Surface Temperature Estimates Research Articles

Performance of high-resolution MUR satellite sea surface temperature data as a proxy for near-surface in situ temperatures on neotropical reefs

As ocean temperatures increase, so does the need for accurate, regular, and widespread temperature data. This is especially important for researchers studying corals, which can be pushed over their bleaching thresholds by small, sustained changes in water temperature. Satellite data products offer longitudinal estimates of sea surface temperature (SST) taken at regular intervals without the ongoing costs and geospatial limits associated with underwater temperature loggers. However, previous work indicates satellite data are sometimes characterized by significant temperature biases, particularly in stratified coastal waters. Here, we compared the foundation sea surface temperature (fSST) estimated from a highly processed, gridded satellite data set (GHRSST MUR L4 Global SST) to in situ temperature (IST) data collected from coral reef habitats in the top 20 m of the water column at 19 sites in Panama. Comparisons between Pacific and Caribbean coasts and between upwelling and non-upwelling seasons provide a robust test of the utility of this product to estimate subsurface temperatures. The mean differences between fSST and IST show that MUR fSST accurately reflects temperatures at shallow (<9 m) depths on the Caribbean coast (mean bias ≤0.55°C ± standard error (SE) 0.01°C) and during the non-upwelling season on the Pacific coast (mean bias ≤0.26°C ± SE 0.03°C). MUR fSST poorly reflects IST during the Pacific upwelling season, especially at sites 12 m and deeper. In such cases, 66% of satellite estimates deviated from ISTs by >1°C. Our findings underscore how highly processed SST data products should be ground-truthed before being used as a substitute for IST, especially in upwelling areas.

Dependency of the impacts of geoengineering on the stratospheric sulfur injection strategy – Part 2: How changes in the hydrological cycle depend on the injection rate and model used

Abstract. This is the second of two papers in which we study the dependency of the impacts of stratospheric sulfur injections on the model and injection strategy used. Here, aerosol optical properties from simulated stratospheric aerosol injections using two aerosol models (modal scheme M7 and sectional scheme SALSA), as described in Part 1 (Laakso et al., 2022), are implemented consistently into the EC-Earth, MPI-ESM and CESM Earth system models (ESMs) to simulate the climate impacts of different injection rates ranging from 2 to 100 Tg(S) yr−1. Two sets of simulations were run with the three ESMs: (1) regression simulations, in which an abrupt change in CO2 concentration or stratospheric aerosols over pre-industrial conditions was applied to quantify global mean fast temperature-independent climate responses and quasi-linear dependence on temperature, and (2) equilibrium simulations, in which radiative forcing of aerosol injections with various magnitudes compensated for the corresponding radiative forcing of CO2 enhancement to study the dependence of precipitation on the injection magnitude. The latter also allow one to explore the regional climatic responses. Large differences in SALSA- and M7-simulated radiative forcing in Part 1 translated into large differences in the estimated surface temperature and precipitation changes in ESM simulations; for example, an injection rate of 20 Tg(S) yr−1 in CESM using M7-simulated aerosols led to only 2.2 K global mean cooling, while EC-Earth–SALSA combination produced a 5.2 K change. In equilibrium simulations, where aerosol injections were utilized to offset the radiative forcing caused by an atmospheric CO2 concentration of 500 ppm, the decrease in global mean precipitation varied among models, ranging from −0.7 % to −2.4 % compared with the pre-industrial climate. These precipitation changes can be explained by the fast precipitation response due to radiation changes caused by the stratospheric aerosols and CO2, as the global mean fast precipitation response is shown to be negatively correlated with global mean atmospheric absorption. Our study shows that estimating the impact of stratospheric aerosol injection on climate is not straightforward. This is because the simulated capability of the sulfate layer to reflect solar radiation and absorb long-wave radiation is sensitive to the injection rate as well as the aerosol model used to simulate the aerosol field. These findings emphasize the necessity for precise simulation of aerosol microphysics to accurately estimate the climate impacts of stratospheric sulfur intervention. This study also reveals gaps in our understanding and uncertainties that still exist related to these controversial techniques.

Weather event characterization: a catalyst for improved spatial mapping and benefit quantification in winter road maintenance

Monitoring winter road surface conditions (RSC) is essential for optimizing winter road maintenance operations and ensuring public traffic safety and mobility. However, many existing RSC estimation methods focus primarily on local or site-specific conditions, thus lacking effectiveness in comprehensive spatial mapping under varying winter weather events. Additionally, the potential financial benefits these methods could provide to maintenance authorities remain largely unexplored. This study addresses these gaps by introducing a refined methodology that harnesses the K-Means algorithm to characterize various weather events – a significant stride towards improving the generalizability and accuracy of road surface temperature (RST) estimations. The study also incorporates regression kriging (RK), a renowned geostatistical technique, as an integral component of a system for constructing an extensive spatial map of RSC across large highway networks. This strategy maximizes the use of data from existing road weather information systems (RWIS) to bridge their large spatial gaps. This study further quantifies the potential savings derived from optimally placing RWIS stations and reducing traffic collisions. The efficacy of these methods is validated through a real-world case study on two major interstate highways in Iowa, US, with RST estimation discrepancies as low as 0.619 °C. The findings also indicate potential cost savings by conserving up to 10 RWIS stations, which can be further translated into monetary savings of RWIS capital costs, traffic collisions prevention, enhanced traffic mobility and savings in maintenance material usage.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

MIS 5e sea surface temperature estimation; a multi-proxy approach using a marine macrofossil assemblage (Mar Piccolo, Gulf of Taranto, Southern Italy)

It is reported a multi-proxy palaeoclimatic study conducted on a MIS 5e calcarenite from the Mar Piccolo Basin (MP), Gulf of Taranto (GT) (Central Mediterranean). The calcarenite returned a rich malacofauna consisting of 120 extant species, including four of the tropical Senegalese Fauna, today absent in the Mediterranean. The biogeographic-climatic affinity of the assemblage shows, compared to today, a double percentage of warm affinity species, while the cold affinity species are nearly equally represented, indicating a warmer but not strictly tropical SST. This is confirmed by the most recurring preferred SST ranges of the assemblage, indicating an average of 20 °C. The skeletal compositions of five well-preserved molluscan and coral specimens were analyzed for trace elements and stable isotopes for further mean SST estimations. From the comparison of the results of several equations available in literature, it appears that only some SST estimations are realistic, converging into similar values of, on average, 20.8 ± 0.9 °C. Considering all the used proxies, the MIS 5e SST difference compared to today falls in the range 1.2–2.0 °C for the GT (being a more reasonable scenario) and 2.0–2.8 °C for the MP. This is not a firmly tropical-like SST setting as suggested by the sole Senegalese fauna, indicating at least 2.7 °C–3.5 °C more than today's GT and MP, respectively. The approximations and assumptions made for obtaining SST values with any single proxy indicate the need of a multi-proxy approach to define the best SST estimation.

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

Estimation of land surface temperature from Landsat-8 OLI: comparative analysis of two periods for Duzce in Turkey

Estimation of land surface temperature from Landsat-8 OLI: comparative analysis of two periods for Duzce in Turkey

Optimizing laser-based micro-cutting for PMMA microfluidic device fabrication: thermal analysis and parameter optimization

Abstract Laser processes have gained popularity in microfluidic device fabrication. This study aims to determine the optimal parameters for laser-based micro-cutting to achieve the desired width, depth, profile, and material removal, considering the thermal properties of PMMA. A CNC CO2 laser was used, and 29 trials tested various speed and power combinations. Two theoretical models based on trial results focused on depth and width. COMSOL Multiphysics FEA software estimated surface temperature. Theoretical depth estimation matched experimental data more accurately when the P/S ratio was below 0.15 and the scanning speed was set at 500 mm/s or 750 mm/s. At 500 mm/s, width estimation was most accurate, up to 30 W. At 750 mm/s, experimental width exceeded predictions. Material removal increased proportionally with increasing P/S ratio, but beyond a threshold of 0.15, material removal remained nearly constant despite rising heat input. Laser-cut track shape varied, resembling a ‘U’ at lower and a ‘V’ at higher ratios. The groove shape transitioned from ‘U’ to ‘V’ when the temperature surpassed 1200 K. A V-shaped groove required a temperature exceeding 1500 K. Optimization confirmed a microchannel depth of 0.197 mm, width of 0.256 mm, and ‘U–V’ channel shape achievable at 30 W and 200 mm/s scanning speed, with a surface temperature of 1325 K.

Improving monthly mean land surface temperature estimation by merging four products using the generalized three-cornered hat method and maximum likelihood estimation

Land surface temperature (LST) is a critical parameter associated land surface energy and water balance at the land–atmosphere interface. Monthly mean LST (MMLST) is used not only as an input parameter for land surface models, but also as an indicator for general climate trend studies. Four MMLST products (i.e., NESDC, TPDC, ZENODO, and ERA5) were generated by different research teams and institutes using different algorithms. In this study, a new method was presented to improve MMLST estimation by merging these four products using the generalized three-cornered hat (TCH) method and maximum likelihood estimation (MLE). The generalized TCH method was used to simultaneously calculate the relative uncertainties of these four MMLST products during the period 2003–2019. In general, the smallest uncertainties are obtained for the TPDC MMLST product in most areas of the globe, whereas the largest uncertainties are achieved for the ERA5 MMLST product, especially in high-latitude regions. The MLE method was used to merge these four MMLST products and to generate a new MMLST product in consideration of the uncertainty of each individual MMLST product. In situ measurements at 25 stations from the AmeriFlux and SURFRAD networks during the period 2003–2019 were used to evaluate the accuracies of the individual and merged MMLST products. The largest root mean squared error (RMSE) of approximately 2.4 K is obtained for the TPDC MMLST product, whereas the smallest RMSE of around 1.6 K is reached for the merged MMLST product. The NESDC, ZENODO, and ERA5 MMLST products have similar RMSE of approximately 2 K. The results indicate that the merged MMLST product outperforms each individual MMLST product. The merged MMLST product shows promising prospects for climate- and global-scale studies and applications.

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

AbstractThe response of vegetation canopy conductance (gc) to changes in moisture availability () is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale gc observations exist. Here, we leverage a collection of satellite, reanalysis and station‐based near‐surface air and surface temperature estimates, which are physically and statistically linked to due to the local cooling effect of gc through transpiration, to develop a novel emergent constraint of in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate by ∼33%, particularly in grasslands, croplands, and savannas in semi‐arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce gc when soil moisture decreases. As gc controls carbon, water and energy fluxes, the misrepresentation of modeled contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained gc on both soil moisture dynamics and the biosphere‐atmosphere feedbacks that affect local temperatures and regional weather patterns.

Determination of the thermal state of a block gravel filter during its transportation along the borehole

Purpose. Development of a methodology for determining the thermal state of block inverse gravel filters manufactured using low-temperature technology during their transportation in a well based on computer and mathematical modeling. Methods. The study of hydrodynamic processes occurring during the transportation of the filter in the borehole, as well as the calculation of thermal fields in the filter body, was performed using the Ansys Fluent software package. To determine the effective thermophysical characteristics of the filter, the approaches of the heat transfer theory in porous media were applied. To investigate the thermal conditions of the filter at heat exchange with well fluid, the use of analytical solution of the heat conduction problem in a cylindrical shell, taking into account the properties of porous dispersed water-saturated medium, is proposed. Findings. The methodology for calculating the thermal state of a gravel filter during its operation in a well has been developed. For estimation of the filter surface temperature, the expression obtained on the basis of the analytical solution of the heat conduction equation, taking into account the characteristics of the porous filter medium, is proposed. Hydrodynamic and thermal fields in the borehole and the filter body during the filter transportation process in the borehole have been obtained. Originality. For the first time, the problem of heat exchange of a gravel inversion filter, manufactured by low-temperature technology in a well is considered. The influence of hydrothermal conditions in the well on the process of filter heating during its transportation in the well is shown. The hydrodynamic fields during the flow of the drilling mud around the inverse gravel filter are determined. Practical implications. The proposed approaches and results of the study allow to determine and can be used in the development of technological regulations for the use of block gravel filters produced by low-temperature technology for the equipment of hydrogeological wells. The methodology for modelling the process of two-phase inversion transition of aggregate state of the binding agent during transportation of inverted block-type gravel filter during well construction has been developed.

Impact of microwave observations on the estimation of Arctic sea surface temperatures

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (>58°N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers.

The effect of strong nonlinearity on wave-induced vertical mixing

Abstract. A semi-analytical solution to an advection–diffusion equation is coupled with a nonlinear wavemaker model to investigate the effect of strong nonlinearity on wave-induced mixing. The comparisons with weakly nonlinear model predictions reveal that in the case of waves of higher steepness, enhanced mixing affects the subsurface layer of the water column. A fully nonlinear model captures the neglected higher-order terms from a weakly nonlinear solution and provides a reliable estimation of the time-mean velocity field. The corrected wave-induced mass-transport velocity leads to improved estimates of subsurface mixing intensity and ocean surface temperature.

Assessment of orbital sensors in estimating sugarcane crop evapotranspiration with the “SAFER” algorithm

Understanding sugarcane water demand is important for planting planning, yield estimation, irrigation management, forecasting and water resources management. The purpose of this study was to estimate the evapotranspiration of the variety RB985476 in 2nd. re-grow, from August/2017 to September/2018, in a mechanized harvest area and with straw distribution over dystrophic red Latosolo, through the SAFER algorithm, with two passive orbital image sensors: Landsat 8 and Sentinel 2. The results demonstrated that SAFER better estimated ETc from Landsat 8 images and that remote sensing is not efficient for ETc estimation in phenological stages with low plant coverage. The sensors show divergences in surface and albedo temperature estimates and no difference in NDVI estimation was observed. The landsat 8 products are superior in evapotranspiration estimates when compared to the standards recommended in this paper.

Warming modulates the photosynthetic performance of Thalassiosira pseudonana in response to UV radiation.

Diatoms form a major component of phytoplankton. These eukaryotic organisms are responsible for approximately 40% of primary productivity in the oceans and contribute significantly to the food web. Here, the influences of ultraviolet radiation (UVR) and ocean warming on diatom photosynthesis were investigated in Thalassiosira pseudonana. The organism was grown at two temperatures, namely, 18°C, the present surface water temperature in summer, and 24°C, an estimate of surface temperature in the year 2,100, under conditions of high photosynthetically active radiation (P, 400-700 nm) alone or in combination with UVR (P + UVR, 295-700 nm). It was found that the maximum photochemical yield of PSII (Fv/Fm) in T. pseudonana was significantly decreased by the radiation exposure with UVR at low temperature, while the rise of temperature alleviated the inhibition induced by UVR. The analysis of PSII subunits turnover showed that high temperature alone or worked synergistically with UVR provoking fast removal of PsbA protein (KPsbA), and also could maintain high PsbD pool in T. pseudonana cells. With the facilitation of PSII repair process, less non-photochemical quenching (NPQ) occurred at high temperature when cells were exposed to P or P + UVR. In addition, irrespective of radiation treatments, high temperature stimulated the induction of SOD activity, which partly contributed to the higher PSII repair rate constant (Krec) as compared to KPsbA. Our findings suggest that the rise in temperature could benefit the photosynthetic performance of T. pseudonana via modulation of its PSII repair cycle and protective capacity, affecting its abundance in phytoplankton in the future warming ocean.

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.

Comparing CMIP6 Climate Model Simulations of Annual Global Mean Temperatures to a New Combined Data Product

AbstractA new statistical approach to validating climate models is introduced. First, five observational estimates of global mean surface temperature with estimated standard errors are combined into one data product, latent observed annual global temperature anomalies for the years 1880–2014, using a Bayesian hierarchical statistical approach. Summarizing these observed anomalies, estimates of smooth trend, levels of warming, and residual dependence as summarized by the spectral density function, are provided with simultaneous 95% credible bands. Then, corresponding estimates of smooth trend, levels of warming, and residual dependence are produced for sixth Climate Model Intercomparison Project (CMIP6) historical simulations analyzed at the annual global temperature anomaly scale, and compared to these bands. Among our results, we find that 93 out of the 318 CMIP6 historical model runs contain trends fitting inside the simultaneous bands for the smooth trend constructed from the data products, and for residual temporal dependence 69 out of 318 model runs contain spectral density functions that are within the corresponding data‐product‐based‐bands. We estimate the mean global temperature increase from 1995–2014 relative to 1880–1899, from the data product, to be 0.896°C with a 95% credible interval of between 0.877 and 0.915. We find that 14 CMIP6 model runs agree with this interval, 197 model runs lead to a smaller temperature increase globally, and 107 model runs lead to a larger temperature increase.

Challenges in the Detection and Attribution of Northern Hemisphere Surface Temperature Trends Since 1850

Since 2007, the Intergovernmental Panel on Climate Change (IPCC) has heavily relied on the comparison between global climate model hindcasts and global surface temperature (ST) estimates for concluding that post-1950s global warming is mostly human-caused. In Connolly et al., we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance (TSI) and ST data sets. We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST, one could (prematurely) conclude anything from the warming being “mostly human-caused” to “mostly natural.” Richardson and Benestad suggested our analysis was “erroneous” and “flawed” because we did not use a multilinear regression. They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC’s attribution statement. They also objected that many of the published TSI data sets were out-of-date. However, here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series, the original conclusions of Connolly et al. are confirmed for all five ST data sets. Therefore, it is still unclear whether the observed warming is mostly human-caused, mostly natural or some combination of both.

Assessing high resolution thermal monitoring of complex intertidal environments from space: The case of ECOSTRESS at Rias Baixas, NW Iberia

Currently, thermal sensors on satellites and other orbital platforms with high spatial resolution designed to monitor the oceans are often insufficient to assess the surface temperature of small bodies of water or within the coastal boundary layer. As the accuracy and sensitivity of remote sensing satellites improve, the demand for more accurate and up-to-date basic data sets for calibration increases. The quality of the thermal data collected by ECOSTRESS on the International Space Station allows the characterization of thermal stress levels in coastal ecosystems with a high spatial resolution of 70 m and a return time from hours to 5 days. This study focused on the calibration of ECOSTRESS estimates with in situ surface temperature data from sensors installed at 3 cm depth in the sediment on the intertidal muddy sandflats of three of the Rías Baixas in Galicia, NW Iberian Peninsula, from 2019 to 2021. A final number of 45 ECOSTRESS temperature images were analyzed. From these, 20% contained substantial georeferencing errors which had to be corrected manually with GIS software tools. We applied the Fourier's law of Heat Conduction to derive surface estimates from loggers sub-surface measurements that could be directly compared with ECOSTRESS data. Overall, a good calibration which explained more than 80% of ECOSTRESS temperature estimates for the whole dataset was obtained, but with an intrinsic cold bias around 1.39 °C. When temperatures at a depth of 3 cm were used, the linear fit became worse and the negative bias increased to 1.49 °C. Closer inspection revealed that night measurements were responsible for this larger deviation, as ECOSTRESS estimates became much colder compared to within sediment measurements because of the combined effect of the instrument intrinsic bias and nocturnal surface cooling. The best calibration was obtained when surface estimates were calculated just for the nighttime, as the cold bias decreased to 0.93 °C. More importantly, during hot daytime conditions in emersion above 20 °C, ECOSTRESS data matched surface temperature estimates, probably because of a better performance of ECOSTRESS algorithm at dry surfaces with lower emissivity. Thus, during the most ecologically relevant periods when high temperatures could drive thermal stress in many commercially-important bivalve species, ECOSTRESS provides accurate surface estimates that can be used to derive sub surface temperatures at the depths at which the different burrowing organisms live. We thus conclude that this instrument constitutes an important global tool to examine thermal stress at an unprecedented spatial scale for complex sea-land boundary systems.

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. Overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.