Comparison of diurnal temperature cycle model and polynomial regression technique in temporal normalization of airborne land surface temperature

The scientific community has widely reported the impacts of climate change on the Central Himalaya. To qualify and quantify these effects, long-term land surface temperature observations in both the daytime and nighttime, acquired by the Moderate Resolution Imaging Spectroradiometer from 2000 to 2017, were used in this study to investigate the spatiotemporal variations and their changing mechanism. Two periodic parameters, the mean annual surface temperature (MAST) and the annual maximum temperature (MAXT), were derived based on an annual temperature cycle model to reduce the influences from the cloud cover and were used to analyze their trend during the period. The general thermal environment represented by the average MAST indicated a significant spatial distribution pattern along with the elevation gradient. Behind the clear differences in the daytime and nighttime temperatures at different physiographical regions, the trend test conducted with the Mann-Kendall (MK) method showed that most of the areas with significant changes showed an increasing trend, and the nighttime temperatures exhibited a more significant increasing trend than the daytime temperatures, for both the MAST and MAXT, according to the changing areas. The nighttime changing areas were more widely distributed (more than 28%) than the daytime changing areas (around 10%). The average change rates of the MAST and MAXT in the daytime are 0.102 °C/yr and 0.190 °C/yr, and they are generally faster than those in the nighttime (0.048 °C/yr and 0.091 °C/yr, respectively). The driving force analysis suggested that urban expansion, shifts in the courses of lowland rivers, and the retreat of both the snow and glacier cover presented strong effects on the local thermal environment, in addition to the climatic warming effect. Moreover, the strong topographic gradient greatly influenced the change rate and evidenced a significant elevation-dependent warming effect, especially for the nighttime LST. Generally, this study suggested that the nighttime temperature was more sensitive to climate change than the daytime temperature, and this general warming trend clearly observed in the central Himalayan region could have important influences on local geophysical, hydrological, and ecological processes.

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Cloud tolerance of remote-sensing technologies to measure land surface temperature

Hydrology and Earth System Sciences ◽

10.5194/hess-20-3263-2016 ◽

2016 ◽

Vol 20 (8) ◽

pp. 3263-3275 ◽

Cited By ~ 13

Author(s):

Thomas R. H. Holmes ◽

Christopher R. Hain ◽

Martha C. Anderson ◽

Wade T. Crow

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

The United States ◽

Temperature Cycle ◽

Negative Bias ◽

Spectral Window ◽

Clear Sky ◽

Stable Performance ◽

Cloud Screening

Abstract. Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive-microwave (MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25° resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product.This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR-based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

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Direct estimation of land-surface diurnal temperature cycle model parameters from MSG–SEVIRI brightness temperatures under clear sky conditions

Remote Sensing of Environment ◽

10.1016/j.rse.2014.04.017 ◽

2014 ◽

Vol 150 ◽

pp. 34-43 ◽

Cited By ~ 27

Author(s):

Si-Bo Duan ◽

Zhao-Liang Li ◽

Bo-Hui Tang ◽

Hua Wu ◽

Ronglin Tang

Keyword(s):

Land Surface ◽

Temperature Cycle ◽

Model Parameters ◽

Cycle Model ◽

Diurnal Temperature ◽

Clear Sky ◽

Direct Estimation ◽

Brightness Temperatures ◽

Sky Conditions

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Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

Remote Sensing ◽

10.3390/rs11232843 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2843 ◽

Cited By ~ 3

Author(s):

Liu ◽

Tang ◽

Yan ◽

Li ◽

Liang

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Surface Radiation ◽

Bayesian Optimization ◽

Radiation Budget ◽

Temperature Cycle ◽

Avhrr Data ◽

Mean Square Errors

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.

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ENHANCED MODELING OF REMOTELY SENSED ANNUAL LAND SURFACE TEMPERATURE CYCLE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-1067-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1067-1074

Author(s):

Z. Zou ◽

W. Zhan ◽

L. Jiang

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Meteorological Data ◽

Yangtze River Delta ◽

Temperature Cycle ◽

Different Seasons ◽

Weather Changes ◽

Mean Square Errors

Satellite thermal remote sensing provides access to acquire large-scale Land surface temperature (LST) data, but also generates missing and abnormal values resulting from non-clear-sky conditions. Given this limitation, Annual Temperature Cycle (ATC) model was employed to reconstruct the continuous daily LST data over a year. The original model ATCO used harmonic functions, but the dramatic changes of the real LST caused by the weather changes remained unclear due to the smooth sine curve. Using Aqua/MODIS LST products, NDVI and meteorological data, we proposed enhanced model ATCE based on ATCO to describe the fluctuation and compared their performances for the Yangtze River Delta region of China. The results demonstrated that, the overall root mean square errors (RMSEs) of the ATCE was lower than ATCO, and the improved accuracy of daytime was better than that of night, with the errors decreased by 0.64 K and 0.36 K, respectively. The improvements of accuracies varied with different land cover types: the forest, grassland and built-up areas improved larger than water. And the spatial heterogeneity was observed for performance of ATC model: the RMSEs of built-up area, forest and grassland were around 3.0 K in the daytime, while the water attained 2.27 K; at night, the accuracies of all types significantly increased to similar RMSEs level about 2 K. By comparing the differences between LSTs simulated by two models in different seasons, it was found that the differences were smaller in the spring and autumn, while larger in the summer and winter.

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