ENHANCED MODELING OF REMOTELY SENSED ANNUAL LAND SURFACE TEMPERATURE CYCLE

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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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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Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates

Remote Sensing ◽

10.3390/rs10071149 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1149 ◽

Cited By ~ 7

Author(s):

Yongmin Yang ◽

Jianxiu Qiu ◽

Renhua Zhang ◽

Shifeng Huang ◽

Sheng Chen ◽

...

Keyword(s):

Energy Balance ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Heat Fluxes ◽

Temperature Decomposition ◽

Component Temperature ◽

The Impact ◽

Mean Square Errors

Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. Approaches for accurate decomposition of the component temperature and E/T partitioning from ET based on TSEB requires careful investigation. In this study, three TSEB models are used: (i) the TSEB model with the Priestley-Taylor equation, i.e., TSEB-PT; (ii) the TSEB model using the Penman-Monteith equation, i.e., TSEB-PM, and (iii) the TSEB using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space, i.e., TSEB-TC-TS. These models are employed to investigate the impact of component temperature decomposition on E/T partitioning accuracy. Validation was conducted in the large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research-Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE) in the northwest of China, and results showed that root mean square errors (RMSEs) of latent and sensible heat fluxes were respectively lower than 76 W/m2 and 50 W/m2 for all three approaches. Based on the measurements from the stable oxygen and hydrogen isotopes system at the Daman superstation, it was found that all three models slightly overestimated the ratio of E/ET. In addition, discrepancies in E/T partitioning among the three models were observed in the kernel experimental area of MUSOEXE. Further intercomparison indicated that different temperature decomposition methods were responsible for the observed discrepancies in E/T partitioning. The iterative procedure adopted by TSEB-PT and TSEB-PM produced higher LEC and lower TC when compared to TSEB-TC-TS. Overall, this work provides valuable insights into understanding the performances of TSEB models with different temperature decomposition mechanisms over semiarid regions.

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Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

Remote Sensing ◽

10.3390/rs11172016 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2016

Author(s):

Lijuan Wang ◽

Ni Guo ◽

Wei Wang ◽

Hongchao Zuo

Keyword(s):

Surface Temperature ◽

Diurnal Variation ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Regional Scale ◽

Pso Algorithm ◽

Variation Characteristics ◽

Split Window Algorithm ◽

Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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Corrigendum to “Improved estimates of monthly land surface temperature from MODIS using a diurnal temperature cycle (DTC) model” [ISPRS J. Photogramm. Remote Sens. 168 (2020) 131–140]

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2020.11.017 ◽

2021 ◽

Vol 171 ◽

pp. 118

Author(s):

Leiqiu Hu ◽

Ying Sun ◽

Gavin Collins ◽

Peng Fu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temperature Cycle ◽

Diurnal Temperature

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Spatio-Temporal Variations of CO2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature

Sustainability ◽

10.3390/su12208388 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8388

Author(s):

Juchao Zhao ◽

Shaohua Zhang ◽

Kun Yang ◽

Yanhui Zhu ◽

Yuling Ma

Keyword(s):

Surface Temperature ◽

Co2 Emissions ◽

Land Surface Temperature ◽

Land Surface ◽

Yangtze River ◽

Yangtze River Delta ◽

The Yangtze River ◽

Delta Region ◽

Yangtze River Delta Region ◽

The Yangtze River Delta

The rapid development of industrialization and urbanization has resulted in a large amount of carbon dioxide (CO2) emissions, which are closely related to the long-term stability of urban surface temperature and the sustainable development of cities in the future. However, there is still a lack of research on the temporal and spatial changes of CO2 emissions in long-term series and their relationship with land surface temperature. In this study, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data, Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data, energy consumption statistics data and nighttime land surface temperature are selected to realize the spatial informatization of long-term series CO2 emissions in the Yangtze River Delta region, which reveals the spatial and temporal dynamic characteristics of CO2 emissions, spatial autocorrelation distribution patterns and their impacts on nighttime land surface temperature. According to the results, CO2 emissions in the Yangtze River Delta region show an obvious upward trend from 2000 to 2017, with an average annual growth rate of 6.26%, but the growth rate is gradually slowing down. In terms of spatial distribution, the CO2 emissions in that region have significant regional differences. Shanghai, Suzhou and their neighboring cities are the main distribution areas with high CO2 emissions and obvious patch distribution patterns. From the perspective of spatial trend, the areas whose CO2 emissions are of significant growth, relatively significant growth and extremely significant growth account for 8.78%, 4.84% and 0.58%, respectively, with a spatial pattern of increase in the east and no big change in the west. From the perspective of spatial autocorrelation, the global spatial autocorrelation index of CO2 emissions in the Yangtze River Delta region in the past 18 years has been greater than 0.66 (p < 0.01), which displays significant positive spatial autocorrelation characteristics, and the spatial agglomeration degree of CO2 emissions continues to increase from 2000 to 2010. From 2000 to 2017, the nighttime land surface temperature in that region showed a warming trend, and the areas where CO2 emissions are positively correlated with nighttime land surface temperature account for 88.98%. The increased CO2 emissions lead to, to a large extent, the rise of nighttime land surface temperature. The research results have important theoretical and practical significance for the Yangtze River Delta region to formulate a regional emission reduction strategy.

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Comprehensive assessment of four-parameter diurnal land surface temperature cycle models under clear-sky

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2018.06.008 ◽

2018 ◽

Vol 142 ◽

pp. 190-204 ◽

Cited By ~ 8

Author(s):

Falu Hong ◽

Wenfeng Zhan ◽

Frank-M. Göttsche ◽

Zihan Liu ◽

Ji Zhou ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Comprehensive Assessment ◽

Temperature Cycle ◽

Clear Sky

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An Analysis of Land Surface Temperature Trends in the Central Himalayan Region Based on MODIS Products

Remote Sensing ◽

10.3390/rs11080900 ◽

2019 ◽

Vol 11 (8) ◽

pp. 900 ◽

Cited By ~ 8

Author(s):

Wei Zhao ◽

Juelin He ◽

Yanhong Wu ◽

Donghong Xiong ◽

Fengping Wen ◽

...

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Environment ◽

Himalayan Region ◽

Maximum Temperature ◽

Temperature Cycle ◽

Increasing Trend ◽

Nighttime Temperatures

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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