Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Yang, Yuan; Cai, Wen; Yang, Jian

doi:10.3390/rs9050410

Cited by 92 publications

(61 citation statements)

References 55 publications

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“…Since available, MODIS LST data has been used for various studies, such as evaluating and monitoring urban heat islands [11][12][13][14][15], estimating air surface temperatures (Ta) [16][17][18][19], retrieving soil moisture [20,21], drought assessment [22], and hydrology applications [23]. Most of these studies have shown that the changes of land surface properties (e.g., normalized difference vegetation index (NDVI), elevation, and land use/cover types) will result in the variations of LST [3].…”

Section: Introductionmentioning

confidence: 99%

Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam

Phan

Kappas

Tran

2018

Climate

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Land surface temperature (LST) is one of the most important variables for applications relating to the physics of land surface processes. LST rapidly changes in both space and time, and knowledge of LST and its spatiotemporal variation is essential to understand the interactions between human activity and the environment. This study investigates the spatiotemporal variation of LST according to changes in elevation. The newest version (version 6) of MODIS LST data for 2015 was used. An area of 40,000 km 2 (200 × 200 km 2 ) in northwest Vietnam with elevations ranging from 8 m to 3165 m was chosen as a case study. Our results showed that the drop in LST with increased elevation varied throughout the year during both the daytime and nighttime. The monthly averages in 2015 and an altitude increase of 1000 m resulted in a decrease in LST ranging from 3.8 • C to 6.1 • C and 1.5 • C to 5.8 • C for the daytime and nighttime, respectively. This suggests that in any study relating to the spatial distribution of LST, the effect of elevation on LST should be considered. In addition, the effects of land use/cover and elevation distribution on the relationship between LST and elevation are discussed.

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Section: Introductionmentioning

confidence: 99%

Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam

Phan

Kappas

Tran

2018

Climate

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“…This study employed the level 3 MODIS eight-day tiled land surface temperature (LST) product. Previous studies have shown that MODIS LST exhibited a high correlation with the near-surface air temperature as recorded by weather stations (e.g., [33][34][35]). The period of the thaw was identified when mean daily LST was exceeded 0 • C. Although the LST data used in this study was the eight-day product, some null values were included due to poor weather conditions.…”

Section: Remote Sensing Datamentioning

confidence: 91%

Spatio-Temporal Variations of Soil Active Layer Thickness in Chinese Boreal Forests from 2000 to 2015

et al. 2018

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The soil active layer in boreal forests is sensitive to climate warming. Climate-induced changes in the active layer may greatly affect the global carbon budget and planetary climatic system by releasing large quantities of greenhouse gases that currently are stored in permafrost. Ground surface temperature is an immediate driver of active layer thickness (ALT) dynamics. In this study, we mapped ALT distribution in Chinese boreal larch forests from 2000 to 2015 by integrating remote sensing data with the Stefan equation. We then examined the changes of the ALT in response to changes in ground surface temperature and identified drivers of the spatio-temporal patterns of ALT. Active layer thickness varied from 1.18 to 1.3 m in the study area. Areas of nonforested land and low elevation or with increased air temperature had a relatively high ALT, whereas ALT was lower at relatively high elevation and with decreased air temperatures. Interannual variations of ALT had no obvious trend, however, and the ALT changed at a rate of only −0.01 and 0.01 m year −1 . In a mega-fire patch of 79,000 ha burned in 2003, ∆ALT (ALT i − ALT 2002 , where 2003 ≤ i ≤ 2015) was significantly higher than in the unburned area, with the influence of the wildfire persisting 10 years. Under the high emission scenario (RCP8.5), an increase of 2.6-4.8 • C in mean air temperature would increase ALT into 1.46-1.55 m by 2100, which in turn would produce a significant positive feedback to climate warming.

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“…Resultados semelhantes aos observados neste estudo foram obtidos por Yang et al (2017) que estimaram a temperatura do ar anual no Nordeste da China, cujo resultado do erro REQM foi de 4,63 °C para a temperatura máxima; de 3,99 °C para a temperatura mínima e de 3,60 °C para a temperatura média do ar. Nessa mesma linha, os modelos obtidos por Cristóbal et al (2008) para estimar a temperatura do ar diária, mensal e anual para o Nordeste da Península Ibérica e para a Catalunha, apresentaram REQM variando entre 0,65 e 1,69 °C para a temperatura média, entre 1,06 e 2,64 °C para a temperatura mínima e entre 0,86 e 2,36 °C para a temperatura máxima do ar.…”

Section: Validação Dos Modelosunclassified

“…Para contornar as limitações decorrentes da falta destes registros, diferentes métodos têm sido estudados a fim de se obter maior exatidão nas estimativas de dados ausentes. Dentre eles, destacam-se aqueles baseados nos princípios da estatística clássica e geoestatística (VIOLA et al, 2010;PERIN et al, 2015), inteligência artificial (VENTURA et al, 2013;DEPINÉ et al, 2014), lógica fuzzy (FERREIRA, 2012), sensoriamento remoto (ZHANG et al, 2015;YANG et al, 2017) e também a regressão múltipla. Particularmente para este último, o qual tem sido utilizado intensamente, em função de sua simplicidade, diversos trabalhos têm sido conduzidos em várias regiões brasileiras, a exemplo de Minas Gerais e Pará (FERREIRA et al, 2006), São Paulo (PANTANO; BARDIN, 2012), Rio Grande do Sul (CARGNELUTTI FILHO et al, 2008) e estados da região nordeste (MEDEIROS et al, 2005).…”

Section: Introductionunclassified

Coordenadas Geográficas E Altitude Na Estimativa Da Temperatura Do Ar No Estado De Goiás

Capuchinho

Siqueira

Cruz

et al. 2019

RBAI

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RESUMOA estimativa de dados meteorológicos é imprescindível quando não se dispõe de dados observacionais. No entanto, a confiabilidade no uso destes, está condicionada a existência de concordância entre os dados preditos e os observados. Particularmente para a variável temperatura do ar, sendo uma das mais importantes para o planejamento das atividades agrícolas. Diante disso, objetivou-se com este trabalho, estimar os valores máximos, mínimos e médios da tempetura do ar, na escala decendial, a partir de coordenadas geográficas e altitude para o estado de Goiás. Para tanto, foram consideradas séries históricas de dados (1987-2017) oriundas de 27 municípios goianos. Para as estimativas consideradas neste estudo utilizou-se técnicas de análise de regressão múltipla, onde foi verificada a significância dos modelos preditores. Os coeficientes de determinação ajustados resultantes do modelo encontrado variaram de 0,66 a 0,82 para Tmax, de 0,56 a 0,72 para Tmin e de 0,66 a 0,74 para Tmed. Com este resultado, as médias das temperaturas máximas e médias decendiais, podem ser estimadas satisfatoriamente nos municípios goianos por meio da altitude, latitude e longitude.Palavras-chave: modelo matemático, temperatura do ar, regressão múltipla GEOGRAPHICAL COORDINATES AND ALTITUDE IN AIR TEMPERATURE ESTIMATION IN THE STATE OF GOIÁS ABSTRACTThe estimation of meteorological data is essential when observational data are not available. However, the reliability in its use, is conditioned by the existence of agreement between the predicted and observed data. Particularly for the variable air temperature, one of the most important for the planning of agricultural activities, this type of information has a strategic character. The objective of this work was to estimate the air temperature, maximum, minimum and mean values of air temperature, in the decendial scale, from geographic coordinates and altitude for the state of Goiás (Brazil). Historical data series from 27 municipalities in the state of Goiás were considered. For the estimates considered in this study multiple regression analysis techniques were used, where the significance of the predictive models was verified. The adjusted coefficients of determination resulting from the model found ranged from 0.66 to 0.82 for Tmax, from 0.56 to 0.72 for Tmin and from 0.66 to 0.74 for Tmed. With this result, mean maximum and average decendial temperatures can be estimated satisfactorily in the municipalities of Goiás by means of altitude, latitude and longitude.

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Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Cited by 92 publications

References 55 publications

Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam

Land Surface Temperature Variation Due to Changes in Elevation in Northwest Vietnam

Spatio-Temporal Variations of Soil Active Layer Thickness in Chinese Boreal Forests from 2000 to 2015

Coordenadas Geográficas E Altitude Na Estimativa Da Temperatura Do Ar No Estado De Goiás

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