Search Results (10)

The urban heat island (UHI) effect has become a critical environmental issue affecting urban livability and public health, attracting widespread attention from both academia and society. Although numerous studies have examined the influence of urban characteristics on land surface temperature (LST), most have been restricted to single variables or single time points, and the traditional “urban–rural dichotomy” approach fails to capture intra-urban thermal heterogeneity. To address this limitation, this study integrates the Local Climate Zone (LCZ) framework with machine learning techniques to systematically analyze the diurnal variation patterns of LST across different LCZ types in Beijing and explore the interactive effects of urban characteristic variables on LST. The results show the following: (1) Compact building zones (LCZ 1–3) exhibit significantly higher daytime LST than open building zones (LCZ 4–6), with reduced differences at night; high-rise buildings cool daytime surfaces through shading but increase nighttime LST due to heat storage. (2) Blue–green space variables, such as NDVI and tree coverage (T_PLAND), substantially lower daytime LST through evapotranspiration, but their nighttime cooling effect is weak; cropland coverage (C_PLAND) plays a particularly important role in lowering nighttime LST. (3) Blue–green space and urban form variables exhibit significant interaction effects on LST, with contrasting impacts between day and night. (4) Population activity variables are strongly correlated with increased LST, especially at night, when their warming effects are more prominent. This study reveals the relative importance and nonlinear relationships of different variables across diurnal cycles, providing a scientific basis for optimizing blue–green space configuration, improving urban morphology, regulating human activity, and formulating effective UHI mitigation strategies to support the development of more sustainable urban environments. Full article

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. Full article

The aim of this study was to analyze and compare evapotranspiration estimates obtained from different data sources over grassland regions in central-western Poland during the vegetation seasons in the years 2021 and 2022. The dataset provided includes evapotranspiration (ET) estimates derived from three sources: (1) evapotranspiration measurements from the ECOSTRESS satellite; (2) evapotranspiration estimates calculated using the energy balance method based on ERA5-Land meteorological data with land surface temperature (LST) from MODIS; and (3) evapotranspiration estimates with meteorological data derived from ground measurements replacing ERA5-Land data and using MODIS LST for the surface temperature. For the second and third sources, where the energy balance method (Penman–Monteith) was applied, the data used for the ET calculation were obtained from the nearest ground-based meteorological station to the test fields, with the most distant fields being up to 40 km away in a straight line. In addition, for comparison, the MOD16 global evapotranspiration product was added. In a study conducted in the central-western region of Poland, specifically in Wielkopolska (NUTS2–PL41), 18 grassland plots ranging in size from 0.36 to 21.34 ha were studied, providing valuable insights into the complex relationships between environmental parameters and evapotranspiration processes. The evapotranspiration derived from different sources was tested by applying correlation with soil moisture and the height of the grass obtained from ground measurements. It was found that the evapotranspiration data derived from ECOSTRESS had the best correlation with soil moisture (r = 0.46, p < 0.05) and the height of the grass (r = 0.45, p < 0.05), both of which were statistically significant. The values of the ground measurements (soil moisture and vegetation height were considered as verification for the evapotranspiration precision). In addition, the information about precipitation and air temperature during the time of measurements was considered as the verification for the evapotranspiration conditions. Comparisons between ECOSTRESS data and other sources suggest that ECOSTRESS measurements may offer the most precise estimates of evapotranspiration in the studied region. Full article

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities. Full article

Tropical forests are major sources of global terrestrial evapotranspiration (ET), but these heterogeneous landscapes pose a challenge for continuous estimates of ET, so few studies are conducted, and observation gaps persist. New spaceborne products such as ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) are promising tools for closing such observation gaps in understudied tropical areas. Using ECOSTRESS ET data across a large, protected tropical forest region (2250 km²) situated on the western slope of the Andes, we predicted ET for different days. ET was modeled using a random forest approach, following best practice workflows for spatial predictions. We used a set of topographic, meteorological, and forest structure variables from open-source products such as GEDI, PROBA-V, and ERA5, thereby avoiding any variables included in the ECOSTRESS L3 algorithm. The models indicated a high level of accuracy in the spatially explicit prediction of ET across different locations, with an r² of 0.61 to 0.74. Across all models, no single predictor was dominant, and five variables explained 60% of the models’ results, thus highlighting the complex relationships among predictor variables and their influence on ET spatial predictions in tropical mountain forests. The leaf area index, a forest structure variable, was among the three variables with the highest individual contributions to the prediction of ET on all days studied, along with the topographic variables of elevation and aspect. We conclude that ET can be predicted well with a random forest approach, which could potentially contribute to closing the observation gaps in tropical regions, and that a combination of topography and forest structure variables plays a key role in predicting ET in a forest on the western slope of the Andes. Full article

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. Full article

The coming years will see the launch of several missions (TRISHNA, LSTM, SBG), which will acquire images in four or more spectral bands in thermal infrared (TIR) at high spatial resolution (~50–60 m) and with high temporal revisit (~2–3 days). The derivation of surface temperature and emissivity values from top-of-atmosphere radiances is not straightforward, as it is a non-deterministic process requiring additional information. In this paper, we propose the algorithm DirecTES to efficiently separate surface temperature and emissivity. This algorithm is based on the use of a comprehensive spectral database of emissivity, resulting in a well-posed deterministic problem while not assuming strong hypotheses. The algorithm can also benefit from non-TIR information, such as the acquisitions from the same satellite but in the visible and near-infrared domains, or exogenous data—land/sea mask or soil-occupation map. These would help identify the nature of the surface and therefore improve the temperature and emissivity retrievals. After the complete description of the method, we evaluate the performances of DirecTES on theoretical landscapes in TRISHNA’s context under a large range of atmospheric conditions. The retrievals of surface temperature reach RMSEs of 0.8 K over vegetation and 0.5 K over water, including both sensor and atmospheric uncertainties. We then evaluate DirecTES on ECOSTRESS images on sites where the ECOSTRESS Land Surface Temperature (LST) performance has been documented; DirecTES surface temperature retrievals are consistent with the ECOSTRESS LST product and the in-situ data. Full article

For the purpose of deriving spatiotemporal estimates of soil moisture, the triangle method is one of the most widely used approaches today utilizing remote sensing data. Generally, those techniques are based on the physical relationships that exist when a satellite-derived land surface temperature (Ts) is plotted against a spectral vegetation index (VI). The present study proposes an improvement in the triangle method in retrieving soil moisture over heterogeneous areas. In particular, it proposes a new approach in robustly identifying the extreme points required for the technique’s implementation. Those extreme points are then used in calculating fractional vegetation cover (Fr) and scaled Ts. Furthermore, the study proposes a new approach for calculating the coefficients required to develop the relationships between surface soil moisture (SSM) and Fr/Ts, which is implemented using a model and field data. As a case study, an agricultural field in the Varanasi district in India has been used, on which the triangle method is implemented using ECOSTRESS and Sentinel-2 data. The much-improved spatial resolution satellite data of ~70 m from ECOSTRESS allowed deriving more vivid results of SSM spatial variability for the study area. Comparisons between field soil moisture calculated using the proposed method returned an RMSE of 0.03 and R² value of 0.84, which are considered very satisfactory. The methodology proposed herein and the results obtained are of significant value with regards to the triangle method, contributing to ongoing efforts at present examining its use for operational product development at a global scale. Full article

Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy ($R^2$ = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy. Full article

The ECOSTRESS multi-channel thermal radiometer on the Space Station has an unprecedented spatial resolution of 70 m and a return time of hours to 5 days. It resolves details of oceanographic features not detectable in imagery from MODIS or VIIRS, and has open-ocean coverage, unlike Landsat. We calibrated two years of ECOSTRESS sea surface temperature observations with L2 data from VIIRS-N20 (2019–2020) worldwide but especially focused on important upwelling systems currently undergoing climate change forcing. Unlike operational SST products from VIIRS-N20, the ECOSTRESS surface temperature algorithm does not use a regression approach to determine temperature, but solves a set of simultaneous equations based on first principles for both surface temperature and emissivity. We compared ECOSTRESS ocean temperatures to well-calibrated clear sky satellite measurements from VIIRS-N20. Data comparisons were constrained to those within 90 min of one another using co-located clear sky VIIRS and ECOSTRESS pixels. ECOSTRESS ocean temperatures have a consistent 1.01 °C negative bias relative to VIIRS-N20, although deviation in brightness temperatures within the 10.49 and 12.01 µm bands were much smaller. As an alternative, we compared the performance of NOAA, NASA, and U.S. Navy operational split-window SST regression algorithms taking into consideration the statistical limitations imposed by intrinsic SST spatial autocorrelation and applying corrections on brightness temperatures. We conclude that standard bias-correction methods using already validated and well-known algorithms can be applied to ECOSTRESS SST data, yielding highly accurate products of ultra-high spatial resolution for studies of biological and physical oceanography in a time when these are needed to properly evaluate regional and even local impacts of climate change. Full article