Search

Seasonal regimes of stream temperatures are important for ecological health as well as for societal water use. Seasonal regimes can be captured in the annual temperature cycle (the mean temperature for each day of the year) or in summary statistics such as seasonal mean temperatures, the former of which is the focus of this work. The annual temperature cycle is often characterized as a sine function, which performs satisfactorily for most streams. However, the sine function is unable to capture major seasonal variations, particularly for colder, drier, and high‐elevation regions. Seasonal summary statistics are effective for classification but do not capture the full time series, preventing the use of lost time‐series information, and lack context for the comparison of trends, hindering distinction between different causes of similar seasonal trends. We propose an improved function called the "three‐sine model" to describe the stream annual temperature cycle with higher accuracy and demonstrate its use in two case studies. The three‐sine model uses a cosine function over the entire year coupled with two seasonal anomaly sine functions. The three‐sine model captures the stream annual temperature cycle with eight parameters, reveals distinct spatial trends, and outperforms the sinusoidal model for all elevations and 99% of streams. We conclude that this approach can support improved stream temperature analysis by capturing detailed seasonal trends in context. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K ~ 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 ~ 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness. [ABSTRACT FROM AUTHOR]

Satellite-derived land surface temperatures (LSTs) often encounter a tradeoff between spatial and temporal resolutions, as well as severe cloud contamination. While extensive efforts have focused on resolution enhancement and under-cloud reconstruction, generating fine-resolution (≤100 m) diurnal LSTs under all-weather conditions remains a challenge, which hampers fine-scale monitoring of climatological, hydrological, and ecological processes. The latest 70-m ECOSTRESS observations at varying times of day provide an unprecedented opportunity for detailed mapping of diurnal LST dynamics, and reanalysis products with spatiotemporal continuity offer promising references for all-weather thermal dynamics. However, these advantages have rarely been integrated to concurrently achieve high spatiotemporal resolution and completeness. Here, we present a simple yet effective framework for reconstructing 60–100 m, hourly, all-weather LSTs based on the Landsat, ECOSTRESS and Reanalysis temperature Combination (termed LERC). The framework involves three steps: (i) preliminary under-cloud estimations within annual cycles of several times by fitting an enhanced annual temperature cycle (EATC) model to clear Landsat/ECOSTRESS scenes and China Land Data Assimilation System (CLDAS) LST fluctuations; (ii) optimized daily estimations at each selected time by correcting biases of the preliminary under-cloud estimations and re-modeling the EATC with temporally densified samples; and (iii) hourly seamless estimations by interpolating the two nearest daily estimations with reference to the weighted diurnal changes in CLDAS. LERC was evaluated in an urban-dominated region throughout 2020, resulting in an average root-mean-squared-error of 2.0 K (3.0 K) against 50 Landsat and ECOSTRESS images (hourly ground measurements at 13 sites). Compared to the enhanced spatial and temporal adaptive reflectance fusion model, classic ATC model, diurnal temperature cycle model, and three sophisticated all-weather products, LERC demonstrates outperformance in terms of general accuracy, spatiotemporal variability, and robustness against sparse input. LERC has great potentials for generating long-term reliable all-weather LST records with a high spatiotemporal resolution to promote broad applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Seasonal and diurnal SUHI (SUHI sea and SUHI diu) dynamics were studied globally. • Both SUHI sea and SUHI diu dynamics showed six typical patterns. • SUHI sea dynamics relate to climate during the day and rural land cover type at night. • SUHI diu dynamics are regulated by the urban-rural contrast in vegetation and albedo. Knowledge of the temporally continuous dynamics of seasonal and diurnal surface urban heat islands (SUHIs) as well as their underlying determinants is crucial to better understand their variations at multiple time scales. Owing to the orbital limitation of satellites, previous studies primarily focused on SUHI dynamics at limited time-nodes, either in a diurnal or seasonal cycle. However, a joint investigation of the continuous dynamics of seasonal and diurnal SUHIs (hereafter referred to as SUHI sea and SUHI diu) remains lacking. The comprehensive taxonomy of the patterns of continuous SUHI sea and SUHI diu dynamics across global cities is also not clear. Using satellite-derived land surface temperature (LST) data, we investigated the prevalent patterns of continuous SUHI sea and SUHI diu dynamics across global cities by combining annual and diurnal temperature cycle models and the k -means clustering algorithm. Our results showed that: (1) Both SUHI sea and SUHI diu dynamics exhibited six typical patterns including, single-peak type (SPT), single-valley type (SVT), peak-valley type (PVT) , valley-peak type (VPT), two-peak type (TPT), and two-valley type (TVT). (2) The daytime SUHI sea dynamics pattern was closely related to the background climate, with SPT and PVT mainly occurring in cities located in the warm temperate and snow zones, SVT and VPT in the arid zone, and TPT and TVT in the equatorial zone. In contrast, the nighttime SUHI sea dynamics pattern depended more on rural land cover type, with SPT, PVT, and TPT mostly occurring in cities surrounded by barren lands with high albedo and SVT, VPT, and TVT in cities surrounded by dense vegetation with low albedo. We also find a significant negative relationship between daytime SUHI sea dynamics and urban-rural contrast in vegetation and between nighttime SUHI sea dynamics and urban-rural contrast in albedo across cities. (3) For SUHI diu dynamics, SPT, PVT, and TVT were mainly located in cities with higher vegetation coverage in rural than in urban areas, while SVT, VPT, and TPT were in cities with higher vegetation coverage in urban areas. The SUHI diu dynamics were found to be synthetically affected by the urban-rural contrast in vegetation and albedo. We consider these findings to be beneficial for deepening the understanding of SUHI dynamics at various time scales. [ABSTRACT FROM AUTHOR]

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.

• Seasonal patterns of SUHII worldwide are classified into five typical patterns. • Same seasonal patterns between DNC and daytime SUHII account for 80% globally. • Seasonal patterns of daytime SUHII indicate opposite seasonal variations to ∆EVI. • Daytime SUHII is corresponded to the opposite patterns to LHF but similar to NSR. Variations and controls of surface urban heat island (SUHI) drawn great attention since its close association with environmental issues and heat risk. However, three issues remained neglect, namely, the mismatch and insignificant variations, seasonal patterns for SUHII DNC (day-and-night contrast) and impacts from surface energy variations. Therefore, this paper classifies seasonal patterns of SUHII based on ATCS (annual temperature cycle with seasonal adjustment) into five typical patterns, and further investigates impacts from surface energy variations. There are three major findings. (1) Approximately 70% of global cities are identified as the spring-summer and summer-autumn patterns at daytime, while global cities identified as the winter-spring and insignificant patterns account for 36% and 24%. The seasonal patterns of DNC are significantly affected by that of daytime. (2) The seasonal patterns of SUHII indicate opposite seasonal variations to ∆ EVI (enhanced vegetation index) at daytime and ∆ WSA (white sky albedo) at nighttime. (3) The cities identified as the spring-summer and summer-autumn patterns of SUHII are corresponded to the opposite patterns of latent heat flux at daytime, but similar patterns of net shortwave radiation at both day and night. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) and its annual or inter-annual variations play an important role in understanding global climate change, urban heat island, and the process of land-atmosphere energy exchange. Many annual temperature cycle (ATC) models [i.e., ATC with three or five parameters (ACP3 or ACP5)] have been proposed to analyze the annual variations of LST in the past decades. In this study, two year-to-year continuous and derivable models (YYCD_ACP3 and YYCD_ACP5 models) were proposed to model several years of ATCs. The fitting results of the YYCD_ACP3 model with global Aqua/MODIS daytime LSTs from 2014 to 2018 show that the YYCD_ACP3 model achieved a good performance in fitting the time-series LSTs with an overall normalized root-mean-square error (NRMSE) of 0.21, coefficient of determination (R2) of 0.74, and refined index of agreement ( $d$ ) of 0.85. In addition, the modeling results of ten representative samples covering different climatic conditions and land cover worldwide show that, except for two sites located in tropical and Antarctic, the YYCD_ACP3 model could show a good performance with R2 greater than 0.6. Although the ACP3 model shows similar performance to the YYCD_ACP3 model, the fitting curve of the YYCD_ACP3 model is continuous and smooth for describing the interannual variations of LST. When the LSTs of 2014–2018 are fitted as a whole by using both models, the YYCD_ACP3 model shows a slightly better performance than that of the ACP3 model. The application of the YYCD_ACP3 model with the global MODIS LSTs from 2003 to 2018 indicates that the results of the YYCD_ACP3 model have the potential to reveal the interannual variations of LST. Therefore, we conclude that the YYCD models are valuable for modeling the variations of LST over several years and can be widely applied.

• A hybrid ATC model (ATCH) by integrating harmonics and LST controls was proposed. • ATCH generally possesses a high accuracy and well-behaved generalization ability. • ATCH was evaluated under clear-sky and all-weather conditions. • Strategies were designed to gain model derivatives suitable for various scenarios. 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. [ABSTRACT FROM AUTHOR]

Weakening of annual temperature cycle (ATC) on the Tibetan Plateau (TP) in the last decades has been identified, and there is a subsequent change in the frequency of the ATC magnitude extremes. These changes in the climate have a potential influence on biological and ecological systems. However, the frequency of occurrences of the ATC magnitude extremes in the recent decades compared to the preindustrial period, as well as the potential driving forces remains unclear. This can limit the robust predictions and risk assessments for extreme events of the ATC in the future. In this study, we analyzed the frequency of occurrence of the ATC magnitude extremes using both recent observations (1955-2011) and a tree-ring-based reconstruction covering the past three centuries over the TP. The results indicate that the ATC magnitude extremes in maximum obviously decrease and in minimum distinctly increase under climate warming, both for the instrumental period and the past three centuries. Moreover, the occurrence of the ATC magnitude extremes on the TP had a distinct regional characteristic and was mainly related to the winter temperature extremes triggered by the anomalous meridional gradient of geopotential height impelling cold air intrudes the TP. Although volcanic eruptions were expected to result in summer cooling and winter warming, and thus trigger the minimum ATC magnitude extremes, this effect only can be identified in event-specific eruptions (e.g., the 1835 Cosiguina eruption). Our study results provide an essential background for developing robust predictions and risk assessments for the ATC magnitude extremes on the TP. [ABSTRACT FROM AUTHOR]

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 (ATC sza) 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. • A combined annual-diurnal temperature cycle (ADTC) model is presented. • The ADTC model simultaneously describes the annual and diurnal LST dynamics. • The model is controlled by five parameters (ADCPs) with physical meaning. • Five MODIS LST tiles were modelled with an average RMSE of 4.2 K. • The RMSE with respect to the standard ATC (Bechtel, 2012) was 1.2 K. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.

The 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. Key Points: The periodic information of LST mapping using annual temperature cycle model presents the general thermal pattern over mountainous regionEarthquake‐induced destruction in surface vegetation cover greatly influences the thermal environment, and its impact will last a long timeGlobal warming has a strong effect to high mountain region by reducing snow cover duration but shows small impact to lower surfaces [ABSTRACT FROM AUTHOR]

Abstract Due to its large spatial coverage and frequent revisit, satellite-derived land surface temperature (LST) has been recently used to explore annual temperature cycle (ATC) variations at regional and global scales. However, variability in seasonality of LSTs has not been examined in detail, particularly in urban areas where elevated temperatures are normally observed. By assuming repetitive temperature cycles, this study aims to reveal differences in ATC parameters between urban and rural areas and the impacts of surface urban heat island (UHI) on the ATC range over the continental United States. To this end, urban areas of larger than 10 km2 (a total of 1856 urban polygons) in the continental United States were identified from the map of urban extents produced by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data from 2012. The corresponding rural polygons of the same size were generated by using a buffer method. The ATC parameters were optimized using a sinusoidal function fitted with the 8-day MODIS LST composite data. Results showed that urban and rural areas exhibited a significant difference, with a p-value <0.01, in the ATC parameters, including mean annual surface temperature (MAST), yearly amplitude of surface temperature (YAST), and the revised phase shift parameter. The higher MAST and YAST, but lower phase shift values, were mainly associated with the urban areas. This finding indicated that urban areas contributed to changes in extreme temperatures (the minimum and maximum temperatures) as well as to an overall warming. The regression analysis suggested that surface UHI intensities were positively correlated with the differences in MAST (R2 = 0.9) and YAST (R2 = 0.5) between urban and rural areas, but negatively correlated (R2 = 0.2) with the differences in the revised phase shift parameter. In addition, highest surface UHIs (∼3 k) and largest differences in ATC parameters were observed in tropical regions, followed by temperature zones, continental (cold) zones, and arid zones. Overall, this study revealed that urbanization-induced land cover changes could influence urban systems by enhancing temperature variations. However, it should be cautioned that uncertainty in the analysis may arise from the characterization of surface UHIs for the selected ∼2000 cities with varying configuration and morphological dimensions. [ABSTRACT FROM AUTHOR]

The century-long search for the precise mechanisms responsible for urban heat islands continues, while urban warming worsens in many megacities. Most studies have focused on mean temperature, daily and annual temperature ranges and urban heat island intensity. We hypothesize that an analysis of the changes in the characteristics of the complete daily and annual temperature cycles, including not only the mean temperature and temperature ranges (amplitudes) but also the maximum and minimum temperatures and the phases, can provide more information on urban warming phenomena. Through a detailed analysis of long-term observations in Hong Kong, we found that the difference in the daily cycle between urban and rural stations is very distinct, whereas the annual cycles are much more similar, suggesting that the urban environment has a greater effect on the daily cycle than on the annual cycle. The daily phase has shifted a total of 1.77 h later over the last 130 years (1.36 h per century) in the urban area of Hong Kong according to the Hong Kong Observatory (HKO) data. The annual phase change at HKO reflects the globally observed phenomenon that the annual phase advances or seasons onset earlier. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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.

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters. [ABSTRACT FROM AUTHOR]

A widely accepted standpoint contends that plant growth near the cold edge of the species range, such as treelines, does not depend on the annual temperature seasonality (i.e. difference between maximum and minimum temperature values) but rather on the warmth of summer season. In contrast to this expectation, we show that the growth of treeline Norway spruce ( Picea abies) is well explained by temperature seasonality as a single climatic determinant. To do so, the tree-ring data of spruce trees growing on alpine treeline in Lapland was compared with long climate records. Biennial time-series of temperature seasonality capture both the decadal and abrupt growth fluctuations with a correlation coefficient of r = 0.601. We also show that the archetypal association between summer temperature and treeline tree growth may in fact be by far a more complex relationship than previously thought. Spruce growth appears responsive to late- June (r = 0.494) and mid-July (r = 0.310) temperatures but unresponsive to temperatures during the early July, that is, during the grand period of the tracheid formation. Climatic warming may enhance the treeline spruce growth unless the warming is concentrated on unresponsive interval in the midst of the growing season. Water relations did not play significant role as agents of P. abies growth. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing. [ABSTRACT FROM AUTHOR]

Abstract: Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and LST data by considering annual temperature cycle (ATC) and urban thermal landscape heterogeneity. The technique of linear spectral mixture analysis was employed to relate the Landsat radiance with the MODIS one, so that the temporal changes in radiance can be incorporated in the fusion model. This paper details the theoretical basis and the implementation procedures of the proposed data fusion algorithm, Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). A case study was conducted that predicted LSTs of five dates in 2005 from July to October in Los Angeles County, California. The results indicate that the prediction accuracy for the whole study area ranged from 1.3K to 2K. Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption. [Copyright &y& Elsevier]

Abstract: Subsurface soil temperature is a key variable of land surface processes and not only responds to but also modulates the interactions of energy fluxes at the Earth's surface. Thermal remote sensing has traditionally been regarded as incapable of detecting the soil temperature beneath the skin-surface. This study shows that thermal remote sensing can be used to estimate soil temperatures. Our results provide insights into thermal observations collected with tandem polar-orbiting satellites when used toward obtaining soil temperatures under clear-sky conditions without the use of any ground-based information or field-measured soil properties. We designed an analytical three-time-scale (3-scale, for short) model, dividing the annual cycle of soil temperatures into three subcycles: the annual temperature cycle (ATC), which represents the daily-averaged temperature; the diurnal temperature cycle (DTC), which represents the instantaneous temperature; and the weather-change temperature cycle (WTC), which is divided into two parts to represent both the daily-averaged (WTCavg) and the instantaneous temperature (WTCinst). The DTC and WTCinst were further parameterized into four undetermined variables, including the daily-averaged temperature, thermal inertia, upward surface flux factor, and day-to-day change rate. Thus, under clear-sky conditions, the four thermal measurements in a diurnal cycle recorded with tandem polar-orbiting satellites are sufficient for reconstructing the DTC of both land surface and soil temperatures. Polar-orbiting satellite data from MODIS are used to show the model's capability. The results demonstrate that soil temperatures with a spatial resolution of 1km under snow-free conditions can be generated at any time of a clear-sky day. Validation is performed by using a comparison between the MODIS-inverted and ground-based soil temperatures. The comparison shows that the accuracy of inverted soil temperatures lies between 0.3 and 2.5K with an average of approximately 1.5K. These results open a new frontier in the application of thermal remote sensing wherein soil temperatures with high spatial and temporal resolutions can be remotely estimated. [Copyright &y& Elsevier]

• A seamless downscaling method for ESA CCI SSM product is proposed in this study. • The method well overcomes the impact from the gaps in ESA CCI SSM and MODIS product. • The downscaled SSM data show good temporal consistency with field measurements. • The downscaled result provides a good spatio-temporal continuity in SSM variations. 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 (ub RMSE), 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 ub RMSE 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. [ABSTRACT FROM AUTHOR]

Remotely sensed and accurate daily mean land surface temperature (T dm) 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 T dm 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 T dm 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 T dm. 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 T dm from thermal observations of tandem polar orbiters. Therefore, the ADTC-based framework is a potentially valuable tool for many related applications. • A framework for estimating accurate daily mean LST is proposed. • The framework combines an enhanced ATC model and a four-parameter DTC model. • The framework is validated under cloud-free and overcast conditions. • Separate contributions and uncertainties of the ATC and DTC models are quantified. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]