Search Results (777)

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

High-resolution estimation of global near-surface air temperature (Ta) is essential for investigating microclimates, ecosystem processes, and agricultural suitability. However, sparse in situ observations do not capture local heterogeneity, whereas existing datasets lack fine-scale detail because of their coarse spatial resolution. To address this limitation, we developed an ensemble machine-learning framework using Landsat 8/9 data. Predictions from LightGBM, XGBoost, and CatBoost were combined through Bayesian model averaging (BMA), which assigns probabilistic weights to individual models to improve robustness. The models were trained using a globally distributed spatiotemporal matchup dataset that paired HadISD in situ Ta observations with MODIS/VIIRS products to support subsequent Landsat-based application. Key inputs included land surface temperature (LST), vegetation indices, elevation, solar zenith angle, and spatiotemporal features. The BMA ensemble achieved strong validation performance, with an RMSE of ~3 K, near-zero bias, and an R² of 0.92. Feature-importance analysis identified LST as the dominant predictor, underscoring the primary role of surface thermal state in estimating Ta. The proposed method can generate robust global Ta fields at 90 m resolution, revealing fine-scale thermal patterns that have previously been difficult to resolve at the global scale. Unlike many regional models calibrated for single study area or dependent on dynamic external auxiliary fields, our Landsat-predominant application framework supports operational mapping of clear-sky and overpass-time Ta. Such detailed instantaneous data can advance climate research, improve assessments of ecological responses and climate impacts, and support applications such as urban heat island monitoring and precision agriculture. Full article

In this work, we discuss the impact of polarized scene radiances on the intercalibration of CPF and VIIRS reflective solar bands and the mitigation of these effects using empirical Polarization Distribution Models (ePDMs). The ePDMs, derived from multidirectional polarized reflectance measurements taken by the POLDER instrument, can provide the polarization state of the reflected solar radiation in terms of the Degree and Angle of Polarization, DOP and AOP, for each spatially, temporally, and angularly matched intercalibration footprint between CPF and VIIRS. The CPF science team will leverage these ePDMs to identify scenes with low polarization to reduce intercalibration uncertainties for specific VIIRS channels that are polarization-sensitive. The study also demonstrates that, in the absence of ePDM-based filtering of intercalibration samples, polarization-induced biases in VIIRS reflectance measurements for shortwave bands (e.g., M3 0.49 μm) can be as high as 2.4% for clear-sky over ocean scenes. Full article

Accurate and timely wildfire hotspot detection is essential for ecological sustainability and supporting climate resilience strategies. Although sensors such as MODIS and VIIRS have been widely used for wildfire detection, the potential of ocean color satellites for terrestrial wildfire monitoring remains largely unexplored. In this study, a Spectral–Spatial Attention U-Net (SSA-UNet) framework is proposed for wildfire hotspot detection using multispectral observations from the HY-1E Coastal Zone Color Scanner II (COCTS2) over the Three-North region of China. The proposed framework integrates spectral attention to enhance fire-sensitive bands and spatial attention to capture contextual wildfire patterns under complex environmental conditions. Experimental results show that SSA-UNet achieves a Precision of 0.8913, Recall of 0.7961, and F1-score of 0.8680, outperforming conventional threshold-based approaches and baseline deep learning models. Ablation experiments further demonstrate the effectiveness of the spectral–spatial attention mechanism, while band analysis highlights the important contributions of near-infrared, shortwave infrared, and thermal infrared observations for wildfire hotspot detection. The real wildfire case analysis further confirms the practical applicability of the proposed framework. The results demonstrate that HY-1E COCTS2 data have considerable potential for large-scale terrestrial wildfire monitoring when combined with deep learning techniques. Full article

Rapid urbanization and associated greenhouse gas emissions pose severe challenges to global climate goals. Accurately estimating urban carbon emissions at fine administrative scales is a critical prerequisite for spatially differentiated mitigation policies and achieving carbon neutrality. However, while current research has validated the feasibility of using nighttime light (NTL) remote sensing for carbon estimation, most studies predominantly focus on macro scales, paying limited attention to intra-urban spatial heterogeneity and the value of high-resolution imagery. Using Nanjing, China, as a case study, this study examines the optimal scale, model, and data source for estimating urban total carbon emissions. NTL features from NPP/VIIRS and Luojia1-01 imagery were extracted at the district and township levels. Spatial lag and spatial error models were compared, and geographically weighted regression was further applied at the township level. The results show that urban carbon emissions in Nanjing exhibit clear scale effects and spatial non-stationarity. At the township level, the total indicator (TCE-TNLI) better reflects emission expansion in peripheral areas, while the intensity indicator (CI-ANLI) shows better predictive performance and robustness. With high-resolution Luojia1-01 imagery, the intensity model further reduces the effects of pixel saturation and administrative scale differences, achieving better model performance. These findings establish a robust methodological framework for fine-scale urban carbon accounting, demonstrating that integrating high-resolution imagery with intensity-based models is crucial for supporting spatially differentiated low-carbon planning in high-density megacities. Full article

A comprehensive understanding of the spatial dynamics and operational characteristics of fishing activities in the Arabian Sea is critical for effective marine management and regional resource conservation. Based on VIIRS/DNB nighttime light imagery from 2017 to 2022 and the YOLOv11 model, this study presents an applied observational pipeline for the spatial extraction of fishing vessel positions. Spatial statistical methods were employed to analyze the operational patterns of light-fishing fleets, and habitat niches were identified by integrating marine environmental data. The results indicate that: (1) The YOLOv11 model achieved a precision (P) of 0.966, a recall (R) of 0.954, and a mean average precision (mAP) of 0.969. Under clear-sky and thin-cloud conditions, it demonstrated superior detection accuracy compared to existing VBD (VIIRS Boat Detection) products. (2) Through Kernel Density Hotspot Analysis (KDHSA), the primary spatial distribution of the light-fishing fleet was delineated. Fishing Operation Areas (FOAs) exhibited a pronounced seasonal “clustering–diffusion–re-clustering” pattern. The Center of Effort (CoE) generally followed a counter-clockwise migration trajectory, though a clockwise shift was observed during the 2019–2020 fishing season. (3) Random Forest analysis identified dissolved oxygen at 200 m (DO200), sea surface height (SSH), and temperature at 200 m (T200) as the primary predictive environmental features associated with vessel distribution. The core spatial ranges associated with high vessel density were 9.5–14.9 mmol⋅m⁻³ for DO200, 0.24–0.36 m for SSH, and 17.3–18.0 °C for T200. Notably, the statistical contribution of subsurface factors significantly exceeded that of sea surface temperature (SST). Future research should integrate ship position data with fishery biological data to further explore the drivers of FOA variations. This study provides a scientific basis for the sustainable management and rational development of marine resources in the Northwest Indian Ocean. Full article

Forest fires are an increasing environmental challenge in Southern Europe, requiring reliable tools for assessing both fire-induced disturbances and subsequent ecosystem recovery. This study presents an integrated satellite-based system for automated monitoring of post-fire forest dynamics. The system combines multispectral data from Sentinel-2 and Landsat (TM, ETM+, OLI, OLI-2) with thermal anomaly information from MODIS and VIIRS within a unified processing framework. It is structured into two modules: Post-Fire Disturbance (PFDMO) and Post-Fire Recovery (PFRMO). The methodology builds on a validated algorithm integrating the Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA), enabling automated multi-temporal analysis from fire detection to recovery assessment. The system was applied to three wildfire-affected areas in Bulgaria under different environmental conditions. Results reveal substantial spatial variability in disturbance and recovery, with PFDMO values ranging from −5.17 to +10.16 and PFRMO values from −2.25 to +7.40. The results demonstrate the applicability of the proposed system for monitoring post-fire forest dynamics and illustrate its potential to support informed decision-making in forest management, biodiversity conservation, and sustainable resource use. The main contribution of the system lies in the integration of disturbance and recovery assessment within a single automated and scalable workflow based on freely available satellite data. Full article

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire products are widely used for global fire monitoring, but single-sensor records are limited by differences in observation geometry, spatial resolution, detection sensitivity, and swath coverage. To combine the long-term continuity of Aqua MODIS with the higher sensitivity of Suomi NPP VIIRS, this study developed a correction-before-fusion framework for MYD14 and VNP14IMG and generated a daily fused fire radiative power (FRP) dataset at the native MODIS footprint scale. MYD14 and VNP14IMG observations from 2012 to 2024 were processed using duplicate-detection correction, footprint-scale near-synchronous matching, area-based VIIRS cloud correction, and anomalous-sample screening. Cloud-corrected VIIRS FRP was then used as the reference to develop an empirical viewing zenith angle (VZA)-dependent correction model for MODIS FRP. Finally, VZA-corrected MODIS FRP and cloud-corrected VIIRS FRP were integrated using a quality-prioritized fusion strategy. The correction model achieved high fitting accuracy ($R^2\ge 98.18\%$) and reduced MODIS underestimation under large-VZA conditions. Compared with the original MODIS product, the fused product increased detected fire pixels by approximately 3.82-fold, improved spatial continuity, and reduced temporal data gaps. Landsat-based validation showed improved low-intensity fire detection while maintaining low commission error. This framework provides a harmonized long-term FRP dataset for fire monitoring, emission estimation, and fire-climate studies. Full article

This study examines the spatiotemporal evolution and driving mechanisms of urban expansion in the Guangxi Zhuang Autonomous Region, China, from 2013 to 2023. Using Suomi-NPP VIIRS nighttime light (NTL) data, we combine Standard Deviational Ellipse (SDE) analysis, centroid migration, kernel density estimation (KDE), landscape metrics, Local Moran’s I (LISA), and system Generalised Method of Moments (system-GMM) estimation. The results show that the centroid of urban development remained within Binyang County while moving overall toward the southeast with recurrent north–south oscillations. The SDE results indicate a stable northeast–southwest orientation, with secondary expansion in other directions. The urban structure is dominated by a strong Nanning core, accompanied by secondary clusters in Liuzhou, Guilin, and other prefecture-level cities. Nanning recorded the largest absolute expansion, followed by secondary centres, including Liuzhou, Guilin, Yulin, Wuzhou, Fangchenggang, Qinzhou, and Beihai, whereas western and northern Guangxi expanded more slowly. The system-GMM results indicate that financial deepening has a marginally significant positive effect on built-up area expansion and fiscal pressure has a marginally significant constraining effect, both at the 10% level; land finance dependency does not emerge as an independent driver in this small panel. We interpret these findings through a Source–Channel–Valve framework, in which financial deepening provides the capital source, land finance represents a hypothesised institutional channel, and fiscal pressure acts as a regulatory constraint. The study provides empirical evidence for sustainable and regionally coordinated urban development in Guangxi and comparable geographically constrained regions. Full article

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R²) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research. Full article

Agricultural crop residue burning is a major driver of seasonal PM2.5 pollution and greenhouse gas (GHG) emissions in Northern Thailand. This study quantified GHG emissions from the open burning of rice, maize, and sugarcane residues across six provinces (Chiang Mai, Mae Hong Son, Lampang, Uttaradit, Nakhon Sawan, and Kamphaeng Phet) from 2019 to 2024 using the 2006 IPCC emission methodology. Spatiotemporal patterns of fire hotspots were characterized using MODIS and VIIRS satellite data, combined with kernel density estimation (KDE) and land-use classification in ArcGIS Pro. Total non-CO₂ GHG emissions (CH₄ and N₂O, expressed as CO₂-eq using GWP100 from IPCC AR5) over the six years totaled 2,599,551 tCO₂-eq, with major rice contributing the largest share (35%), followed by sugarcane (24%), second rice (21%), and maize (20%). Nakhon Sawan was the leading emitter (41%), reflecting its extensive rice and sugarcane cultivation. Pearson correlation analysis revealed consistently positive relationships between daily fire hotspot counts and PM2.5 concentrations (r = 0.30–0.84), with the strongest correlations observed in Mae Hong Son, where basin topography traps pollutants. Time-series analysis confirmed pronounced seasonal PM2.5 peaks that exceeded Thailand’s 24-h NAAQS limit (37.5 μg/m³) by 7–9 times in severe years. Biochar production via pyrolysis was evaluated as a zero-burning alternative, with an estimated annual carbon sequestration potential of 2.3–3.5 million tCO₂-eq, substantially exceeding emissions from open burning. These findings indicate that crop-residue valorization options—including biochar production, composting, and biochar co-compost—could theoretically offset agricultural GHG emissions and reduce field-burning PM2.5 emissions in Northern Thailand. However, the realized mitigation will depend on (i) verification of biochar long-term stability in tropical Thai soils through dedicated in situ trials, (ii) economic incentives that offset biochar production costs of approximately 1500–3500 THB per tonne, and (iii) integration within a policy mix that combines burning bans, mechanization support, and farmer extension services. Without these enabling conditions, biochar should be regarded as a future-perspective option rather than an immediately deployable solution. Full article

Because of high visible and near-infrared (VNIR) reflectance, and deep shortwave infrared (SWIR) absorption, snow and ice are unique among terrestrial land cover. As such, both are well-suited to mapping and monitoring using optical remote sensing. However, to date, almost all studies of snow and ice spectroscopy have been limited to single or small numbers of specific cryospheric environments. These studies serve a diversity of objectives, but together also suggest the importance of the global continuum of snow and ice composition and spectroscopy. The continuum of snow and ice composition gives rise to the characteristics that allow different types of snow and ice to be distinguished optically. Particularly with imaging spectrometers. Characterization of this continuum of reflectance can facilitate development of physical models to quantify snow and ice composition and abundance, particularly in the presence of other types of land cover. In this study, a collection of ~140,000,000 visible through SWIR (VSWIR) reflectance spectra, collected by NASA’s EMIT imaging spectrometer from 56 diverse cryospheric environments, is used to characterize the continuum of snow and ice reflectance. This continuum is characterized using linear dimensionality reduction to quantify the dimensionality and topology of the spectral feature space of snow and ice. The resulting spectral feature space is effectively two-dimensional with a planar spectral feature continuum bounded by dry and wet snow, ice and dark targets (e.g., shadow, water). Because of the near collinearity of snow and ice endmember reflectances, linear spectral mixture models based only on these endmembers are ill-posed and unstable to inversion. However, in landscapes where sufficiently homogeneous seasonal snow is present with other land cover types, the standardized spectroscopic mixture model based on the Substrate, Vegetation and Dark (SVD) continuum can be extended with an instance-specific snow endmember (SVD + snow) to yield plausible areal fraction estimates with small misfits to observed spectra. More generally, the snow–ice-dark continuum can also be represented accurately with an optimal normalized difference index exploiting compositionally distinct differential absorptions at ~650 and ~1230 nm to distinguish dry from wet snow from white and blue ice. This optimized index, referred to as the Continuous Cryosphere Index (CCI), minimizes BRDF effects of topographic slope and aspect relative to illumination, while avoiding the saturation that causes the Normalized Difference Snow Index (NDSI) to conflate wet snow with white and blue ice reflectance. In addition to imaging spectrometers like EMIT, operational sensors like MODIS, VIIRS and WorldView-3 have spectral bands near 650 nm and 1230 nm, so they could also be used for CCI mapping. Full article

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts. Full article

Despite the well-known strong influence of spatial resolution on the quality of burned area mapping and the need for timely environmental information, global wildfire monitoring services are commonly based on coarse spatial resolution (300–500 m) reflectance imagery and deliver products months or years after the present date. The paper presents, for the first time, an algorithm that provides highly accurate near-real-time medium spatial resolution burned area, from 20 m Sentinel-2 imagery. The paper exploits a pioneering sensor-independent potential of a mapping method, based on land surface reflectance modelling and machine learning, originally optimised for Sentinel-3 imagery. The mapping method uses predictions of time series of burned area from a neural network, which are combined with the spatio-temporal density of active fire detections. The mapping method was calibrated and validated using reference datasets for the years 2020 and 2019, respectively. The novelty of this method lies in its high accuracy and multi-latency flexibility: it achieves a Dice coefficient (DC) of 82.7% with zero-day latency, already surpassing the 81.8% accuracy of current state-of-the-art non-time critical methods. As reflectance data availability increases, accuracy scales to DC 84.7% and 85.4% with 5 and 10 days of latency, respectively, and to DC 87.2% for monthly composites with 45 days of latency. Full article

Phytoplankton size classes (PSCs) fundamentally regulate ocean productivity, biogeochemical cycling, and carbon export, yet their distribution and optical variability across the Arabian Sea remain poorly constrained. This study develops and validates a regionally tuned absorption-based approach for phytoplankton size class estimation using in situ phytoplankton absorption spectra (a_ph(λ)) collected during six research cruises between 2016 and 2024. A significant power-law relationship between a_ph(443) and the spectral slope (S_443–510) (R² = 0.963, p < 0.001) provided a consistent optical basis for distinguishing PSCs. Co-located HPLC pigment data were used to derive empirical a_ph(443) thresholds for pico- (≤0.011 m⁻¹), nano- (0.011–0.059 m⁻¹), and micro-phytoplankton (>0.059 m⁻¹). Class-specific mean spectra showed clear optical distinctions consistent with size-dependent pigment packaging. Model evaluation showed reduced error and improved regression agreement relative to existing a_ph- and chl-a-based models when applied to the Arabian Sea dataset, with regression slopes close to unity (0.78–0.81) across all PSCs. This regional model also improved representation of transitional nano communities, which are commonly associated with higher uncertainties in global models. The empirical relationships developed in this study were applied to VIIRS Level 3 a_ph(443) data for 2024 to generate PSC distributions. Satellite-derived PSC fields revealed pronounced spatial gradients and regional contrasts across the Arabian Sea, including micro-phytoplankton blooms in the northern Arabian Sea and mixed nano-dominated communities along the western Arabian Sea (Somali coast). Pico-phytoplankton dominated the low-absorption oligotrophic offshore waters, while nano-phytoplankton were most common in transitional regions influenced by moderate nutrient inputs. Taken together, these results demonstrate that the combined a_ph(443)-S_443–510 framework provides a practical, regionally optimized method for retrieving PSCs at synoptic scales across the Arabian Sea, supporting improved bio-optical modelling and satellite-based monitoring of phytoplankton community structure in this region. Full article