Satellite-derived land surface phenology (LSP) metrics are widely used to monitor vegetation phenology at large scales. However, current LSP products have uncertainties from limited satellite revisit cycles and adverse weather. This study integrated satellite and near-surface camera data to construct a satellite-camera system. Compared to traditional satellite products, near-surface camera-calibrated products more accurately determine vegetation phenological phases. Our analysis shows satellite-camera fused vegetation index time series strongly agree with satellite data while retaining near-surface cameras' high temporal resolution. The multi-cycle double logistic model (MDLM) was proposed to overcome conventional models' limitations in capturing only single-peak patterns, effectively characterizing multi-peak dynamics. We corrected the 2022 and 2023 vegetation phenological metrics (PMs) across the contiguous United States through interpolation, using high-quality ground reference benchmarks for validation. The corrected satellite-camera phenological data demonstrate improved performance compared to satellite-derived products. Analysis of different data sources reveals general consistency in identifying PMs, with higher accuracy for growth than senescence stages. Studies on three representative land types (grassland, forest, and cultivated land) also demonstrate that integrating satellite imagery with near-surface camera data enhances LSP monitoring and improves the temporal accuracy of vegetation phenology, which is crucial for optimizing agricultural management and advancing ecological and environmental research.

ABSTRACT Accurately identifying extreme land surface temperature (LST) zones—heat cores (HCs, areas with persistent high temperatures) and cold cores (CCs, areas with persistent low temperatures)—is crucial for urban climate resilience. Traditional approaches fail to capture subtle LST variations and hierarchical structures. This study introduces the Local Contour Tree Analysis (LCTA), a graph theory–based method that captures topological hierarchies and dynamic interactions, surpassing conventional fixed-threshold techniques. Applied to western Shenzhen, China, it revealed HCs' northward interannual migration from southern clustering through dispersion to northern clustering, while CCs remained stable as primary cooling sources. Seasonally, HCs exhibited stronger clustering in summer and greater dispersion in winter; CCs maintained structural layering year-round with reduced heterogeneity in winter. Structurally, HCs were simple and compact, while CCs were complex, multi-layered, and interconnected. Urban thermal deterioration concentrated in stable HCs due to impervious surface expansion and vegetation loss, while improvements occurred around CCs through vegetation recovery. These shifts reflected urbanization trends and revealed scale-specific characteristics tied to development stages. Findings elucidate HC and CC mechanisms, informing green-blue infrastructure planning via targeted vegetation corridors to enhance cooling connectivity and counteract HC expansion, offering actionable heat mitigation strategies for rapidly developing urban regions.

This study investigates the sensitivity of satellite-based brightness temperature measurements at 3.74, 10.85, and 12.00 μ m with respect to the identification of water, ice, and mixed-phase clouds (MPC). Radiative transfer simulations computed by SCIATRAN reveal that the directional brightness temperature difference at 3.74 μ m ( Δ BT 3 . 74 ), which is dependent on scattering, enables water clouds and MPC separation from ice clouds. For water clouds and MPC, Δ BT 3 . 74 typically exceeds 2 K, whereas for ice clouds it remains below 2 K. To separate MPC from water clouds, we introduce the Liquid Cloud Probability Index (LCPI) based on cloud top temperature and absorption differences between water and ice at 10.85 and 12.00 μ m. LCPI values generally exceed 0.4 for water clouds but fall below 0.4 for many MPC cases. The Δ BT 3 . 74 and LCPI approach is validated using the Sea and Land Surface Temperature Radiometer (SLSTR) dual-view data collocated with the 2B-CLDCLASS-LIDAR cloud phase product, showing over 90% accuracy in water and ice phase classification, and approximately 60% for MPC. This dual-view, multi-channel method enhances the detection of cloud phases, offering improved results for brightness temperature measurements. • Directional brightness temperature difference at 3 . 74 μ m can be used to separate water and ice clouds. When used with brightness temperature differences at 10 . 85 μ m and 12 . 00 μ m , mixed-phase clouds can be identified. • Mixed-phase clouds occurring at lower cloud top heights are challenging to separate from water clouds.

Urban heat exposure patterns and domain-specific executive function in adolescents.

Accurate reconstruction of fine-resolution, spatially temporally continuous land surface albedo is critical for energy balance studies and climate change modeling. This paper proposes a dual-constrained optimal image matching strategy (DC-OPS) for reconstructing seamless 16 m daily albedo by integrating 500 m MODIS and 16 m GF-1 WFV products through the flexible spatiotemporal data fusion (FSDAF) model. The strategy combines temporal proximity and texture similarity to identify optimal reference image pairs, enhancing reconstruction accuracy. Validated in the Huailai region and the Tibetan Plateau (TP), the results show high accuracy. In Huailai, DC-OPS yielded an RMSE of 0.0304 against original GF-1 images and 0.0282 against in situ measurements. In the TP region, the method maintained a reliable RMSE of 0.0377 despite extreme data scarcity. Comparative analysis demonstrated that DC-OPS outperforms traditional methods such as harmonic analysis of time series (HANTS) and neighborhood similar pixel interpolator (NSPI), especially in capturing rapid surface changes such as snowfall. The strategy exhibits robust applicability across diverse landscapes, providing a foundation for large-scale applications by enhancing spatial temporal continuity.

PGDM: Physically guided diffusion model for land surface temperature downscaling

Purpose As climate warming and urban heat exposure continue to intensify, accurately identifying the cooling effect of vegetation is essential for developing effective urban climate adaptation strategies. However, the relationship between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) is highly sensitive to spatial scale. Inappropriate scale selection may lead to misjudgments of urban cooling potential due to the Modifiable Areal Unit Problem (MAUP). This study aims to reveal the scale dependency of the NDVI–LST relationship and assess its implications for urban green space planning and climate adaptation decision-making. Design/methodology/approach Taking Shanghai as a case study, this research constructs multi-scale grids while maintaining consistent spatial resolution of remote sensing data. Land use change information from 2000 to 2024 is integrated into the analysis. By combining the Random Forest model with the SHAP interpretation method, this study examines the statistical relationship, nonlinear response structure and variations in cooling turning points of NDVI–LST across different scales and land use contexts. This approach enables a systematic assessment of how spatial aggregation methods influence the identification of vegetation cooling effects. Findings The results show that as the analysis scale shifts from coarse to fine, local heterogeneity and contextual effects are significantly enhanced. The nonlinear response structure of NDVI–LST and its cooling thresholds undergo systematic changes with scale. At coarse scales, local fluctuations are smoothed out and the overall response becomes more stable. At fine scales, nonlinear characteristics become more pronounced and spatial differences more prominent. From the MAUP perspective, scale selection not only affects the assessment of statistical correlations but may also introduce biases in evaluating the cooling efficiency of green spaces within climate adaptation planning. Originality/value This study proposes a reusable multi-scale assessment framework from the MAUP perspective. It reveals the scale sensitivity of the NDVI–LST relationship and its contextual dependency across different land use backgrounds. The findings highlight the critical value of multi-scale information in urban green infrastructure planning and heat risk management. This provides methodological innovation and practical references for developing evidence-based urban climate adaptation strategies.

Following the Viking experiments in 1976, many of the original inferences of biological metabolism have been replicated via abiotic mechanisms we now know are plausible on Mars' surface. While in many cases subsequent experiments have cast doubt on whether Viking truly detected life, numerous other studies since Viking have greatly expanded our knowledge of life's limits and microbial metabolism. In particular, increased characterization of Earth's subsurface has revealed the astounding complexity and adaptability of life, highlighting chemically based metabolisms as potentially strong targets for future life detection missions. Over the same time frame, we have gained knowledge of putatively more habitable regions in Mars' subsurface, relative to the original Viking lander surface sites, that could host similar organisms. In this review, we discuss the wealth of knowledge concerning the habitability of zones across Mars' surface/subsurface, and we suggest specific microbial metabolisms that should be targeted in future life detection missions based on laboratory and field studies under analogous conditions on Earth and with consideration of recommendations from the larger Astrobiology community. The ability to leverage these advancements in subsurface research toward the incorporation of increased specificity in future life detection efforts is additionally discussed in the context of current Mars subsurface mission progress and planetary protection and defense concerns.

Methods such as multi-factor geographically weighted machine learning (MFGWML), geographically neural network weighted regression (GNNWR), and its enhanced version—geographically convolutional neural network weighted regression (GCNNWR)—have improved spatial non-stationarity modeling. However, they are over-reliant on spatial proximity weighting and underutilize spatial neighborhood information. Therefore, this study proposes a novel Geographically Conditioned Multiscale Convolutional Neural Network (GCMCNN) that downscales MODIS land surface temperature (LST) data to 100 m resolution by integrating two- and three-dimensional surface features. GCMCNN enhances the capture of spatial heterogeneity through a geographically aware (geo-aware) mechanism conditioned by location and attributes, while improving the characterization of spatial correlation by developing a multiscale convolutional neural network (CNN). GCMCNN model outperformed four benchmark models (random forest, MFGWML, GNNWR, and GCNNWR) in LST downscaling, reducing RMSE by 15.4–26.16% and MAE by 15.05–23.59%, and increasing R2 by 39.81–148.09%. GCMCNN improved the model performance of CNN through its geo-aware mechanism and multi-level spatial feature extraction and fusion, reducing RMSE and MAE by 6.6% and 8%, respectively, and increasing R2 by 7.5%. Furthermore, spatial block cross-validation better reflects the model’s true generalization ability than random cross-validation. This study highlights the importance of accurately modeling spatial heterogeneity and correlation in LST downscaling.

In arid and semi-arid countries, accurate agricultural drought characterization is crucial for climate-resilient planning and efficient early warning systems. Therefore, by integrating remote sensing data, CMIP6 projections, and machine learning (ML) models, the study assesses the spatio-temporal dynamics, driving factors, and future evolution of agricultural drought in the Borena Zone, southern Ethiopia. The Vegetation Health Index (VHI) was primarily targeted using several drought-related indicators, such as the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land surface temperature (LST), and Temperature Condition Index (TCI) to detect the drought. Additionally, Shapley Additive Explanations (SHAP) and ML models were utilized to differentiate drought dynamics and performance for two seasons: March-May (MAM) and August-November (ASON). The findings show that the observed distribution of rainfall during both seasons spans from less than 20mm month⁻¹ in the southern lowlands throughout a large portion of the southern basin, and that LST variation increases to 35.1°C during MAM and rises to 38.1°C during ASON, making it greatly susceptible to evapotranspiration. Consequently, the southern zone's root-zone soil moisture decreases from 0.30 to 0.35m³ m⁻³ in MAM to 0.25-0.30m³ m⁻³ in ASON, along with notable decreases in NDVI and MNDWI. The ML models show strong drought prediction performance when targeting VHI; the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) and Cohen's kappa of XGBoost achieve 0.997, and 0.72 during MAM, and Random Forest achieves approximately 0.988 and 0.78 during ASON, respectively. TCI and NDVI are the most important predictors of drought severity according to SHAP-based feature attribution. Integrating future drought forecasts from a bias-corrected CMIP6 model (CNRM-CM6-1) under SSP2-4.5 and SSP5-8 scenarios indicates a significant expansion and intensification of drought. Mild drought is projected to affect up to 36.2% of the zone under SSP2-4.5, whereas under SSP5-8.5, moderate, severe, and extreme drought are projected to increase by 18.87%, 5.03%, and 3%, respectively, by the late 21st century. These robust results highlight escalating risks to rain-fed agriculture, rangeland productivity, and water resources, underscoring the urgency of machine-learning-based drought early-warning systems and targeted adaptation strategies in the Borena Zone.

It is generally believed that CO2 physiological forcing can partially mitigate land surface drying under global warming by reducing stomatal conductance and evapotranspiration. Most of this type of study focuses on the direct regulation by vegetation physiology, overlooking interactive feedback from the atmosphere. Using fully coupled earth system model simulations, we find that the physiological benefit may have been optimistically overestimated. Vegetation-induced energy change may in turn further affect atmospheric vapor pressure deficit (VPD), exerting extra evapotranspiration demand indirectly. Indirect VPD feedback over northern mid-high latitudes could offset 54% (±26%) of evapotranspiration reduction driven by stomatal closure under current CO2 condition, and that proportion increases to 68% (±18%) at 4 × CO2. The enhanced VPD feedback is largely driven by vegetation-mediated albedo decline and temperature rise in northern mid-high latitudes, which intensifies evapotranspiration loss as stomatal constraints are minimal. These are important findings, substantially limiting the physiological benefits of CO2 with extra pressure on surface aridification and water resources.

Evaluating groundwater quality influences from oil field operations and other anthropogenic activities in an urban setting, Santa Fe Springs, California.

A Bayesian regression modeling framework for identifying land surface temperature and evapotranspiration treatment effects in UAV and airborne data

Microrefugia are small areas that allow species to persist in changing environments. Dolines in karst landscapes may function as such safe havens. By analysing vegetation data from 270 plots collected within nine large dolines and on the surrounding plateau in Northern Hungary, we found that topographic complexity in dolines has the potential to support a wide array of plant species with diverse biogeographic affinities. To safeguard the large number of endangered plant species and the high biodiversity associated with dolines, conservation management should take into account the complex interactions among topographic complexity, environmental conditions, and species distributions in karstic microrefugia and their surrounding areas.

ABSTRACT Accurate evapotranspiration (ET) estimation in an energy balance model, such as SEBAL, depends on the selection of hot and cold anchor pixels. This selection is highly subjective and varies among modellers, leading to substantial uncertainty and frequent inconsistencies in ET outputs. Manual identification is time-consuming and often problematic, with incorrect anchor-pixel choices producing unrealistic energy partitioning and even negative ET estimates. The sensitivity analysis in this study revealed that hot anchor pixels with identical land surface temperature (LST) but at different spatial locations can yield significantly different, and sometimes negative, ET estimates, underscoring the need for an optimal, physically representative anchor-pixel pair. Therefore, this study introduces an automated optimization framework using a genetic algorithm that selects an anchor pixel pair from the candidate anchor pixel pool, defined based on the NDVI-LST feature space, to maximize the evaporative fraction over agricultural areas while maintaining energy balance closure. The approach eliminates subjectivity in anchor pixel selection and ensures consistent energy-balance computation. The model performance was evaluated using 10-day cumulative ET estimates and the water balance model, ORYZA2000, which was parameterized with detailed field-level crop, water, soil and meteorological information. Optimized SEBAL estimates showed strong agreement with independent ET estimates from field-parameterized water balance model, with R2 values between 0.89 and 0.94 and RMSE values of 5.21–8.56 mm. In contrast, ET estimates using manual anchor pixel selection exhibited lower performance with R2 values between 0.77 and 0.83 and RMSE values between 7.69 and 10.52 mm. Moreover, the negative ET values obtained during the manual approach were automatically excluded while executing optimized SEBAL. The proposed method is automated and ensures consistent ET estimates, thereby greatly improving the operational reliability and applicability of the SEBAL model across a large spatial domain.

ABSTRACT Tropical forests are crucial ecological resources but are face severe threats from human activities. Assessing forest cover is essential for monitoring and for the development of effective sustainable forest management approaches. This study analyses the changes in forest canopy density and land surface temperature (LST) in Kolasib district of Mizoram, India, over a period of three decades (1995–2025), utilizing Landsat satellite imagery via Google Earth Engine (GEE). The Forest Canopy Density (FCD) model was computed by integrating the Normalized Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI), Barren Index (BI), and Shadow Index (SI). A Mann-Kendall trend test and Pearson correlation were also applied to the long-term data. The results revealed a 1.83% increase in non-forest areas and a 0.21% decrease in dense forest, primarily driven by infrastructure and shifting cultivation. Conversely, moderate forest increased by 2.41% due to vegetation recovery and land‑use policies. The Mann-Kendall trend analysis shows that LST is increasing by 0.042°C annually, accumulating in a 1.26°C increase over the three decades. Although NDVI has improved over recent years, the thermal footprint remains elevated, proving that forest conversion permanently alters the regional microclimate. These findings emphasize the necessity of sustainable land-use planning for continued forest management and conservation.

Abstract Urban heat islands (UHIs) pose significant environmental challenges in rapidly urbanising regions, highlighting the need for high-quality spatial datasets to better characterise urban thermal patterns. The Local Climate Zones (LCZ) approach provides a standardised land-use framework for UHI research and is known to strongly correlate with Land Surface Temperature (LST). In this study, 27 LCZ maps for five Southeast Asian cities were generated using cloud-free Landsat 8/9 scenes. Corresponding LST maps were retrieved to construct a paired dataset. Spearman’s rank correlation analysis confirmed a similar relationship between LCZ classes and LST existed in all pairs, shown by Spearman’s r ranging from -0.675 to -0.874, indicating a strong negative direction. When evaluated using the same testing polygons, our LCZ maps consistently outperformed the existing global LCZ dataset. A fully convolutional network was trained using the dataset to perform pixel-wise LST regression using LCZ maps, achieving an RMSE of 1.371℃ and an R 2 of 0.891 under random-split setting and RMSE values ranging from 1.212 °C to 1.623 °C across individual city validations. The results highlight the substantial predictive performance of LCZ information for spatial LST estimation and demonstrate the suitability of the dataset for UHI machine-learning applications. The dataset is publicly available at https://doi.org/10.5281/zenodo.18223368 . Graphical Abstract Based on the graphical abstract snapshot, this study utilises Landsat 8 and 9 to develop a paired Local Climate Zones (LCZ) and Land Surface Temperature (LST) for five major Southeast Asian Cities. For each city, all Landsat 8/9 scenes from 2013 to 2025 were retrieved and clipped to the city extent and checked for cloud cover, if the cloud cover is less than 10%, the scene is selected for LCZ mapping through a Convolutional Neural Network (CNN) using our carefully-selected LCZ polygons, if the validation overall accuracy of the mapped LCZ scene is greater than or equal to 70%, the corresponding LST scene is calculated and clipped to form an LCZ and LST pair. Spearman’s rank correlation analysis resulted in an average Spearman’s rank of -0.783 across all generated pairs. The formed SEA-LCZ-LST dataset was tested for pixel-wise LST regression from LCZ using a Fully Convolutional Network (FCN) and achieved a RMSE of 1.371℃ and R2 of 0.891 under random-split setting. These findings demonstrate the dataset’s suitability for machine-learning-based UHI applications in the Southeast Asian region.

Deyang City, situated in a typical mountain-plain transition zone of Southwest China, faces high ecological fragility due to its steep topographic gradient, frequent seismic disturbances (e.g., the 2008 Wenchuan earthquake), and intensive anthropogenic pressures from rapid urbanization and agricultural production. Taking this representative area as a case study, this study utilized multi-source remote sensing data from 2000, 2005, 2010, 2015, and 2020. A comprehensive Land Degradation Index (LDI) was constructed by integrating NDVI, Surface Albedo, Salinity Index, and Land Surface Temperature. Using transition matrix, Theil-Sen median trend analysis with Mann-Kendall tests, Geodetector models, and Coefficient of Variation (CV), we systematically diagnosed the spatio-temporal evolution, trends, stability, and driving factors of land degradation. The results show that: (1) From 2000 to 2020, the LDI exhibited a spatial pattern of “higher in the northwest, high and dispersed in the center, and low in the southeast.” The combined proportion of “Low” and “Slight” degradation rose from 43.33% to 62.86%, though a distinct rebound occurred in 2010 due to the Wenchuan earthquake. (2) Transition matrix analysis revealed a positive evolution, with the L1 grade increasing by 18.01%, reflecting long-term ecological recovery. (3) Trend analysis indicated a regional LDI decline (β = −0.0173/5years), with improvement areas (31.16%) exceeding degradation areas (12.13%). (4) Geodetector results identified temperature and precipitation as foundational drivers, while per capita GDP and population density gained explanatory power during rapid economic expansion. Universal bivariate and nonlinear enhancements suggest land degradation results from complex multi-factor coupling. (5) CV analysis showed high stability (90.75% of area with CV < 0.1), but the northwestern mountain-plain transition zone exhibited elevated ecological vulnerability due to seismic disturbance and human activities. This study provides an analytical framework for diagnosing land degradation in mountain-plain composite regions, offering a scientific basis for ecological restoration and sustainable land management policies.

Sustainable agriculture in tropical regions relies on precise understanding of soil water dynamics under varying topographic, textural, and climatic conditions. This study integrates Electrical Resistivity Tomography (ERT) and Electromagnetic Induction (EMI) to characterize soil moisture distribution and subsurface textural heterogeneity across three contrasting agricultural landscapes in West Java, Indonesia—Subang (coastal lowlands), Bandung (uplands), and Sumedang (terraced highlands). ERT provided high-resolution vertical profiles to 5 m depth, revealing resistivity ranges that correspond to lithological and hydrological properties. In Subang, low resistivity (1.7–30 Ω·m) showed high-salinity clay loam with a shallow water table. Conversely, high resistivity in Bandung (70–300 Ω·m) reflected well-drained sandy layers with limited retention. Intermediate values in Sumedang suggested deep moisture storage within clay-rich layers beneath drier topsoil, influenced by terrace morphology. EMI mapping complemented ERT by capturing lateral resistivity variations at fixed depths, offering spatial continuity across the surveyed areas. The combined approach revealed that slope gradient, soil texture, and drainage conditions jointly govern water retention and availability. The integration of ERT and EMI provides complementary information on vertical and lateral variability of soil moisture distribution. Field observations and soil profile analysis confirm the reliability of the geophysical interpretation. These findings demonstrate that integrated geophysical imaging provides an effective non-invasive tool for mapping soil moisture variability and supporting precision agriculture strategies in tropical agricultural environments.

Urban heat intensification threatens environmental quality and human health, particularly in arid urban areas where cooling resources are scarce. While impervious surface area is a well-established driver of urban land surface temperature (LST), the influence of built-up spatial patterns beyond their total area remains poorly understood. This study aimed to disentangle the effect of built-up pattern after accounting for built-up area on LST using a dual-scale analysis in an arid urban-barren mosaic. The spatial pattern of Sentinel-2 10m-derived built-up areas, measured as Area, Mean Patch Size (MPS), Shape Index (SHI), Nearest Neighbor Distance (ENN), and Number of Patches (NP), was extracted within each 100m Landsat thermal pixel, creating a dataset of 8,130 observations. Landsat-derived summer LST served as the dependent variable in a set of flexible regression models designed to evaluate both the total extent of built-up land and the spatial structure of urban patches. Results showed that adding configuration metrics to Area-only models improves LST prediction accuracy (R² = 0.589 to 0.693) and reduces RMSE (2.792°C to 2.409°C). Additional models examined how different spatial metrics interact, allowing the influence of built-up patterns to be evaluated independently of total built-up area. Unsupervised clustering (average silhouette width = 0.295) revealed that, when controlling for area, Compact configurations, low NP with high MPS, SHI, and ENN, were on average 2.7°C warmer than Fragmented or Balanced types. These findings demonstrate that, after adjusting for built-up extent, built-up pattern exerts a measurable and spatially distinct influence on LST, underscoring its importance for climate-responsive urban design in arid regions.