning

confidence: 99%

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Jacob¹,

Schmugge

Olioso

et al. 2008

Advances in Land Remote Sensing

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U n c o r r e c t e d P r o o f 244 F. Jacob et al.Abstract Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. IntroductionUsing TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1-2], atmosphere [3][4][5][6], oceans [7][8][9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11][12][13][14], urban areas [15][16][17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil-vegetation-atmosphere interface [18][19][20][21][22][23][24]; (2) interactions between land surface and atmospheric boundary la...

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Section: Assessing Modeling Tools and Inversion Methodsmentioning

confidence: 99%

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Jacob¹,

Schmugge

Olioso

et al. 2008

Advances in Land Remote Sensing

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“…Sin embargo, en general, el objetivo es conseguir una exactitud mejor que 1 K (Seguin et al, 1999). A partir de observaciones instantáneas monocanal en el infrarrojo térmico (IRT) se han observado buenos resultados con el sensor Thematic Mapper (TM) de los satélites Landsat 4 y 5, y con los sensores aeroportados DAIS (e.g., 1 K sobre zonas agrícolas semiáridas, Sobrino et al, 2004aSobrino et al, , 2004b. A partir de observaciones instantáneas de sensores con dos canales o dos ángulos de observación en el IRT, varios experimentos de validación han demostrado proporcionar exactitudes mejores que 1 K. Sin embargo, una exactitud de 1 K por lo general requiere información local sobre las condiciones de la superficie para corregir los efectos de emisividad.…”

Section: Introductionunclassified

“…A partir de observaciones instantáneas de sensores con dos canales o dos ángulos de observación en el IRT, varios experimentos de validación han demostrado proporcionar exactitudes mejores que 1 K. Sin embargo, una exactitud de 1 K por lo general requiere información local sobre las condiciones de la superficie para corregir los efectos de emisividad. Además, la falta de tal información puede inducir a errores de hasta 3 K (Sobrino et al, 2003(Sobrino et al, , 2004a(Sobrino et al, , 2004cSòria y Sobrino, 2006;Coll et al, 2005). A partir de observaciones instantáneas multiespectrales en el IRT, el algoritmo de separación de la temperatura y la emisividad (TES, Gillespie et al, 1998) proporciona estimaciones de la TST con una exactitud de aproximadamente 1 K , mientras que el uso de datos hiperespectrales aumenta la exactitud de la TST hasta 0.5 K (Payan y Royer, 2004).…”

Section: Introductionunclassified

Análisis Comparativo De Métodos Para La Estimación De La Emisividad en La Banda Térmica Del Sensor Tm De Landsat-5

et al. 2017

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RESUMENEl objetivo de este trabajo es comparar las estimaciones de emisividad de la banda térmica del sensor TM de Landsat-5 obtenidas según los algoritmos Valor y Caselles (1996), Sobrino y Raissouni (2000) y Wittich (1997). La evaluación de dichos algoritmos a través de medidas terreno sobre suelos demuestra que proporcionan estimaciones enmarcadas dentro de los valores esperados. Además, se demuestra la capacidad de TM para detectar un aumento significativo (hasta 0.018) de la emisividad de los suelos en función de su contenido de humedad. Asimismo, el estudio pone de manifiesto una discrepancia entre métodos en las estimaciones de emisividad menores a 0.014, variable según el tipo de cubierta. Un análisis espacio-temporal por clases temáticas permite, de manera similar en los tres métodos, observar los cambios estacionales de los cultivos. En conclusión, la comparación aquí realizada destaca la importancia de la elección del algoritmo a aplicar y de la correcta definición de las constantes de entrada teniendo en cuenta las características espectrales de la zona de estudio.Palabras clave: emisividad, infrarrojo térmico, Landsat, TM, cubiertas del suelo, NDVI, fracción de cubierta vegetal. COMPARATIVE ANALYSIS OF EMISSIVITY ESTIMATION METHODS IN THE THERMAL BAND OF THE LANDSAT-5 TM SENSOR ABSTRACTThe objective of this work is to compare the emissivity estimates corresponding to the thermal band of the Landsat-5 TM sensor obtained following the algorithms of Valor and Caselles

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“…Equipped with such good characteristics, the SEVIRI data is able to monitor weather systems throughout the diurnal cycle, with unprecedented temporal and spatial resolution, and has the unique capabilities for cloud imaging and tracking, fog detection, measurement of the Earth-surface, and cloud-top temperatures, as well as many other improved measurements [15,[46][47][48]. Several numerical methods [49][50][51][52] have been developed to estimate land surface temperature using SEVIRI data.…”

Section: Satellite Datamentioning

confidence: 99%

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

Zhao

Bian

et al. 2014

Remote Sensing

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Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 OPEN ACCESSRemote Sens. 2014, 6 2214 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas.

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Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site

Cited by 73 publications

References 19 publications

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Análisis Comparativo De Métodos Para La Estimación De La Emisividad en La Banda Térmica Del Sensor Tm De Landsat-5

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

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