Annual Temperature Cycle Model Research Articles

Combined modelling of annual and diurnal land surface temperature cycles

The land surface's thermal dynamics follows annual and diurnal cycles that are to a large extent controlled by solar geometry. Therefore, annual and diurnal variations of land surface temperature (LST) can be modelled with relatively simple functions controlled by a small number of parameters, typically from three to six. The parameter values of the models can be determined by fitting the respective functions to time series of LST observations. Commonly either annual or diurnal LST variations are modelled or they are modelled sequentially in a two-step process. Here, we combine an annual temperature cycle (ATC) model controlled by the solar zenith angle (ATCsza) with a four-parameter version of the diurnal temperature cycle (DTC) model GOT09: this yields a new annual-diurnal temperature cycle (ADTC) model that simultaneously describes the annual and diurnal surface temperature dynamics. The proposed ADTC model is controlled by physically meaningful parameters: annual minimum temperature, annual temperature amplitude, annual maximum of daily temperature amplitude, mean time of thermal noon and time lag of maximum temperature with respect to summer solstice. Thus, the entire annual-diurnal LST dynamics is described with only five parameters. The new model was tested by fitting it to one year of LST observations obtained for five globally representative tiles of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard EOS – TERRA and EOS – AQUA satellites. For these tiles, the mean of the root mean square error (RMSE) was 4.2 K. ADTC modelled LSTs were also compared against those obtained with the standard ATC model for the four MODIS overpass times at five representative sites: for these, an overall RMSE of 1.2 K between the two models was obtained. The ADTC derived LSTs were validated against in-situ measurements from three different sites, which yielded an overall RMSE of 3.4 K. Additional investigations over five areas with different land covers (i.e. urban, lake, forest, mountain area and desert) revealed the potential of the ADTC parameters to describe the corresponding surface and climate properties. Since it is driven by solar geometry, the ADTC model reproduces double LST peaks in the tropics naturally. Furthermore, all available observations are modelled simultaneously, which means that a single set of parameters is obtained for each pixel and year.

Combining Spatiotemporally Global and Local Interpolations Improves Modeling of Annual Land Surface Temperature Cycles

Annual temperature cycle (ATC) models are widely used to characterize temporally continuous land surface temperature (LST) dynamics within an annual cycle. However, the existing ATC models ignore the spatiotemporally local correlations among adjacent LST pixels and are inadequate for capturing the complex relationships between LSTs and LST-related descriptors. To address these issues, we propose an improved ATC model (termed the ATC_GL), which combines both the spatiotemporally global and local interpolations. Using the random forest (RF) algorithm, the ATC_GL model quantifies the complex relationships between LSTs and LST-related descriptors such as the surface air temperature, normalized difference vegetation index, and digital elevation model. The performances of the ATC_GL and several extensively used LST reconstruction methods were compared under both clear-sky and overcast conditions. In the scenario with randomly missing LSTs, the accuracy of the ATC_GL was 2.3 K and 3.1 K higher than that of the ATCE (the enhanced ATC model) and the ATCO (the original ATC model), respectively. In the scenario with LST gaps of various sizes, the ATC_GL maintained the highest accuracy and was less sensitive to gap size when compared with the ATCH (the hybrid ATC model), Kriging interpolation, RSDAST (Remotely Sensed Daily Land Surface Temperature), and HIT (Hybrid Interpolation Technique). In the scenario of overcast conditions, the accuracy of the ATC_GL was 1.0 K higher than that of other LST reconstruction methods. The ATC_GL enriches the ATC model family and provides enhanced performance for generating spatiotemporally seamless LST products with high accuracy.

Reconstruction of land surface temperature under cloudy conditions from Landsat 8 data using annual temperature cycle model

Land surface temperature (LST) is an important parameter in the processes of energy exchange and water cycle between the land surface and the atmosphere. The impact of cloud cover leads to spatially incomplete of thermal infrared (TIR)-based LST products, which seriously hinders the applications of LST products in various fields. Several methods have been developed to reconstruct LST under cloudy conditions in previous studies, but there is a lack of an effective method for the reconstruction of cloudy LST at the spatial resolution of Landsat pixel (30 m). In this study, a novel method was proposed to reconstruct LST under cloudy conditions from Landsat 8 data. The LST reconstruction method includes four main steps: (1) identification of cloud-free, cloud-shadow, cloud-obscured, and cloud-covered pixels by integrating the Fmask method with a cloud-shape matching method; (2) calculation of annual temperature cycle (ATC)-based reference LST by fitting an ATC model to all available Landsat 8 LST product during 2013-2020; (3) estimation of LST residual from spatially adjacent similar pixels; and (4) estimation of reconstructed LST in terms of the sum of ATC-based reference LST and LST residual. The performance of the LST reconstruction method was evaluated using Landsat 8 LST images under clear-sky conditions as reference data. The root mean squared error (RMSE) between reconstructed LST and Landsat 8 reference LST ranges from 0.9 K to 2.5 K. The LST reconstruction method was further applied to reconstruct actual Landsat 8 LST images under cloudy conditions. Compared with original Landsat 8 LST images, the spatial distribution of reconstructed LST images is more complete. The pattern of reconstructed LST images reflects the spatial variability of LST well. The accuracy of the LST reconstruction method was validated against in situ LST measurements at six SURFRAD (Surface Radiation Budget Network) sites. The overall bias and RMSE between reconstructed LST and in situ LST at all sites are approximately −0.3 K and 3.5 K, respectively. The LST reconstruction method has great potentials to improve the applications of Landsat LST product in urban thermal environment monitoring and crop water stress monitoring.

An Improved Annual Temperature Cycle Model With the Consideration of Vegetation Change

Land surface temperature (LST) is an important parameter in land surface processes with strong relationship between surface energy and water exchange. To effectively capture the surface thermal dynamics, the annual temperature cycle model is a good option by depicting the annual variation as a constant term plus a sine function. However, this type model suffers from the assumption of constant surface thermal property which is hardly satisfied due to the changes in vegetation cover. To well address this issue, the normalized difference vegetation index (NDVI) is introduced as an indicator to characterize the variation in surface thermal property and added to the original form to propose an improved version. Through comparison between the fitting effects of the proposed model with the original one, the improvement shows good performance in suppressing the annual maximum temperature and elevating the annual minimum temperature with the increase in vegetation cover. The difference in the annual maximum and minimum temperature between the estimates from the proposed model and the original model shows good linear regression with NDVI difference when compared with the annual mean value, with the speed of −3.22 and 4.84, respectively. In addition, the fitting accuracy is also improved with a slight increase in the coefficient of determination (0.002) and a decrease in the root mean squared error (0.018 K). The application of the proposed model also provides reasonable distribution of the annual temperature parameters in the southwest of Europe and part of North Africa, confirming its potential effect in thermal dynamic monitoring.

Surface Warming Trend Analysis Based on MODIS/Terra Land Surface Temperature Product at Gongga Mountain in the Southeastern Tibetan Plateau

AbstractAs the highest mountain in the southeastern Tibetan Plateau, Gongga Mountain is highly sensitive to climate change which is hardly assessed with limited meteorological stations in this region. To analyze the warming trend of Gongga Mountain, the mean annual surface temperature (MAT) was extracted based on an annual temperature cycle model with the MODIS/Terra daily land surface temperature (LST) product and used to identify the MAT trends from 2000 to 2018 using the Mann‐Kendall trend test. The results indicate that almost all the pixels passing a significance test with the confidence level of 95% show an increasing trend, and the daytime MAT exhibited a stronger warming trend with the mean rate of 0.11 K/yr than the nighttime MAT (0.07 K/yr). However, more pixels (30.2%) exhibited significant warming trends at night than during the daytime (21.6%). Additionally, the significant warming pixels were mainly distributed above 3,200 m, and the number of pixels increased with elevation. Most importantly, the statistics of the daytime and nighttime changing rates at different elevation ranges support elevation‐dependent warming (EDW). These warming patterns are connected to changes in snow and cloud cover and the rate shows good correlations with the changes in clear‐sky days and vegetation cover conditions. In general, this study verifies that remotely sensed LST can be used to analyze the warming trends in mountain areas and identifies the presence of EDW at Gongga Mountain. This trend could result in a strong influence on the local mountain ecosystem functions, water resources, and glacial changes.

Reconciling the inconsistency of annual temperature cycles modelled from Landsat and MODIS LSTs through a percentile approach

ABSTRACT Land surface temperature (LST) is an important variable to understand surface energy fluxes, land–atmosphere interactions, and urban thermal environments. Time series analysis of LSTs through semi-physical models such as the annual temperature cycle (ATC) model has become critical for these understandings. However, studies are lacking in examining and reconciling the inconsistency of time series LST modelling results across spatial scales, weakening the reliability of these semi-physical models to characterize landscape thermal patterns. In this study, a percentile approach was used to reveal and reconcile discrepancies of ATC parameters estimated from Landsat (100 m) and Moderate Resolution Imaging Spectroradiometer (MODIS, 1000 m) LSTs. Results showed substantial differences across spatial scales for each of the ATC parameters, i.e. mean annual surface temperature (MAST), yearly amplitude of surface temperature (YAST), and revised phase shift (RPS), within the same land cover (e.g. 4.0 K difference between MAST estimated from Landsat LSTs and that from MODIS LSTs for grassland). The spatial distribution of ATC parameters estimated from MODIS LSTs across land cover types was quite different from that from Landsat LSTs. The percentile aggregation analysis suggested that the difference between MAST/YAST (and RPS) derived from MODIS LSTs and Landsat-aggregated values at the 25th (and 40th) percentile within a MODIS block was close to zero. Further regression analysis showed that differences in ATC parameters, particularly MAST and YAST, derived from different datasets could be reconciled. Our study offers new insights into understanding inconsistencies in and reconciliations of ATC parameters modelled at different spatial scales for quantifying landscape thermal patterns spatially and temporally.

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.

A simple yet robust framework to estimate accurate daily mean land surface temperature from thermal observations of tandem polar orbiters

Remotely sensed and accurate daily mean land surface temperature (Tdm) is valuable for various applications such as air temperature estimation and climate change monitoring. However, most traditional methods employed by the remote sensing community estimate Tdm by averaging the – usually few – observed cloud-free land surface temperatures (LSTs). Such estimates can have large sampling bias, especially for tandem polar orbiters, due to their sparse sampling of diurnal LST dynamics and the unavailability of under-cloud LSTs. To estimate accurate Tdm based on thermal observations from tandem polar orbiters, here we propose a simple yet robust framework that combines the annual temperature cycle (ATC) and the diurnal temperature cycle (DTC) models (termed the ADTC-based framework). The ATC model is used to reconstruct daily instantaneous under-cloud LSTs, based on which the DTC model is employed to establish diurnally continuous LST dynamics for estimating Tdm. The proposed framework is validated with geostationary LST observations and in-situ thermal measurements under both cloud-free and overcast conditions. The validations show that, under cloud-free conditions, the ADTC-based framework is able to reduce the positive sampling bias obtained with simple averaging (> 2.0 K) and yields a mean absolute error (MAE) of approximately 0.5 K. Under overcast conditions, the ADTC-based framework yields MAEs of 1.0 K and 0.5 K at the daily and monthly scales, respectively. Furthermore, a contribution analysis indicates that the ATC model reduces the MAE from around 4.2 K to 2.0 K while the DTC model reduces the MAE from around 2.0 K to 1.0 K. Based on our validation results and tests performed with MODIS data, the presented simple yet robust ADTC-based framework is able to accurately estimate large-scale spatiotemporally continuous Tdm from thermal observations of tandem polar orbiters. Therefore, the ADTC-based framework is a potentially valuable tool for many related applications.

Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model

Urban heat island (UHI), driving by urbanization, plays an important role in urban sustainability under climate change. However, the quantification of UHI's response to urbanization is still challenging due to the lack of robust and continuous temperature and urbanization datasets and reliable quantification methods. This study proposed a framework to quantify the response of surface UHI (SUHI) to urban expansion using the annual temperate cycle model. We built a continuous annual SUHI series at the buffer level from 2003 to 2018 in the Jing-Jin-Ji region of China using MODIS land surface temperature and imperviousness derived from Landsat. We then investigated the spatiotemporal dynamic of SUHI under urban expansion and examined the underlying mechanism. Spatially, the largest SUHI interannual variations occurred in suburban areas compared to the urban center and rural areas. Temporally, the increase in SUHI under urban expansion was more significant in daytime compare to nighttime. We found that the seasonal variation of SUHI was largely affected by the seasonal variations of vegetation in rural areas and the interannual variation was mainly attributed to urban expansion in urban areas. Additionally, urban greening led to the decrease in summer daytime SHUI in central urban areas. These findings deepen the understanding of the long-term spatiotemporal dynamic of UHI and the quantitative relationship between UHI and urban expansion, providing a scientific basis for prediction and mitigation of UHI.

Modeling Daily Temperatures Via a Phenology-Based Annual Temperature Cycle Model

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and the effect of clouds and other atmospheric conditions result in discontinuous daily thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). Annual temperature cycle (ATC) models can help to supply daily continuous LSTs via limited observations, but these ATC models seldom consider the disturbance of weather conditions or the land cover change. On the other hand, spatial interpolation techniques also limit in implementation when available data in one day or several days are not able to obtain enough spatiotemporal information for LST reconstruction. The objective of this study is to propose a phenology-based ATC model (termed PATC), which takes the phenology change and local weather change into account, to reconstruct daily unscanned LSTs at an annual scale. Daily MODIS LSTs collected in 2015 were utilized to analyze the performance of PATC compared with other ATC models. Results show that PATC improved the accuracy by 1.6 and 0.5 K compared to the classic ATC model in the daytime and nighttime, respectively. Compared to the enhanced ATC model, PATC also shows better performance with higher accuracies, especially during the growing season of vegetation in the daytime. Future research may focus on an incorporation with Landsat observations and diurnal temperature cycle models to implement LST reconstruction at a diurnal scale.

Interannual Spatiotemporal Variations of Land Surface Temperature in China From 2003 to 2018

Land surface temperature (LST) is a comprehensive embodiment of surface energy balance and land surface processes. The spatial and temporal variation of LST is of great significance for studying surface characteristics and climate change. In this study, the spatiotemporal variations of LST in China from 2003 to 2018 is examined by using the continuous and derivable annual temperature cycle model. The trends of the annual mean and annual amplitude of LST is detected using the Mann–Kendall test and Theil–Sen estimator. In addition, we have further revealed the correlation between normalized difference vegetation index (NDVI) and LST in different land cover types. The results show that the annual mean LST presents a spatial distribution pattern of high values in the southern regions and low values in the northern regions and that the factors of altitude and land cover type also affect the LST's spatial distribution. The annual amplitude of the LST presents a spatial distribution pattern of high values in the northern regions and low values in the southern regions. In the majority of instances, the phase of LST in China was positioned between the 175th and 205th day of each year. Both the annual mean and annual amplitude of the LST have a mean increasing trend in China with a rate of 0.02 K/year, and the areas with large significant changes accounted for 8.6% out of the total area, with a mean rate of approximately 0.05 K/year, in this period. Significant changes in the annual mean LST were correlated with the change in vegetation coverage and the impact of land cover types on the interannual variations of LST is also determined in this study. While the increase in vegetation coverage in barren land exhibits a clearly recognizable upward trend in the annual mean LST, the improved vegetation coverage in the grassland region presents a downward trend in annual mean LST.

SUHI analysis using Local Climate Zones—A comparison of 50 cities

Assessment of surface urban heat islands (SUHI) has been hampered by the lack of a consistent framework to permit consistent interpretation between cities. Local Climate Zones (LCZ) are a universal description of local scale landscape types based on expected variation at neighbourhood scale (≥1km2) in and around cities. In this study, we investigate the suitability of the LCZ scheme for SUHI studies based on 50 cities from across the globe. For comparability we use an annual temperature cycle model for MODIS land surface temperature (LST) at different overpass times and multi-year mean Landsat 8 LST. The SUHI analysis shows significant differences in the intra-urban estimate of SUHI for different built LCZ types. Substantial variability of SUHI within LCZ classes and between cities exists and SUHI patterns vary by time of day. Landsat derived estimates have very high correlations to those from MODIS at a similar time. The use of an LCZ approach combined with annual SUHI estimates provides a promising approach for a consistent and comprehensive SUHI analysis framework subject to further work to assess the spatial scale of matching LST and LCZ data, filter for topographic effects, and include the phenological status.

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

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.

Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.

Spatiotemporal Variability in Land Surface Temperature Over the Mountainous Region Affected by the 2008 Wenchuan Earthquake From 2000 to 2017

AbstractThe mountainous region affected by the 2008 Wenchuan earthquake was frequently analyzed to assess its vegetation recovery. However, the dynamic of surface thermal environment was rarely investigated but directly influenced local geophysical and biophysical processes. Under this background, this research aimed to analyze the trend of surface temperature based on Moderate Resolution Imaging Spectroradiometer daily land surface temperature product from 2000 to 2017 and understand its change mechanism. An annual temperature cycle model was applied to obtain the daytime and nighttime annual cycle parameters (ACPs), including mean annual surface temperature (MAST), yearly amplitude of surface temperature, and phase shift. The trend analysis with the Mann‐Kendall test illustrated the strong inverse relationship between the two ACPs (MAST and yearly amplitude of surface temperature) and elevation, and the nighttime terms showed a stronger connection than those of the daytime. The areas with significant decreases in vegetation coverage induced by the earthquake exhibited an obvious upward trend in daytime MAST and annual maximum temperature, while the vegetation improved areas presented a cooling effect. Comparatively, the nighttime ACPs was little sensitive to vegetation change and the change magnitude was relatively small. For the high mountain area, with the shrinkage of snow cover duration, the global warming effect resulted in a warming trend by increasing the annual minimum temperature at both the daytime and nighttime. Overall, this study provided insights to the impact of the earthquake to mountain thermal environment and its different responses to changes in vegetation cover and climatic environment.

Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.

Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socio-economic factors: A case study of the Shanghai metropolitan region

Multi-scale assessment of urban thermal environment dynamics and its influencing factors have been considered an essential precondition for mitigation and regulation of urban heat island (UHI) effects. However, the annual cycle behavior of satellite-derived urban land surface temperature (LST) is still unclear due to the limitation of irregular and infrequent satellite LST data. This study investigated annual dynamics of LST and the UHI effects of Shanghai by using an annual temperature cycle (ATC) model to reconstruct the Moderate-resolution imaging spectroradiometer (MODIS) 8-day LST data with 1 km2 resolution in 2015. Ordinary least squares (OLS) and spatial regression models were further applied to investigate the relationships between day/night LST and urban biophysical composition and configuration and socio-economic characteristics in the metropolitan area of Shanghai, China. The results indicate that ATC model performed well with overall root mean square errors (RMSE) of 2.6 K. The performance of ATC is better in night-time than that in day-time. The thermal gradient analysis showed the urban center was hotter than outskirts by up to 2.0 K in day and 0.8 K in night. Spatial patterns of UHI intensity between day-time and night-time had significant differences in central urban areas. We also observed higher amplitude of the annual temperature cycle in central urban areas than that in rural. Furthermore, our statistical model showed biophysical indicators and land composition had a stronger influence on LST than the other explanatory variables. Nighttime light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting partnership (NPP) as a spatial proxy for socio-economic factors had significant positive relationships with mean LST. Compared with OLS and spatial lag model (SLM), spatial error model (SEM) is more appropriate to predict the urban LST. We conclude that distinct control strategies between day-time and night-time may improve efficiency of attenuating the UHI for a metropolitan region, and the spatial and temporal elements should be considered in landscape and urban planning.

ENHANCED MODELING OF REMOTELY SENSED ANNUAL LAND SURFACE TEMPERATURE CYCLE

Abstract. 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.

Disaggregation of remotely sensed land surface temperature: A new dynamic methodology

AbstractThe trade‐off between the spatial and temporal resolutions of satellite‐derived land surface temperature (LST) gives birth to disaggregation of LST (DLST). However, the concurrent enhancement of the spatiotemporal resolutions of LST remains difficult, and many studies disregard the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs. Here we propose a new dynamic methodology to enhance concurrently the spatiotemporal resolutions of satellite‐derived LSTs. This methodology conducts DLST by the controlling parameters of the temperature cycle models, i.e., the diurnal temperature cycle (DTC) model and annual temperature cycle (ATC) model, rather than directly by the LST. To achieve the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs, herein we incorporate a modulation procedure that adds temporal thermal details to coarse resolution LSTs rather than straightforwardly transforms fine‐resolution scaling factors into LSTs. Indirect validations at the same resolution show that the mean absolute error (MAE) between the predicted and reference LSTs is around 1.0 K during a DTC; the associated MAE is around 2.0 K during an ATC, but this relatively lower accuracy is due more to the uncertainty of the ATC model. The upscaling validations indicate that the MAE is around 1.0 K and the normalized mean absolute error is around 0.3. Comparisons between the DTC‐ and ATC‐based DLST illustrate that the former retains a higher accuracy, but the latter holds a higher flexibility on days when background low‐resolution LSTs are unavailable. This methodology alters the static DLST into a dynamic way, and it is able to provide temporally continuous fine‐resolution LSTs; it will also promote the design of DLST methods for the generation of high‐quality LSTs.