Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

Quan, Jinling; Zhan, Wenfeng; Chen, Yunhao; Wang, Mengjie; Wang, Jinfei

doi:10.1002/2015jd024354

Cited by 97 publications

(77 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decreasing vegetation and increasing impervious surfaces can increase heat stored during daytime. Therefore, more heat will be released from impervious surfaces during nighttime, leading to the increasing nighttime SUHII [25,55]. One unique phenomenon was that the SUHII was significantly negatively correlated with ∆EVI at 4 of 10 cities in winter nights.…”

Section: The Relationships Between Suhii and ∆Evimentioning

confidence: 89%

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

et al. 2017

View full text Add to dashboard Cite

Abstract:In the context of rapid urbanization, systematic research about temporal trends of urbanization effects (UEs) on urban environment is needed. In this study, MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature (LST) data and enhanced vegetation index (EVI) data were used to analyze the temporal trends of UEs on vegetation and surface urban heat islands (SUHIs) at 10 big cities in Yangtze River Basin (YRB), China during 2001-2016. The urban and rural areas in each city were derived from MODIS land cover data and nighttime light data. It was found that the UEs on vegetation and SUHIs were increasingly significant in YRB, China. The ∆EVI (the UEs on vegetation, urban EVI minus rural EVI) decreased significantly (p < 0.05) in 9, 7 and 5 out of 10 cities for annual, summer and winter, respectively. The annual daytime and nighttime SUHI intensity (SUHII; urban LST minus rural LST) increased significantly (p < 0.05) in 10 and 4 out of 10 cities, respectively. The increasing rate of daytime SUHII and the decreasing rate of ∆EVI in old urban areas were much less than the whole urban area (0.034 • C/year vs. 0.077 • C/year for annual daytime SUHII; 0.00209/year vs. 0.00329/year for ∆EVI). The correlation analyses indicated that the annual and summer daytime SUHII were significantly negatively correlated with ∆EVI in most cities. The decreasing ∆EVI may also contribute to the increasing nighttime SUHII. In addition, the significant negative correlations (r < −0.5, p < 0.1) between inter-annual linear slope of ∆EVI and SUHII were observed, which suggested that the cities with higher decreasing rates of ∆EVI may show higher increasing rates of SUHII.

show abstract

Section: The Relationships Between Suhii and ∆Evimentioning

confidence: 89%

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The seasonal component is a regular and periodic change at an annual scale, primarily driven by the annual variation in insolation. The noise component is a stochastic and irregular variation that is caused by observation conditions (e.g., signal-to-noise ratio), atmospheric environments (e.g., clouds and aerosols) [41]. BFAST can be used to analyze different types of satellite image time series, and applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics.…”

Section: Trend and Season Decomposition Modelmentioning

confidence: 99%

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

et al. 2017

View full text Add to dashboard Cite

Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km 2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R 2 ) (0.89-0.957 for SR and 0.79-0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R 2 : 0.598-0.728 and 0.61-0.735, respectively).

show abstract

“…NDVI and NDWI represent greenness and wetness, respectively [74], both of which strongly influence evapotranspiration and surface cooling [75], and thus a decrease in NDVI/NDWI is usually followed by a Ta rise. Similarly, ISA contributes to enhancing sensible heat flux while reducing latent heat flux [40,69] and consequently heating up the atmosphere. BSA (or albedo) indicates a reflective proportion of incoming solar radiation, and therefore a low albedo increases solar heat absorption as well as heat release to the atmosphere [13,57].…”

Section: The Seven Predictors and Complementary Predictorsmentioning

confidence: 99%

“…The root mean square error (RMSE), mean absolute error (MAE), mean error (ME), and correlation coefficient (R 2 ) of the predicted Ta versus the measured Ta for each withheld weather station were calculated and then summarized across space (by land cover and site) and time (by year, season and month). To further evaluate the representation of temporal patterns by the predicted Ta, trend and seasonal decomposition [69] was performed on both the predicted and the measured Ta time series during the study period. The derived inter-and intra-annual variation parameters (mainly trend slope, seasonal amplitude and annual average temperature) were compared over each weather station.…”

Section: Model Evaluationmentioning

confidence: 99%

“…To further evaluate the representation of temporal patterns by the predicted Ta, trend and seasonal decomposition [69] was performed on both the predicted and the measured Ta time series during the study period. The derived inter-and intra-annual variation parameters (mainly trend slope, seasonal amplitude and annual average temperature) were compared over each weather station.…”

Section: Model Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

Self Cite

View full text Add to dashboard Cite

Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

show abstract

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

Cited by 97 publications

References 116 publications

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

Contact Info

Product

Resources

About