Land Surface Temperature and Surface Air Temperature in Complex Terrain

Mutiibwa, Denis; Strachan, Scotty; Albright, Thomas P.

doi:10.1109/jstars.2015.2468594

Cited by 158 publications

(92 citation statements)

References 52 publications

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“…Our research, in agreement with findings by [20,37] over complex terrain and by [19] over the Corn Belt in the U.S., showed that nighttime LST provided a good proxy of minimum T air , while models with daytime LST had the lowest accuracy. However, in agreement with findings by [17] and [19] and following recommendations by [20], our study also showed that nighttime LST provided a good proxy of maximum T air . Interestingly, by combining both daytime and nighttime LST data, it is possible to improve the accuracy of maximum T air , while no significant improvement in accuracy is found for minimum T air estimates [19], indicating that daytime LST is not relevant for estimating minimum T air .…”

Section: Differences Between Daytime and Nighttime Lstsupporting

confidence: 91%

“…Similarly, the analysis results in Figures 11 and 12 showed that the month-to-month T air accuracy improved with the decrease in terrain roughness, in accordance with the findings by [19,20]. Further, in agreement with [18], we also observed high errors in minimum T air over water surfaces due to the inertia of water, which may lead to an underestimation or an overestimation of minimum T air according to the temperature of the previous months, respectively cooler or warmer than the present [18].…”

Section: Temporal and Spatial Patternssupporting

confidence: 85%

“…As observed by different authors [16][17][18][19][20], besides data availability, cloud contamination and angular effects, the time of overpass of the remote sensor has an impact on the accuracy of Tair estimates. However, [21] stated that the difference between satellite overpass and the timing of maximum or minimum Tair was not critical.…”

Section: Differences Between Daytime and Nighttime Lstmentioning

confidence: 94%

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Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

et al. 2017

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Month-to-month air temperature (T air ) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month T air mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical T air modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum T air , but also for maximum T air . The improvement achieved by the inclusion of remote sensing LST products was higher for minimum T air (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.

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Section: Differences Between Daytime and Nighttime Lstsupporting

confidence: 91%

Section: Temporal and Spatial Patternssupporting

confidence: 85%

Section: Differences Between Daytime and Nighttime Lstmentioning

confidence: 94%

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Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

et al. 2017

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“…Although the T s products are pre‐filtered by the cloud mask (the pixel is cloud‐free if it has the grid value for T s ), Wan () stated that cloud contamination still exists in the V005 products since the cloud‐removing scheme is unable to remove the contaminated pixels under light and moderate cloud conditions. The contaminated pixels usually refer to the extreme low temperature at the top of clouds (Mutiibwa et al ., ). All the data used in this study have been scrutinized to ensure that there are no extreme T s values.…”

Section: Methods and Datamentioning

confidence: 99%

Impact of atmospheric conditions and levels of urbanization on the relationship between nocturnal surface and urban canopy heat islands

Feng

Cai

Chapman

2019

Quart J Royal Meteoro Soc

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Previous investigations of urban heat islands (UHI) are primarily focused either on the canopy heat island intensity (aUHII) derived from weather stations, or on the surface urban heat island intensity (sUHII) derived from satellite instruments. Research of the relationship between sUHII and aUHII (the sUHII‐aUHII relationship) is limited and this study attempts to further progress this possibility by examining the night‐time sUHII‐aUHII relationship for three factors: season, wind speed (WS), and basic land‐use categories modified from local climate zones (urban/suburban), in Birmingham, United Kingdom. Using high‐resolution datasets of canopy air temperature from Birmingham Urban Climate Laboratory and land surface temperature from the MODIS instrument aboard the Terra and Aqua satellites, with a unique methodology of regression analysis, confidence ellipse analysis of covariance (ANCOVA), and 2‐D Kolmogorov–Smirnov (K‐S) tests, statistical evidence is provided to present the varying patterns and magnitudes between sUHII and aUHII. The significance of the impact of the three considered factors is clearly supported by the statistical tests. The results indicate that satellite data can be used to infer aUHII with a higher confidence for low WS conditions. Results also demonstrate better confidence in the approach for summer and spring seasons, and for more urbanized sites. Indeed, the analysis potentially indicates that wind advection is a key factor for the investigation of the sUHII‐aUHII relationship. Overall, the methods used here are transferable to other cities and/or can be used to guide further research to explore the sUHII‐aUHII relationship under other environmental conditions.

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“…The TVX method also needs ground observation and could not be used in ungauged basins and vegetation sparse watersheds. Statistical regression methods usually try to build the relationship between station observed Ta and remotely sensed LST, elevation, NDVI, surface albedo and other explaining variables (such as longitude/latitude) using multi-variable linear regression model or machine learning approaches [25][26][27][28][29][30][31][32][33][34][35]. Using MODIS LST products and observed daily mean temperature from 95 meteorological stations in the Tibetan Plateau, Zhang et al [36] compared the performances of several regression methods, including MLR (multiple linear regression), PLS (partial least squares regression), BPNN (back propagation neural network), SVR (support vector regression), RF (random forests) and CR (Cubist regression), and 15 different combination schemes of the four MODIS observations per day over the Tibet Plateau.…”

Section: Introductionmentioning

confidence: 99%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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Land Surface Temperature and Surface Air Temperature in Complex Terrain

Cited by 158 publications

References 52 publications

Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

Impact of atmospheric conditions and levels of urbanization on the relationship between nocturnal surface and urban canopy heat islands

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

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