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Rapid 21st century urbanization combined with anthropogenic climate warming are significantly increasing heat-related health threats in cities worldwide. In Los Angeles (LA), increasing trends in extreme heat are expected to intensify and exacerbate the urban heat island effect, leading to greater health risks for vulnerable populations. Partnerships between city policymakers and scientists are becoming more important as the need to provide data-driven recommendations for sustainability and mitigation efforts becomes critical. Here we present a model to produce heat vulnerability index (HVI) maps driven by surface temperature data from National Aeronautics and Space Administration’s (NASA) new Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared sensor. ECOSTRESS was launched in June 2018 with the capability to image fine-scale urban temperatures at a 70 m resolution throughout different times of the day and night. The HVI model further includes information on socio-demographic data, green vegetation abundance, and historical heatwave temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua spacecraft since 2002. During a period of high heat in July 2018, we identified the five most vulnerable communities at a sub-city block scale in the LA region. The persistence of high HVI throughout the day and night in these areas indicates a clear and urgent need for implementing cooling technologies and green infrastructure to curb future warming.

While remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior. Plain Language Summary Belowground processes, like how roots use soil water for transpiration and how soil water is used for soil evaporation, remain as large uncertainties in the Earth system. This is because these belowground processes are difficult to widely observe and thus tend to only be studied at sparsely located field sites. We address these limitations here by simultaneously using high resolution (70 m scale) remote sensing measurements of land surface temperature (LST), which integrates ecosystem soil and vegetation behavior and water use, and ground networks of soil moisture (SM) measurements between 5 and 100 cm. We find that the relationship between LST and SM at different soil depths shows how ecosystems use moisture across the soil profile. Across vegetation types, our analysis suggests most water use originates from upper soil layers where soil evaporation occurs and where roots presumably preferentially draw water under nominal climatic conditions. Grassland sites tend to have a greater preference for use of moisture in upper soil layers than for forested sites, which show an increase in deeper water use below 50 cm. We therefore demonstrate that such methods can reveal how ecosystems respond to SM across the rootzone and across a range of globally available sites. Key Points High resolution satellite retrievals of land surface temperature can reveal ecosystem water use when coupled with soil moisture (SM) networks Across vegetation types, evaporation tends to mainly use water from the upper soil layers with a decrease of SM use with depth Grassland sites tend to have more frequent ecosystem water use of upper layer SM than forested sites

This study developed and evaluated 30‐m daily evapotranspiration (ET) estimates using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) model with ECOSTRESS, Moderate MODIS, harmonized Landsat Sentinel‐2 (HLS) imagery, ERA5‐Land reanalysis, and eddy covariance measurements. The new daily 30‐m ET showed significantly improved performance (overall, r = 0.8, RMSE = 1.736, KGE = 0.466) at 145 EC sites over contiguous United States compared to the current 70‐m ECOSTRESS ET (overall, r = 0.485, RMSE = 4.696, KGE = −0.841). A deep neural network postprocessing model trained with ET measurements from EC sites further improved the performance on test sites that were not used for model training (overall, r = 0.842, RMSE = 0.88, KGE = 0.792). The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. In conclusion, higher spatial resolution satellite imagery of vegetation characteristics and higher temporal resolution radiation data, combined with continent‐wide EC measurements and deep learning, provided substantial added value for improving ET estimations at the field scale (30‐m). Key Points We developed and evaluated 30‐m evapotranspiration (ET) estimates driven by ECOSTRESS, MODIS, harmonized Landsat Sentinel‐2, and ERA5‐Land data The new 30‐m ET estimates showed significantly improved performance compared to the current 70‐m ECOSTRESS ET over contiguous United States Deep neural network with eddy covariance measurements further improved 30‐m ET estimates at unmeasured locations

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes.

Operational coarse-resolution satellite thermal sensors designed for global oceans are often insufficient for evaluating surface temperature of small water bodies. Here, the quality of the thermal data, collected by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), over several South Florida estuaries, Chesapeake Bay, and Lake Okeechobee is evaluated using both in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) Sea Surface Temperature (SST) data. Overall, for SST between ~6 and ~32 °C, ECOSTRESS LST (Land Surface Temperature, used as a surrogate for SST in this study) appears to be slightly underestimated, with the underestimation being more severe at night (−1.13 °C) than during the day (−0.64 °C), in spring and summer (−1.25 ± 1.39 °C) than in autumn and winter (−0.57 ± 0.98 °C), and after May 2019 when two of the five bands failed. The root-mean-square uncertainties of ECOSTRESS SST are generally within 1–2 °C. Spatial analysis further suggests that ECOSTRESS SST covers waters closer to shore and reveals more spatial features than MODIS, with comparable image noise. From these observations, after proper georeferencing and empirical correction of the negative bias, ECOSTRESS SST may be used to evaluate the thermal environments of small water bodies, thus filling gaps in the coarse-resolution satellite data.

Satellite sea surface temperature (SST) is critical for describing marine environments. Traditional SST data, such as those provided by the Group for High Resolution Sea Surface Temperature (GHRSST) program, are valuable, but have a relatively coarse spatial resolution for mapping coral reef thermal environments. Hence, fine resolution SST from orbit would be of great utility to the coral reef research community and speed the pathway to an increased understanding of how, when, and where thermal stress afflicts individual reefs. Such data would support adaptive management, especially so for the design of marine protected areas. Flying aboard the International Space Station, the NASA ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) instrument may already fill this niche with a spatial resolution 204 times finer than GHRSST. To evaluate ECOSTRESS thermal data over reef environments, we deployed 21 temperature loggers over three years across two reef sites in the Red Sea. We compared temperature retrievals from both the coarse resolution GHRSST and the fine resolution, experimental, ECOSTRESS, to this in-situ logger dataset. While temperature data from both orbital platforms correlated strongly with the logger recordings, only ECOSTRESS, with its 70-m pixels, could construct thermal microclimate maps capturing the dynamic temperature fluctuations experienced by our studied reefs. We contend that ECOSTRESS represents a significant advancement in the capability to monitor heat stress on reefs from orbit.

Radiance data from ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), which is the first of a planned virtual constellation of wide-swath ultra-high-resolution thermal satellites, were used to test the concept of on-orbit cross-calibration based on the Global Space-based Inter-Calibration System (GSICS) with the Infrared Atmospheric Sounding Interferometer (IASI) as the reference. Validation of the results was performed using comparisons of corrected ECOSTRESS radiances with strictly independent data from IASI and the Cross-Track Infrared Sounder (CrIS) and with RTTOV radiative transfer simulations of clear-sky observations in iQuam (the NOAA in situ sea surface temperature quality monitor database). ECOSTRESS has known brightness temperature biases in ECOSTRESS Collections 1 and 2, and the biases of Collection 2 are expected to remain in Collection 3 because it retains the Collection 2 radiance calibrations. Our approach reduced both the brightness temperature bias and the temperature dependence of the bias in both Collections 1 and 2 by one to two orders of magnitude. The necessary radiance correction coefficients are provided. The results support the proof-of-concept on-orbit cross-calibration method based on GSICS.

Georeferencing accuracy plays a crucial role in providing high-quality ready-to-use remote sensing data. The georeferencing of nighttime thermal satellite imagery conducted by matching to a basemap is challenging due to the complexity of thermal radiation patterns in the diurnal cycle and the coarse resolution of thermal sensors in comparison to sensors used for imaging in the visual spectral range (which is typically used for creating basemaps). The presented paper introduces a novel approach for the improvement of the georeferencing of nighttime thermal ECOSTRESS imagery: an up-to-date reference is created for each to-be-georeferenced image, derived from land cover classification products. In the proposed method, edges of water bodies are used as matching objects, since water bodies exhibit a relatively high contrast with adjacent areas in nighttime thermal infrared imagery. The method was tested on imagery of the East African Rift and validated using manually set ground control check points. The results show that the proposed method improves the existing georeferencing of the tested ECOSTRESS images by 12.0 pixels on average. The strongest source of uncertainty for the proposed method is the accuracy of cloud masks because cloud edges can be mistaken for water body edges and included in fitting transformation parameters. The georeferencing improvement method is based on the physical properties of radiation for land masses and water bodies, which makes it potentially globally applicable, and is feasible to use with nighttime thermal infrared data from different sensors.

Context Tropical forest regions are being transformed to other land covers at a high rate, often resulting in landscapes with heterogeneous vegetation structures. In combination with environmental factors this may influence ecosystem services, including evapotranspiration (ET). Objectives We aimed to predict the spatial variability of ET using spaceborne observations in a heterogenous tropical landscape, with a particular focus on elucidating the importance of vegetation structural characteristics and their interactions with environmental factors. Methods The study region was located in northeastern Madagascar. Daily ET was retrieved from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). Meteorological, topographical, soil and vegetation structure predictor variables were procured from various open sources. We applied forward feature selection and target oriented cross validation to address potential spatial autocorrelation and used SHAP analyses for elucidating interactions. Results Random forest models achieved high accuracies in the spatial prediction of ET, with R 2 values between 0.7 and 0.9 across different days. The explainable machine learning method, SHAP, revealed that highest contribution was by meteorological variables, followed by vegetation structure, topography, and soil. Analysis of key interactions between variables highlighted the role of vegetation structure in driving ET under different rainfall and wind speed conditions. Conclusions We conclude that the spatial variability of ET in this tropical mosaic landscape can be explained by a combination of biophysical variables, with vegetation structure contributing significantly. The underlying relationships can be useful to understand and potentially steer the climate regulation function of human-modified landscapes.

This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm3/cm3 at 0–100 cm depth to 0.039 cm3/cm3 at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP.