Land Surface Temperature Observations Research Articles

Surface temperature assimilation improving geostationary meteorological satellite surface-sensitive brightness temperature simulations over land

This study explores a possibility of improving Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) simulations over land by assimilating land surface temperature (LST) observations from the National Basic Meteorological Observing Stations of China. The Gridpoint Statistical Interpolation 3D-Var regional data assimilation (DA) system is modified to add LST as a new control variable and its background error variances, horizontal correlations and cross-correlations. The background covariances of LST with other control variables are calculated separately for daytime and nighttime samples in summer and winter seasons. A control experiment (ExpCTL) and three LST DA experiments with (ExpLST) and without (ExpLST_NBC) bias correction or with an average of LST within 2° × 2° grid boxes (ExpLST_SO) are conducted. Considering the fact that surface station observations are point measurements while the satellite TBs measure the total radiation effect of earth's surface within fields-of-view, a bias correction is found necessary for LST DA during daytimes (ExpLST). The biases are quantified by the differences from the Moderate-resolution Imaging Spectroradiometer LST retrievals to compensate for the representative differences. The analyzed fields are then used as input to the Community Radiative Transfer Model to simulate TBs of AHI surface-sensitive channels over land. A long-period statistics shows that ExpLST significantly reduces the observations minus simulations (OB) biases and standard deviations of surface-sensitive TBs in terms of reducing the diurnal variations and season dependences of TB biases over different surface types, which also outperforms ExpLST_NBC and ExpLST_SO at daytime. This study suggests a potential benefit of combining the use of LST observations for assimilating surface-sensitive infrared TBs.

The Operational and Climate Land Surface Temperature Products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking.

Utility of thermal remote sensing for evaluation of a high‐resolution weather model in a city

AbstractProgress in high‐resolution numerical weather prediction (NWP) for urban areas will require new modelling approaches and extensive evaluation. Here, we exploit land surface temperature (LST) data from Landsat‐8 to assess 100 m resolution NWP for London (UK) on four cloud‐free days. The LST observations are directional radiometric temperatures with non‐negligible uncertainties. We consider the challenges of informative comparison between the Landsat LST and the NWP scheme's internal characterisation of the complete surface temperature. The LST spatial coverage allows large‐scale observation–model differences to be explored. In one case, obvious spatial artifacts in the NWP surface temperature are observed relative to the Landsat LST. These are found to be related to the NWP's initial method of downscaling of soil moisture using soil properties. Updated model runs have higher spatial correlation between model and Landsat LST. In cases where meteorological conditions favour the formation of horizontal convective rolls, warmer air temperatures associated with updraughts in the mixed layer extend inappropriately to the urban surface. This manifests as warm stripes in the model surface temperature that are not present in the Landsat LST. NWP–Landsat LST differences are larger in more built‐up areas on days nearer summer solstice. This is largely attributed to urban thermal anisotropy, as Landsat preferentially views warmer urban surfaces, whereas the model LST represents all surfaces. We evaluate two approaches to quantify this sampling effect, but further work is needed to fully constrain it and facilitate more informative model evaluation.

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions.

Downscaling Land Surface Temperature Derived from Microwave Observations with the Super-Resolution Reconstruction Method: A Case Study in the CONUS

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm.

Spatial Characteristics and Temporal Trend of Urban Heat Island Effect over Major Cities in India Using Long-Term Space-Based MODIS Land Surface Temperature Observations (2000–2023)

The alteration of the Earth’s surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth’s microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects.

Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion

One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex and dynamic process, both temporally and spatially, necessitating LST observations with high spatio-temporal resolution. Presently, none of the existing spaceborne thermal sensors can provide quasi-daily field-scale LST observations, prompting the development of methods for data fusion (thermal sharpening) of observations from various shortwave and thermal sensors to meet this spatio-temporal requirement. Previous research has demonstrated the effectiveness of combining shortwave-multispectral Sentinel-2 observations with thermal-infrared Sentinel-3 observations to derive daily, field-scale LST and ET estimates. However, these studies also highlighted limitations in capturing the distinct thermal contrast between cooler LST in irrigated agricultural areas and the hotter, adjacent dry regions. In this study, we aim to address this limitation by incorporating information on thermal spatial variability observed by Landsat satellites into the data fusion process, without being constrained by infrequent or cloudy Landsat thermal observations and while retaining the longwave radiance emission captured by the Sentinel-3 thermal sensor at its native resolution. Two approaches are evaluated, both individually and as a complementary combination, and validated against in situ LST measurements. The best performing approach, which leads to reduction in root mean square error of up to 1.5 K when compared to previous research, is subsequently used to estimate parcel-level actual evapotranspiration. The ET modeling process has also undergone various improvements regarding the gap-filling of input and output data, input datasets and code implementation. The resulting ET is validated using lysimeters and eddy covariance towers in Spain, Lebanon, Tunisia, and Senegal resulting in minimal overall bias (systematic underestimation of less than 0.07 mm/day) and a low root mean square error (down to 0.84 mm/day) when using fully global input datasets. The enhanced LST sharpening methodology is sensor agnostic and should remain relevant for the upcoming thermal missions while the accuracy of the modeled ET fluxes is encouraging for further utilization of observations from Sentinel satellites, and other Copernicus data, for monitoring SDG indicator 6.4.1.

Thermal pattern of Tatun volcanic system by satellite-observed temperatures and its correlation with earthquake magnitudes

The land surface temperature (LST) of volcanoes detected from satellite sensors reflects the thermal status of heat sources in the subsurface. Volcanic earthquakes occur as magma and volcanic fluids transport to the surface from depth. Thus, both LST and earthquake magnitude are key parameters to the study of active volcanoes. Here we investigate the volcanic status of Tatun Volcanic Group (TVG) based on LST and seismic observations. The Earth-observing satellites onboard thermal sensor derived land surface temperature, and the seismic records retrieved volcanic earthquake magnitude are used to delineate the past and current pattern of volcanic activity plus the future trend of the TVG. The spatiotemporal distribution of LST and volcanic earthquake magnitude in TVG are analyzed. The high-similarity trends of the 4-decade LST time series and 3-decade earthquake magnitude time series are inspected. The retrieved surface thermal pattern shows the non-steady-state nature of the subsurface thermal sources at this volcanic complex. The LST trend exhibits a rather positive correlation with the energy released from volcanic earthquakes and consequently, the presumption on the connection between LSTs and earthquakes is validated.

Simulation of urban thermal anisotropy at remote sensing pixel scales: Evaluating three schemes using GUTA-T over Toulouse city

The directional variation in upwelling thermal radiance (known as ‘thermal anisotropy’) affects our understanding of urban land surface temperature (LST) from remote sensing observations. Parametric models have been proposed to quantify and potentially correct the thermal anisotropy from satellite systems. The accurate specification of the coefficients is critical for broadening applications of parametric models. However, the current research is limited to only one study area or one particular approach which often does not sufficiently offer transformative understanding for effective applications over other metropolitan areas. This study focuses on systematically evaluating schemes that determine the model coefficients. We use a geometric model to simulate Urban Thermal Anisotropy Time-series (GUTA-T) and propose different approaches to determine the physically-interpretable parameters. The model and solution schemes reduce the uncertainties caused by observational errors and simplifications of complex urban surfaces. The three schemes include estimating parameter values using component urban surface temperatures obtained from model simulations (Scheme #1mod) or field observations (Scheme #1obs) (‘forward’ approach), inverting parameter values from known anisotropy (Scheme #2) and from multi-angular LST observations (Scheme #3) (‘backward’ approach). The three schemes were separately evaluated and compared to an independent airborne dataset. These schemes have consistent results. The root mean square errors (RMSE) between LST anisotropy from the three schemes and airborne measurements are ranked as: Scheme #3 (1.0 K) < Scheme #2 (1.2 K) < Scheme #1 (based on field data) (1.3 K) < Scheme #1 (based on TUF3D simulation) (1.5 K), whereas the overall amplitude of the variation of directional temperature averaged over the 5 flights is 11.9 K. The inverted parameter values from the three schemes agree well with the results from field measurements. The three schemes have advantages and disadvantages, and are expected to be combined depending on the available input data. These schemes represent multiple options to quantify and/or correct the anisotropic impact from remote sensing LST for urban applications.

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.

An Observational, Irrigation-Sensitive Agricultural Drought Record from Weather Data

Abstract The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land–atmosphere coupling—surface flux equilibrium (SFE) theory—to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. Significance Statement Irrigation grew profoundly in the United States over the twentieth century, increasing the resilience of American agriculture to drought. Yet observational records of agricultural drought, and its response to irrigation, are limited to the satellite era. Here, we show that a common measure of agricultural drought (the evaporative fraction, EF) can be estimated using widespread weather data, extending the agricultural drought record decades further back in time. We show that EF estimated using our approach is both sensitive and specific to the occurrence of irrigation, unlike an alternative derived from a reanalysis.

Regional climate trend analyses for Aqua MODIS land surface temperatures

ABSTRACT Land surface temperature (LST) is the skin surface temperature of the Earth’s land surface and is an essential climate variable (ECV) that critically characterizes the Earth’s climate and surface energy processes. This is the first regional trend analysis for LST with uncertainties, using a stable LST climate data record suitable for climate trend analyses: the Aqua Moderate Imaging Spectroradiometer (MODIS) dataset (MYDCCI) produced in the European Space Agency (ESA) LST Climate Change Initiative (LST_cci) Project. The quality of the trends is verified here for Western Europe, for which consistent trends per decade of 0.5 K and 0.57 K across daytime and night-time data, respectively. Eight regions representative of a range of biomes and, including the Amazon, Western U.S.A., Greenland, the Sahel, Siberia, China, India, and Australia, have been analysed. Our most significant findings show substantial LST increases for Siberia and the Amazon, as well as evidence of recent increasing trends for the Western U.S.A. Siberia showed the most substantial daytime and night-time changes per decade of +0.87 K and +0.93 K, respectively. Furthermore, the Amazon and Western U.S.A. showed significant daytime LST trend increases of +0.82 K and +0.51 K, respectively. Western Europe and Western U.S.A. show the largest night-time change of +0.57 K and +0.54 K, respectively. India presents an atypical time series with a decreasing daytime and increasing night-time trend per decade of −0.62 K and +0.44 K, respectively, consistent with reported air temperatures. Overall the results show significant positive regional LST trends, against a background of both strong seasonal cycles and intermittent disruptive events, which demonstrate the importance of continued LST observations. The results also incentivize remote sensing science to derive ever more rigorous LST datasets and to continue to investigate emissivity and error correlations to allow improved trend analyses for smaller regions and individual locations.

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

The Antarctic Amplification Based on MODIS Land Surface Temperature and ERA5

With global warming accelerating, polar amplification is one of the hot issues in climate research. However, most studies focus on Arctic amplification, and little attention has been paid to Antarctic amplification (AnA), and there is no relevant research based on MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature observations. Compared with 128 stations’ observations, MODIS can capture the variations in temperature over Antarctica. In addition, the temperature changes in Antarctica, East Antarctica, West Antarctica and the Antarctic Peninsula during the period 2001–2018 reflected by the MODIS and ERA5 are basically consistent, and the temperature changes in Antarctica are negatively correlated with the Southern Annular Mode. AnA occurs under all annual and seasonal scales, with an AnA index greater than 1.27 (1.31) from the MODIS (ERA5), and is strongest in the austral winter and weakest in summer. AnA displays regional differences, and the signal from the MODIS is similar to that from ERA5. The strongest amplification occurs in East Antarctica, with an AnA index greater than 1.45 (1.48) from the MODIS (ERA5), followed by West Antarctica, whereas the amplified signal is absent at the Antarctic Peninsula. In addition, seasonal differences can be observed in the sub regions of Antarctica. For West Antarctica, the greatest amplification appears in austral winter, and in austral spring for East Antarctica. The AnA signal also can be captured in daytime and nighttime observations, and the AnA in nighttime observations is stronger than that in daytime. Generally, the MODIS illustrates the appearance of AnA for the period 2001–2018, and the Antarctic climate undergoes drastic changes, and the potential impact should arouse attention.

Thermal pattern at kueishantao (KST) volcano of Taiwan from satellite-observed temperatures and its implication

Abstract Kueishantao (KST) is an active volcanic island off the northeastern coast of Taiwan. Tectonically, it lies in the south of the Okinawa Trough and opposite to the Ilan plain, in which is the southwestern end of the trough. KST provides a convenient observation site for the subsurface geological and geothermal activity and mechanism at its proximity. Land surface temperature (LST) of volcanoes detected from satellite sensors reflects the thermal status of heat sources in the subsurface. LST thus is a key parameter to the understanding of the volcanic process and geothermal resources. This research utilizes the satellite-observed multi-temporal land surface temperature imagery in 1999–2022 on the Kueishantao volcano of Taiwan to explore its geothermal state. The U.S. NASA Earth-observing satellites onboard three thermal sensors (i.e., Landsat ETM+, Terra ASTER, and Aqua/Terra MODIS) derived time series of land surface temperature from 1999 are employed to define the past and current pattern of geothermal activity plus the future trend of the KST. The spatiotemporal LST distribution of KST volcano is explored and analyzed. The spatial LST distribution of the KST volcano indicates that LST anomaly areas are mainly located on the southeast island, which is well correlated with the possible magma reservoir location from previous geophysical and geological surveys. An increasing trend of two-decade LST time series is revealed from all three thermal sensors. The retrieved surface thermal pattern shows non-linear temperature variations that imply the non-steady-state nature of the subsurface thermal sources at this volcano. In summary, satellite LST observations facilitate the understanding on the subsurface magmatic processes of active volcanoes for further management of geothermal resources.

Global mapping of urban thermal anisotropy reveals substantial potential biases for remotely sensed urban climates

Urban thermal anisotropy (UTA) drastically impacts satellite-derived urban surface temperatures and fluxes, and consequently makes it difficult to gain a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using multi-angle land surface temperature (LST) observations from 2003 to 2021 provided by Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes (H) by 45.4% and surface urban heat island intensity (Is) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. Practitioners can limit the biases of H and Is within ±10% by using LSTs from sensor VZAs within ±30°. We also find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. These findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide.

A stepwise framework for interpolating land surface temperature under cloudy conditions based on the solar-cloud-satellite geometry

Thermal infrared land surface temperature (LST) data from satellites often contain extensive missing values due to high cloudiness degree, which severely hinders their use in applications. Despite the many methods developed, common methods, such as fusion with microwave or reanalysis data and the surface energy budget approach, still remain subject to important limitations and uncertainties, such as dependency on coarse resolution data and difficulty in interpolation for large-scale missing LST data. In this study, we proposed a stepwise framework for estimating missing cloudy-sky LST values of Moderate Resolution Imaging Spectroradiometer (MODIS) from informative samples owing to the solar-cloud-satellite geometry (SCSG) effect, by which satellite imagery records the cloudy-sky LST values of a portion of pixels. We first estimated the clear-sky LST equivalents for all cloud-affected pixels via a similarity-based approach and then determined unknown LSTs for cloudy pixels by training a machine-learning model on cloudy-sky LST values observed owing to the SCSG effect. We demonstrated the utility of this approach by using MODIS/Aqua daytime LST data over Qinghai-Tibet Plateau (QTP) and validated the interpolation results against representative in-situ LST observations and two recently published all-weather LST datasets. When compared to the corresponding in-situ measurements, the interpolated cloudy-sky LST values showed satisfactory accuracy with a mean absolute error (MAE) value of 3.99 °C and a coefficient of determination (R2) value of 0.74, while the MODIS/Aqua clear-sky LST values led to an MAE value of 2.66 °C and an R2 value of 0.86. Compared to the two all-weather LST datasets, results of this study showed the highest accuracy over the data-gap-filled regions in terms of all quantitative performance metrics, more natural transition textures, and better representation of seasonal characteristics. The proposed framework has the advantage of relying on the MODIS family data and handling extensive missing data as well as triggers opportunities to leverage the SCSG effect to produce high-quality all-weather LST data.

Monitoring of Land Surface Temperature in Surabaya, Indonesia from 2013-2021 Using Landsat-8 Imagery and Google Earth Engine

Surabaya is one of the big cities in Indonesia which experiences population increase every year due to urbanization. Population growth can cause changes in land cover such as vegetation into built-up areas and an increase in energy consumption. Changes in land cover and increased in energy consumption resulted changes in the average temperature of urban areas. This change can disrupt health and thermal comfort. Temperature changes can be analyzed using remote sensing technology by observing land surface temperature (LST). LST observations can be carried out using Landsat-8 OLI/TIRS image. Landsat-8 has ability to record data continuously and has good resolution for thermal analysis. Image data processing was performed using Google Earth Engine (GEE) due to its advantage in computing big data without the need for good computer specifications. GEE can perform time series data calculations so that the results obtained will be more numerous and better The results showed that the temperature in the city of Surabaya tends to change every year with an average temperature of 31°C. The mean LST in Surabaya City decreased significantly in 2016 by 4°C but increased again in 2017 with an increase of 1.5°C. In the following years, the LST value in the city of Surabaya continued to increase with a constant increase every year. There were more than 20 districts which have temperatures above average. Those districts were located in the center of Surabaya city extending from north to south. In the other side, the districts in the west and east of Surabaya have temperatures below average. There were 17districts experienced a decrease in temperature and 14 districts experienced an increase in temperature. Benowo, Tandes, and Pakal districts were the areas that experienced an average temperature increase. Meanwhile, the districts that experienced the highest decrease were Tegalsari, Wonokromo, and Genteng

The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.

A method for estimating surface soil moisture from diurnal land surface temperature observations over vegetated regions: A preliminary result over an AmeriFlux site and the REMEDHUS network

Most current optical/thermal-based surface soil moisture (SSM) retrieval methods only utilize the instantaneous measurements made by onboard polar-orbit satellites, and they require soil texture as prior knowledge to obtain the volumetric SSM content. A recent study developed a diurnal land surface temperature (LST)-based SSM retrieval method to directly estimate the volumetric SSM content without the knowledge of soil texture. However, most subsequent studies have only examined the use of this method on bare soils. Therefore, the present study aims to investigate the feasibility of the method over vegetated regions with both experimental data and satellite observations. First, the impacts of vegetation on SSM retrieval were investigated through physics-based Common Land Model simulations and high accuracy with an overall root mean square error (RMSE) of 0.010 m3m−3 was achieved when the underlying surface was covered by moderate fractional vegetation cover from 0 to 0.6 with a given soil texture. Based on the simulated results, the SSM was then estimated using data from an AmeriFlux site dominated by crops during the growing season in 2018; the results were further verified using in-situ measurements, and this showed that the estimated SSM correlated well with in-situ measurements (with an RMSE of 0.078 m3m−3). A further evaluation of the Meteosat Second Generation (MSG)-derived SSM at the pixel scale was made using the proposed method, and the results revealed an overall acceptable accuracy (RMSE of 0.074 m3m−3 and bias of 0.071 m3m−3) against the SMAP/Sentinel-1 SSM product at the same spatial resolution of 3 km.