Daily Land Surface Temperature Research Articles

A novel approach combining satellite and in situ observations to estimate the daytime variation of land surface temperatures for all sky conditions

Land surface temperature (LST) and its diurnal variability are key to understanding the land-atmosphere interactions, hydrological processes and climate change. However, at any given point in time approximately half of the Earth's surface is covered by clouds. This restricts the availability of LST through satellite remote sensing, which works best under clear skies. However, in situ observations continue to monitor atmospheric conditions beneath the clouds that could complement satellite measurements during cloudy conditions. The present study explores a novel approach to estimate hourly LST during the daylight hours using remotely sensed surface solar absorption and in situ observations of daily LST extremes (maximum and minimum) together with an adaptive non-linear fitting approach. A learning algorithm trained against in-situ measurements of LST extrema and diurnal cycle of surface solar absorption together with the associated linear correlation between the two parameters, is used to estimate an optimized set of parameters to approximate hourly LST for each day during the daylight hours between sunrise and sunset. Results show that the method captures the intra-day variability of LST very well under most sky conditions with rms errors below 1.5 K.

The cooling and warming effects of potential forest transition on local land surface temperature in Northeast China

Forest cover change directly affects the surface energy balance by altering the radiative and non-radiative properties of the surface. These competing processes may yield considerable biophysical impacts on local and regional climate that depend on background climate and specific forest type. Here we compared the differences in land surface temperature (LST) between temperate forests and adjacent croplands/grasslands to quantify the potential biophysical effects of transitions from croplands/grasslands to forests in Northeast China, based on multiple satellite observation products from 2001 to 2016. Forests were found to show a slight lower daytime LST and strong higher nighttime LST than adjacent croplands or grasslands. As a result, the daily LST of forests was 0.15 ± 0.06 ℃ and 0.32 ± 0.04 ℃ higher than that of croplands and grasslands, respectively. On the seasonal scale, the warming effect of cropland/grassland-to-forest transition during non-growing season outweighed the cooling effect during growing season, and dominated the annual positive LST response. Moreover, the cooling effect of cropland-to-forest transition during growing season was stronger in the early growth stage than late growth stage (−1.65 ± 0.9 ℃ in May to June, −0.39 ± 0.5 ℃ in July to September). The net cooling effect of warm temperate broadleaved forests was found to shift toward net warming effect of cold temperate coniferous forests with increasing latitude. The daytime cooling effect of forests was mainly driven by increased non-radiative evapotranspiration. Overall, the radiative process related to the solar energy dominated the net warming effect of forest transitions over Northeast China. Estimating the climate feedback potential of forests could be useful to provide theoretical references for the adaptation and mitigation strategies for climate change.

Spatial characterization of global heat waves using satellite-based land surface temperature

The warming world greatly suffers the increase of frequency, severity and duration of heat wave events, which would cause the significant societal and environmental damages and implications from local to global scales. In order to eliminate the limitations of site observations in accurate spatial extent identification and large scale monitoring, this study tried to investigate global heat waves in 2018 using spatial datasets of land surface temperature (LST) derived from AQUA TIR sensors. A 15-year daily maximum LST dataset was used to identify the hot days and heat wave events using the relative threshold method based on the probability density function analysis of LST sequences, then they were adopted in analyzing spatial patterns of duration, frequency and intensity of heat wave events using heat wave numbers (HWN), the frequency of the appeared heat wave days (HWF) and the amplitude of heat wave impacts (HWA) indices, respectively. Our results indicated that more heat wave events and longer heat wave duration happened in low and middle latitudes under the warmer land surface in 2018 than last decade, especially many heat wave events occurred in the environmentally fragile regions and the dense population area. Urban regions achieved the largest HWA by 45.5 ± 6.9℃. Both annual HWF and HWN highlighted seven hot spot regions influenced by heat waves, and most of them distributed around the latitude 30°N and the latitude 30°S. Extreme heat wave of hot spot regions in the Southern Hemisphere mainly happened in winter and spring in contrast to that in North Hemisphere appeared in summer and autumn. Dry climate could contribute to the occurrence of heat waves, and heat wave indices variations also implied the compound consequences between heat waves and drought. Our findings demonstrate that remote sensing datasets are capable of providing the continuous whole-Earth coverage of heat wave changes, and they would play more important roles in preventing or mitigating the impacts of extreme heat events on people and natural environment.

Synergies or trade-offs between surface urban heat island and hot extreme: Distinct responses in urban environments

Hot extremes are among the most damaging climate extremes to human society, with urban areas being particularly vulnerable due to the surface urban heat island (SUHI). Despite the growing frequency and intensity of surface hot extreme (SHX), a comprehensive understanding of the patterns and drivers of SUHI responsiveness to SHX (difference in SUHI intensity [δSUHII] between SHX and non-hot extreme conditions [NSHX]) remains obscure. This study aims to shed light on the spatial-temporal pattern of SHX and the distinct SUHI responses to SHX throughout climatically diverse Chinese urban clusters using the seamless daily land surface temperature (LST) dataset. Our results provided evidence that Chinese urban clusters have experienced increasingly frequent, prolonged, and intensive SHX events regardless of climatic context, with stronger occurrences in summer and fall. Spatially, urban clusters in the northern regions have been subjected to more intensified SHX episodes compared to their southern counterparts. Furthermore, our findings revealed distinct δSUHII responses to SHX across China, demonstrating synergies between SUHI and SHX when SHX events constrained to urban areas whereas trade-offs when SHX isolated within rural settings. The magnitude of δSUHII depended upon the relative intensity of SHX between urban and rural settings, and heightened δSUHII generally associated with more intensified SHX events in urban than rural areas. Additionally, the explainable machine learning-based driver exploration showed that δSUHII was largely controlled by disparities in evaporative cooling (δET) between SHX and NSHX during the daytime, whereas during nighttime, it was predominantly governed by changes in surface heat storage, including urban-rural disparities in surface albedo (δABD) and impervious surface fraction (ISF). Facing the intertwined challenges posed by climate change and urbanization, it is imperative for cities to develop effective cooling strategies that emphasize enhanced evaporative cooling and minimized heat storage. These strategies are essential for safeguarding urban residents from potential synergistic effects between SUHI and SHX towards sustainable cities and human settlements.

Estimation of Daily Mean Land Surface Temperature over the Qinghai–Tibet Plateau Based on an RTM-DTC Model

Accurately estimating daily mean land surface temperature (LST) is crucial for studying the urban heat island effect, land–atmosphere energy exchange, and global climate change. However, limited research has been conducted on average surface temperature estimation, particularly in high-altitude regions like the Qinghai–Tibet Plateau with extensive cloud cover. In this study, we propose the Reanalysis Data and Thermal Infrared Remote Sensing Data Merging-Diurnal Temperature Cycle (RTM-DTC) model specifically for the Qinghai–Tibet Plateau, successfully estimating mean LST using the model. We apply the RTM method to reconstruct LST under cloud cover from the MODIS LST product and calculate the average temperature using the DTC model. Validation with in situ measurements from seven meteorological stations on the Tibetan Plateau yielded daily scale RMSEs ranging from 1.81 K to 2.021 K and monthly scale RMSEs ranging from 1.77 K to 2.0 K, with an average RMSE of 1.91 K. These results demonstrate the adaptability of the RTM-DTC model and its ability to depict the annual variation curve of the mean surface temperature, and provide further research on RTM-DTC as a valuable approach.

Influence of urban extent discrepancy on the estimation of surface urban heat island intensity: A global-scale assessment in 892 cities

The estimation of surface urban heat island intensity (SUHII) is crucial for studying the urban thermal environment, which is influenced not only by the commonly known definition of rural reference but also by the delineation of urban extent. Existing studies relies on various urban extent products defined in different ways, and the influence of urban extent discrepancy (UED) on SUHII estimates still remains unclear. In this study, we collected five open-source global urban extent products (GUEPs) for the year 2015 and corresponding daily land surface temperature (LST) observations (MYD11A1). Based on these products, we quantified the UED-induced uncertainty in SUHII estimates by comparing absolute difference (ΔSUHIIAD) and relative difference (ΔSUHIIRD) in SUHII among GUEPs across 892 global cities. Besides, we introduced an ISF-constrained (ISF–C) method to reduce SUHII differences among GUEPs by constraining the impervious surface fraction (ISF) within urban and rural extents. The results show that urban extents delineated by different GUEPs are not consistent, leading to their difference in ISF as well as LST, which in turn causes uncertainties in the estimated SUHII. On average for global cities, the annual daytime and nighttime ΔSUHIIAD are 0.46 ± 0.02 °C (mean ± 95% confidence interval) and 0.24 ± 0.01 °C, with corresponding ΔSUHIIRD of 42.0 ± 2.7% and 35.2 ± 2.3%, respectively. The UED-induced uncertainty in SUHII estimates varies among climate zones, and the annual daytime ΔSUHIIRD averaged for cities located in the arid zone reaches up to 60.8 ± 6.6%, which is nearly twice as high as that in other climate zones. More importantly, both ΔSUHIIAD and ΔSUHIIRD show lower values when using the ISF-C method, implying the effectiveness of this method. This study highlights the non-negligible impact of UED on the estimation of SUHII, which requires more attention due to the inconsistency of urban extents among current products.

Global distinct variations of surface urban heat islands in inter- and intra-cities revealed by local climate zones and seamless daily land surface temperature data

The continuous dynamic of surface urban heat island (SUHI) effect is highly needed in urban climate studies. However, temporal variations of SUHI intensity (SUHII) have been understudied in previous studies owing to the lack of spatially seamless and temporally continuous land surface temperature (LST) data, particularly in urban domains. Also, the relationship between SUHII variations and heterogeneous landscapes, reflecting various thermal and biophysical properties, has not been comprehensively explored globally. Here, we investigated the annual dynamic of nighttime SUHII and the inter- (i.e., city level) and intra-city (i.e., local climate zones level) SUHII variations of 1,028 worldwide cities, using the annual temperature cycle (ATC) model and seamless daily LST data and local climate zones (LCZ) data. The results reveal that the annual mean SUHII (SUHIIM) in urban areas is 0.69 °C for nighttime with an increasing trend towards high latitudes, while the annual SUHII amplitude (SUHIIA) is 0.32 °C and is relatively stable along latitudes. Global SUHIIM and SUHIIA indicate a robust pattern that decreases with decreasing buildings’ height (i.e., from LCZ1 to LCZ3 and from LCZ4 to LCZ6) and increasing openness of building layout (i.e., from compact to open types). In contrast, the annual pattern of LST is mainly regulated by the background climate. The phase shift (i.e., day of year when the LST reaches its maximum value) of annual SUHII shows a distinct time lag effect along the LCZ gradient in cold climate zone. These findings are valuable for scientific planning of resilient cities and can mitigate the impact of heat islands on urban residents by optimizing the composition and configuration of urban landscapes, e.g., changing the height and morphology of buildings.

A generalized method for retrieving global daily mean land surface temperature from polar-orbiting thermal infrared sensor instantaneous observations

ABSTRACT Daily mean land surface temperature (LST) is a crucial indicator for investigating global long-term climate change. The polar-orbiting thermal infrared sensor (POTIRS) can provide limited instantaneous LSTs of a single day on a global scale. Several studies have developed algorithms to derive daily mean land surface temperature (DMLST) from limited instantaneous daily LST data, but these methods are restricted to using data from a single POTIRS after 2000. This study presents a generalized method for estimating global DMLST utilizing one daytime and one night-time observation from any POTIRS since 1981. The proposed method employs a simple linear regression of two instantaneous LST measurements from different observation times (once during the day and once at night) of POTIRSs based on in situ LST measurements from 227 flux stations operating in diverse climate regions globally. The results demonstrate that this simple linear regression model provides highly accurate estimates of DMLST under all-weather conditions, with a root mean square error (RMSE) value lower than 1.7 K. Additionally, the proposed method was employed to estimate DMLST from instantaneous LST products obtained from various sensors, including Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite, Advanced Very High Resolution Radiometer aboard the polar-orbiting National Oceanic and Atmospheric Administration satellite, and Meteorological Operational satellite. Validation results indicate that the DMLSTs estimated from these POTIRS products are in close agreement with the daily mean in situ LST, with RMSE values varying from 2.2 to 2.4 K. We expect that this generalized method will be useful for generating long-term and high-quality DMLST datasets.

Differences in the evolution of urban and rural surface thermal environment and their responses to urban renewal in Shanghai, China.

The rapid and extensive urbanization has profound impacts on urban thermal environment. It is of great significance to comprehensively understand how urbanization affects the evolution of urban thermal environment for urban ecological safety, environmental quality, and residents' health. Based on daily land surface temperature (LST) products of MODIS Aqua satellite in the summer of 2002-2020, we investigated the evolution of urban-rural differences in surface summer thermal environment in Shanghai during 2002-2020 and its response to urban spatial renewal. We used normalized land surface temperature (NLST) and urban heat island ratio index (URI) as the surface thermal environment measurement indicators, by combining vegetation index and impervious surface cove-rage, and used M-K trend analysis and interpretation analysis. The results showed that the linear growth rate of LST in Shanghai was 0.09 ℃·a-1 (2002-2020), and that URI showed a trend of first increasing (2002-2010) and then decreasing (2010-2020). The mean summer LST was generally in the order of urban core>suburban>rural. 1.6% of the areas showed a significant cooling trend, of which 54.0% were distributed in the urban core. 39.5% of the regions showed a significant warming trend, of which 77.6% were distributed in the suburban. In general, there were concentrated significant cooling areas in the highly urbanized urban areas, while there was a significant warming trend in the suburban. The transformation from urban expansion to urban renewal was the main reason for the emergence of concentrated and significant cooling areas in the urban. Nearly 20% of the urban area showed a signi-ficant increase of vegetation coverage. Urban renewal projects such as gathering vegetation or dispersing impervious surfaces in highly urbanized areas are important ways to effectively improve the urban residential thermal environment.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020–October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020–2022) period air quality recorded low improvements relative to (2015–2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021–2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022–1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Detection of vegetation drying signals using diurnal variation of land surface temperature: Application to the 2018 East Asia heatwave

Satellite-based vegetation monitoring provides important insights regarding spatiotemporal variations in vegetation growth from a regional to continental scale. Most current vegetation monitoring methodologies rely on spectral vegetation indices (VIs) observed by polar-orbiting satellites, which provide one or a few observations per day. This study proposes a new methodology based on diurnal changes in land surface temperatures (LSTs) using Japan's geostationary satellite, Himawari-8/Advanced Himawari Imager (AHI). AHI thermal infrared observation provides LSTs at 10-min frequencies and ∼ 2 km spatial resolution. The DTC parameters that summarize the diurnal cycle waveform were obtained by fitting a diurnal temperature cycle (DTC) model to the time-series LST information for each day. To clarify the applicability of DTC parameters in detecting vegetation drying under humid climates, DTC parameters from in situ LSTs observed at vegetation sites, as well as those from Himawari-8 LSTs, were evaluated for East Asia. Utilizing the record-breaking heat wave that occurred in East Asia in 2018 as a case study, the anomalies of DTC parameters from the Himawari-8 LSTs were compared with the drying signals indicated by VIs, latent heat fluxes (LE), and surface soil moisture (SM). The results of site-based and satellite-based analyses revealed that DTR (diurnal temperature range) correlates with the evaporative fraction (EF) and SM, whereas Tmax (daily maximum LST) correlates with LE and VIs. Regarding other temperature-related parameters, T0 (LST around sunrise), Ta (temperature rise during daytime), and δT (temperature fall during nighttime) are unstable in quantification by DTC model. Moreover, time-related parameters, such as tm (time reaching Tmax), are more sensitive to topographic slope and geometric conditions than surface thermal properties at humid sites in East Asia, although they correlate with EF and SM at a semi-arid site in Australia. Additionally, the spatial distribution of the DTR anomaly during the 2018 heat wave corresponds with the drying signals indicated as negative SM anomalies. Regions with large positive anomalies in Tmax and DTR correspond to area with visible damage to vegetation, as indicated by negative VI anomalies. Hence, combined Tmax and DTR potentially detects vegetation drying indetectable by VIs, thereby providing earlier and more detailed vegetation monitoring in both humid and semi-arid climates.

Urban landscape and climate affect residents’ sentiments based on big data

Residents’ sentiments are a quantitative indicator of human feelings, which is useful for optimizing urban residential environments. Little is known about the spatiotemporal variations and potential drivers of sentiment based on big data. A total of 221,104 Weibo social media data were used to quantify daily sentiment in the Beijing metropolitan area during the COVID-19 pandemic from January 1, 2021 to March 8, 2022. Deep-learning natural language processing was used to extract this dataset to investigate the spatiotemporal sentiment patterns. The density of roads and buildings, normalized difference vegetation index (NDVI), population, sky visibility factor, daily land surface temperature (LST), daily precipitation, and daily air pollution concentrations (CO, NO2, PM2.5, SO2, and O3) were explored as potential drivers of sentiment. Results show that (1) the holiday sentiment was 1.31% higher than on weekends and 4.61% higher than on weekdays. Extreme precipitation, air pollution, and COVID-19 lockdown measures have reduced sentiment. (2) The sentiment in spring was found to be the highest. The numbers of functional zones with high sentiment values (>0.8) in spring were 13.59%, in summer 34.48%, and in autumn 14.71%. (3) Sentiment was highest under conditions of moderate greenness (0.4<NDVI<0.6) and comfortable daily temperature (25 °C < LST<30 °C). (4) Sentiment was negatively associated with daily air pollutants, such as PM2.5, NO2, and CO. This paper presents the effectiveness of sentiment quantification based on social media data and deep-learning techniques. The results provide practical implications and support decisions for sustainable urban health development.

Cloud-covered MODIS LST reconstruction by combining assimilation data and remote sensing data through a nonlocality-reinforced network

Reconstruction of cloud-covered thermal infrared land surface temperature (LST) is vital for the measurement of physical properties in land surface at regional and global scales. In this paper, a novel reconstruction method for Moderate Resolution Imaging Spectroradiometer (MODIS) LST data with a 1-km spatial resolution is proposed by combining assimilation data and remote sensing data through a nonlocality-reinforced network (NRN) model. Firstly, a data grading criterion is introduced to evaluate the importance of the various datasets, forming four combinations of multi-modal datasets for the training and testing of the NRN model. Secondly, the NRN model with a multiscale encoding–decoding structure considering the nonlocality-reinforced module is proposed for LST reconstruction. The results suggest that the proposed method can precisely reconstruct cloud-covered LST, with a mean absolute error (MAE) less than 0.8 K, even when no auxiliary remote sensing LST are used (Combination 1). The best result is the full combination (Combination 4), in which the coefficient of determination is 0.8956, the MAE is 0.5219 K, and the root-mean-square error is 0.7622 K. Compared with the traditional harmonic analysis of time series method, the improved enhanced spatial and temporal adaptive reflectance fusion method and the multiscale feature connected convolutional neural network method for LST reconstruction, the proposed method can achieve superior results. The proposed method with Combination 1 has been implemented to reconstruct the daily LST in the study area for 2019. Referring to the meteorological station observations, the reconstructed bias absolute value is less than 1 K, indicating that the proposed model is very effective and valid for regional cloud-covered LST reconstruction.

How 2D and 3D built environments impact urban surface temperature under extreme heat: A study in Chengdu, China

Optimizing the built environment helps alleviate the adverse impact of extreme heat on public health and sustainable urban development. To this end, this study measured the daily land surface temperature (LST) in August 2022 (when extreme heat occurred) in Chengdu and introduced the 3D street view characteristics extracted from street view images. By employing OLS and spatial regression models, we probed the combined effects of 2D and 3D built environments on LST and their relationships under disparate circle spaces. The results show that: (1) compared with the 2D built environment, the 3D built environment presents a superior impact on LST. (2) urban land use that is 3D, compact, and has a high-floor area ratio diminishes LST, as evidenced by the finding that floor area ratio (FAR) reduces LST, but building density enhances it. (3) the green view index exhibits a stronger cooling effect than green space areas. (4) the built environment beneath distinct circle spaces displays a spatially varying impact on LST. For example, FAR is positively correlated with LST in the urban center, while in the urban periphery, it is prominently negative. Our empirical findings are conducive to formulating strategies from both 2D and 3D built environments for the formation and implementation of healthy urban design and development.

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.

Temporal Upscaling of MODIS 1-km Instantaneous Land Surface Temperature to Monthly Mean Value: Method Evaluation and Product Generation

The monthly mean land surface temperature (MMLST) reflects more stable intra- and inter-annual temperature variations, and therefore, it has a wider range of applications than instantaneous land surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), a simple average model for two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> when NOD is <20 and ABD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> when NOD is ≥20. Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003–2020. The validation showed a satisfactory accuracy with a root mean square error of 1.6 K. The intercomparison with MMLSTs from geostationary satellites (containing complete LST daily cycle) presented a good agreement (biases < 0.3 K and STDs < 2 K). Compared with AIRS3STM product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.

Evaluation of Consistency Among MODIS Land Surface Temperature Products for Monitoring Surface Warming Trend Over the Tibetan Plateau

AbstractModerate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily land surface temperature (LST) products are important data sets for global warming monitoring. To reveal the consistency and differences between MODIS Terra and Aqua daily LST products for monitoring LST trends, this study used these two products to extract the mean annual surface temperature (MAST) of the Tibetan Plateau from 2003 to 2020. Then, nine MAST combinations were constructed for daytime, nighttime, and daily average conditions separately with Terra and Aqua MAST series, including Terra only, Aqua only, and the integrated one. Finally, the Mann‐Kendall trend test was applied to these series and obtained the spatiotemporal variability. The results indicated that there was high spatial consistency of the multi‐year averaged MAST of different combinations at daytime/nighttime/daily average scales. Meanwhile, there are quite similar temporal trends in terms of spatial distribution and change rate. The MAST changes are dominated by warming trends, and the areas and change rates of the significant changing pixels at different elevations, vegetation cover ranges, and land cover types presented similar distribution patterns. Although there are some discrepancies in the detailed location for pixels with significant trends among different combinations, the proportion of the pixels with the same trends of daytime/nighttime/daily‐average groups is as high as 68.88%, 66.75%, and 71.76%, respectively. These findings reveal that different MODIS LST products have similar performance in monitoring LST trends, indicating that the detection can well reflect the variation of surface thermal environment regardless of which MODIS LST product is used.

An Integrated Method for the Generation of Spatio-Temporally Continuous LST Product With MODIS/Terra Observations

Land surface temperature (LST) is a crucial parameter in the study of Land Surface processes. Currently, there are great progresses in LST retrieval based on thermal infrared (TIR) remote sensing. However, TIR-based LST suffers from serious spatial discontinuities due to clouds. Although there are methods developed to address this issue, the methods show high uncertainty in days with extremely clouds. Therefore, this study proposed an integrated method to reconstruct cloudy LSTs using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the China Land Data Assimilation System (CLDAS) LST. This method was separated into two parts according to the ratio of clear-sky pixels (RCP). On days with RCP more than 30%, a random forest reconstruction method was used to establish the complicated relationship between LST and its predicting variables, including solar radiation factor, vegetation index, water index, topographic information and latitude, and then applied to cloudy pixels to derive LSTs. For the rest days, the CLDAS LST was selected to assist the reconstruction via downscaling it to 1 km and then merged with clear-sky data to generate spatially continuous results. The proposed method was applied to the Southwest China and generate daily LST product in 2019. Validation with ground measurements demonstrated a high accuracy with the correlation coefficient changing from 0.73 to 0.88. Additionally, the reconstructed LST dataset exhibits similar temporal variability as existing all-weather satellite-based and reanalysis LST products. The findings reveal that this method shows good potential in generating gap-free LST dataset, especially for the mountain regions with heavy clouds.

Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020

Land surface temperature (Ts) and near-surface air temperature (Ta) are key parameters in multiple research fields. In this study, a global seamless (without missing values) and high-resolution (30 arcsecond spatial resolution) temperature (for both Ts and Ta) dataset (GSHTD) from 2001 to 2020 was developed. First, a method called the estimation of the temperature difference (ETD) was proposed to reconstruct both clear- and cloudy-sky Ts. A global seamless 8-day and monthly average all- and clear-sky Ts data were then created using the MODIS Ts data and the ETD method. The seamless monthly average of the mean, maximum and minimum Ta data were further developed using the seamless Ts data, in situ Ta data and Cubist machine learning algorithm. GSHTD has four main advantages. First, GSHTD includes seven types of temperature data: clear-sky daytime and nighttime Ts, all-sky daytime and nighttime Ts, and mean, maximum and minimum Ta. Second, it has global coverage and high spatial resolution. Third, using the ETD method proposed in this study, GSHTD has no missing values. Fourth, the accuracy of GSHTD is high; the average mean absolute errors (MAEs) of ETD in reconstructing the 25 × 25 and 150 × 150 pixel clear-sky daytime (nighttime) Ts data were 0.724 (0.552) and 1.024 (0.895) °C, respectively. The MAEs of ETD were on average 23.2% and 23.7% lower than those of Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) and interpolation of the mean anomalies (IMAs). The MAEs of the estimated monthly average of the mean, maximum and minimum Ta data were 0.797, 0.994 and 1.056 °C, respectively. The developed GSHTD is freely available at Middle Yangtze River Geoscience Date Center (https://cjgeodata.cug.edu.cn/#/pageDetail?id=97), which will be useful in many studies related to climate change, environmental science and ecology, and epidemiology and human health.

Integrating eco-evolutionary optimality principle and land processes for evapotranspiration estimation

Evapotranspiration (ET) plays an important role in land surface processes and global climate. ET cannot be observed directly at a large scale, and its spatial distribution is mainly obtained by ET models at present. However, the application of existing ET models is greatly hampered by a number of plant functional type (PFT)-specific empirical parameters and the difficulty in partitioning the contribution of ET from the canopy and soil. To this end, an eco-evolutionary optimality-based evapotranspiration model (EEOET) was developed in this study with some schemes used in the Noah-MP land surface model. The principle of eco-evolutionary optimality expresses the underlying acclimation/adaption of plants to diverse environments and thus greatly reduces the number of PFT-specific parameters. The Noah-MP provides schemes to partition the energy between the canopy and soil and to calculate photosynthetic and ET resistances. Owing to the above, the EEOET model, which has enhanced mechanisms and fewer PFT-specific parameters, requires inputs of daily land surface temperature, vegetation-related data (leaf area index and fractional vegetation), and soil information (soil texture and soil water content) in addition to routine meteorological data. The model output is daily ET. Global evaluation across 74 FLUXNET sites indicates that the model is capable of reasonably estimating ET for all PFTs and is also comparable to existing models that require more tunable parameters. In addition, the EEOET model is less sensitive to uncertainties in the input data than other models, making it a more robust solution for estimating ET under various conditions.