High Temporal Frequency Research Articles

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

AbstractThere is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US west coast precipitation and trends in ARs can provide insight into future precipitation trends. There are already a variety of different methods used to identify ARs, but many are used in contexts that are often difficult to apply to large climate datasets due to their computational cost and requirement of integrated vapor transport as an input variable, which can be expensive to compute in climate models at high temporal frequencies. Using deep learning (DL) to track ARs is a unique approach that can alleviate some of the computational challenges that exist in more traditional methods. However, some questions still remain regarding its flexibility and robustness. This research investigates the consistency of a DL methodology of tracking ARs with more established algorithms to demonstrate its high‐level performance for future studies.

SAR time series despeckling via nonlocal matrix decomposition in logarithm domain

Advanced satellite and signal processing techniques have made Synthetic Aperture Radar (SAR) capable of monitoring the dynamic processes of the earth with high temporal frequency through co-registered time series. There are several challenges in interpreting these three-dimensional data cubes, including the inherent speckle effect and outliers caused by abrupt changes or sporadically appearing objects. In this paper, we propose a novel despeckling method, LogSAR-NLMD, based on nonlocal matrix decomposition in the logarithm domain to despeckle SAR time series without producing the artifacts caused by outliers. A maximum a posteriori (MAP) problem is developed by taking into account smoothness and low-rank prior knowledge in addition to the likelihood of SAR reflectivities following the Fisher-Tippett distribution. Additionally, nonlocal self-similarity is exploited to construct time series matrices that satisfy the prior better than the original data. In order to solve the MAP problem, the variable splitting procedure and the alternating direction method of multipliers (ADMM) framework are used. The proposed method has been validated by multiple experiments on synthetic and real data by comparing it with other state-of-the-art methods for despeckling SAR time series.

High temporal frequency vehicle counting from low-resolution satellite images

Frequent object counting at a specific location (FOC@Loc) is becoming a newly emerging but highly demanded task since the evolution of human activities can provide crucial statistics for social and economic development. Due to the unique requirement of both high temporal frequency for frequent observations and high spatial resolution for object counting, this article aims to propose a novel framework for FOC@Loc to take advantage of both high definitions of high-resolution (HR) image and continuous low-cost low-resolution (LR) image at the same location. To compensate for the low ground sample distance (GSD) in LR images, some prior knowledge about the fixed location is extracted from HR images, including (1) the short-term spatial consistency: provide exact feature-wise and pixel-wise guidance from HR image to the LR image on the same date, learning to predict vehicle area for each LR image; (2) the long-term location consistency: provide the prior parking density of the study location from the sparse HR image sequence, turning the vehicle area into the counting number of each LR image. The final results indicate that this new method obtains highly consistent counting results with the manual annotations, proofing that the HR image guidance information can promote the utilization of LR images for object counting and further analysis. The proposed dataset and methodology have the potential to boost the applications of extensive and inexpensive LR satellite images.

Contrast and Luminance Gain Control in the Macaque's Lateral Geniculate Nucleus.

There is substantial variation in the mean and variance of light levels (luminance and contrast) in natural visual scenes. Retinal ganglion cells maintain their sensitivity despite this variation using two adaptive mechanisms, which control how responses depend on luminance and on contrast. However, the nature of each mechanism and their interactions downstream of the retina are unknown. We recorded neurons in the magnocellular and parvocellular layers of the lateral geniculate nucleus (LGN) in anesthetized adult male macaques and characterized how their responses adapt to changes in contrast and luminance. As contrast increases, neurons in the magnocellular layers maintain sensitivity to high temporal frequency stimuli but attenuate sensitivity to low-temporal frequency stimuli. Neurons in the parvocellular layers do not adapt to changes in contrast. As luminance increases, both magnocellular and parvocellular cells increase their sensitivity to high-temporal frequency stimuli. Adaptation to luminance is independent of adaptation to contrast, as previously reported for LGN neurons in the cat. Our results are similar to those previously reported for macaque retinal ganglion cells, suggesting that adaptation to luminance and contrast result from two independent mechanisms that are retinal in origin.

The Potential of Monitoring Carbon Dioxide Emission in a Geostationary View with the GIIRS Meteorological Hyperspectral Infrared Sounder

With the help of various polar-orbiting environment observing platforms, the atmospheric concentration of carbon dioxide (CO2) has been well established on a global scale. However, the spatial and temporal pattern of the CO2 emission and its flux dependence on daily human activity processes are not yet well understood. One of the limiting factors could be attributed to the low revisit time frequency of the polar orbiting satellites. With high revisiting frequency and CO2-sensitive spectrum, the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY-4A and FY-4B satellites have the potential to measure the CO2 concentration at a higher temporal frequency than polar-orbiting satellites. To provide a prototypical demonstration on the CO2 monitoring capability using GIIRS observations, a hybrid-3D variational data assimilation system is established in this research and a one-month-long experiment is conducted. The evaluations against the Goddard Earth Observing System version 5 (GEOS-5) analysis field and Orbiting Carbon Observatory -2/-3 (OCO-2/-3) CO2 retrieval products reveal that assimilating GIIRS observations can reduce the first guess’s CO2 concentration mean bias and standard deviation, especially over the lower troposphere (975–750 hPa) and improve the diurnal variation of near surface CO2 concentration.

A low‐cost IoT network to monitor microclimate variables in ecosystems

Abstract Microclimatic and macroclimatic data differ, and microclimatic data are most useful at short time intervals (30‐min scales rather than daily/monthly scales). We developed an end‐to‐end system to acquire such data at a high temporal resolution, transfer them to a server through an Internet of Things network using low‐cost technologies, and make them available with dynamic graphical visualizations and download capabilities. The system has been used for 2 years to monitor environmental variables in contrasting agroecosystems in France, Bolivia and Kenya. It has been proven to be reliable and supports the mismatch between macro‐ and microclimates. This low‐cost Internet of Things system can capture the microclimate in contrasting environments with accuracy comparable to commercial solutions, and great flexibility in data processing. It thus constitutes a possible solution in an academic context and has the potential to be used by a broad audience of scientists interested in capturing environmental variables in real time and at a high temporal frequency.

Assessment of Oak Groves Conservation Statuses in Natura 2000 Sacs with Single Photon Lidar and Sentinel-2 Data

Among the main objectives of Natura 2000 Network sites management plans is monitoring their conservation status under a reasonable cost and with high temporal frequency. The aim of this study is to assess the ability of single-photon light detection and ranging (LiDAR) technology (14 points per m2) and Sentinel-2 data to classify the conservation status of oak forests in four special areas of conservation in Navarra Province (Spain) that comprise three habitats. To capture the variability of conservation status within the three habitats, we first performed a random stratified sampling based on conservation status measured in the field, canopy cover, and terrain slope and height. Thereafter, we compared two metric selection approaches, namely Kruskal–Wallis and Dunn tests, and two machine learning classification methods, random forest (RF) and support vector machine (SVM), to classify the conservation statuses using LiDAR and Sentinel-2 data. The best-fit classification model, which included only LiDAR metrics, was obtained using the random forest method, with an overall classification accuracy after validation of 83.01%, 75.51%, and 88.25% for Quercus robur (9160), Quercus pyrenaica (9230), and Quercus faginea (9240) habitats, respectively. The models include three to six LiDAR metrics, with the structural diversity indices (LiDAR height evenness index, LHEI, and LiDAR height diversity index, LHDI) and canopy cover (FCC) being the most relevant ones. The inclusion of the NDVI index from the Sentinel-2 image did not improve the classification accuracy significantly. This approach demonstrates its value for classifying and subsequently mapping conservation statuses in oak groves and other Natura 2000 Network habitat sites at a regional scale, which could serve for more effective monitoring and management of high biodiversity habitats.

Regional switchgrass carbon sequestration estimates from high‐frequency eddy covariance and Mesonet observations

AbstractMonitoring net ecosystem carbon dioxide (CO2) exchange (NEE) using eddy covariance (EC) flux towers is quite common, but the measurements are valid at the scale of tower footprints. Alternative ways to quantify and extrapolate EC‐measured NEE across potential production areas have not been explored in detail. To address this need, we used NEE measurements from a switchgrass (Panicum virgatum L.) ecosystem and detailed meteorological measurements from the Oklahoma Mesonet and developed empirical relationships for quantifying seasonal (April to October) sums of NEE across potential switchgrass establishment landscapes in Oklahoma, USA. We identified ensemble areas for potential switchgrass expansion regions and created thematic maps of switchgrass productivity using geostatistics and geographic information systems (GIS) routines. The purpose of this study was to explore if model parametrizations based on high temporal frequency meteorological forcing can be used for reliable estimates of NEE for evaluating the source–sink status of carbon. Rectangular hyperbolic light‐response curve and temperature response functions were fitted using EC measurements to estimate gross primary production (GPP) and ecosystem respiration (ER), respectively, on a seasonal scale. Model performance validated by comparing EC‐measured seasonal NEE for 3 yr showed good‐to‐strong agreement (.29 < R2 < .91; p < .05). Additionally, total seasonal NEE estimates were validated with measured biomass data in three additional locations. The estimated seasonal average net ecosystem production (NEP = −NEE) was 3.97 ± 1.92 (SD) Mg C ha−1. However, results based on a simple linear model suggested significant differences in NEP between contrasting climatic years. Overall, the results from this study indicate that this new scaling‐up approach involving high temporal resolution meteorological data may be a helpful tool for assessing spatiotemporal heterogeneity of switchgrass production and the potential of switchgrass fields to sequester carbon in the Southern Great Plains of the United States.

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources.

Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals' emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

Exploring Convolutional Neural Networks for Predicting Sentinel-C Backscatter Between Image Acquisitions

Sentinel-1 C-band radar backscatter satellite images provide a repeating sequence of fine-resolution (10-m) observations that can be used for a number of applications, but the 12-day interval between satellite observations is too infrequent for many applications, such as measuring moisture dynamics. For a variety of applications, moisture information is demanded at high temporal frequency and fine spatial resolution over large areas. Machine learning approaches have been used to predict higher spatial resolutions than the original satellite images, but little effort has been made to increase the temporal resolution of acquired backscatter images. This study extends machine learning approaches to infer fine-resolution backscatter between observations relying on auxiliary data observations, including elevation and daily gridded weather. Several variations of Multi-modal Fully Convolutional Neural Network architectures, problem setup, and training methods are explored for a predominantly rural area in southwest Oklahoma near the transition between humid subtropical and semiarid climates. The training area lies in the overlap zone for adjacent Sentinel-1 satellite tracks, allowing for training with several different temporal offsets. We find that the UNET architecture produced the most accurate and robust estimated backscatter patterns, with superior prediction compared to a prior observation baseline in nearly all cases investigated when geography was included in the training data. This superior performance also generalized to nearby areas when training data for a given geography was not available, where 86% of predictions performed superior compared to a prior observation baseline.

Comparison of acoustic and hydrodynamic cavitation: Material point of view

This study investigated the difference in mechanical response of the martensitic stainless steel X3CrNiMo13-4/S41500/CA6 NM QT780 between hydrodynamic and acoustic cavitation erosion. The results show that acoustic cavitation erosion generates small pits at a high temporal frequency on the material, while hydrodynamic cavitation erosion produces larger pits at a lower frequency. Acoustic cavitation erosion tests have been performed using a 20 kHz ultrasonic horn located at 500 μm in front of a specimen. This experimental setup, known as an indirect method, is inspired from the ASTM G32 standard. Hydrodynamic cavitation erosion tests were conducted with classic experimental conditions of a PREVERO device: a cavitation number of 0.87 corresponding to a flow velocity of 90 m s−1 and an upstream pressure of 40 bars. In addition, for a given exposure time, the percentage of surface covered by the pits is smaller for acoustic cavitation than for hydrodynamic cavitation. Three successive steps have been identified during the damage process: persistent slip bands (PSB) first appear on the surface, cracks initiate and propagate at the PSB locations and nonmetallic interfaces, and finally, parts of the matter are torn off. A careful time examination of the same small area of the exposed sample surface by scanning electron microscopy reveals that acoustic cavitation is faster to initiate damage than hydrodynamic cavitation.

Citizen science data validates aerial imagery to track the ‘rise and fall’ of woody vegetation through extremes of climate

Context Ground truthing remotely sensed imagery for detecting changes in wetland vegetation can be time-consuming and costly for monitoring. Harnessing the resources of citizen scientists (CS) using mobile devices has been under utilised in Australia. Aims The project aimed to test the feasibility and practicality of using CS to collect data using mobile devices to ground truth remotely sensed imagery. Methods Using high-resolution aerial imagery, we detected the establishment of woody vegetation over a 20-year dry phase from 2000 to 2020 in Thirlmere Lakes National Park, NSW, Australia. To ground truth these woody species, we engaged with a local community group using a customised, freely available mobile device application. Key results During the dry event of 2020, CS documented well-established woody species, such as Melaleuca linariifolia (flax-leaved paperbark), amongst the Lepironia articulata grey rush. With the La Niña wet events in early 2020–22 and subsequent higher water levels, the CS documented the survival of M. linariifolia but the dieback of eucalypts, and other woody species. Conclusions Observations at higher temporal frequencies by CS using mobile devices, augmented with researchers’ observations, proved to be a valuable, quality-controlled method to ground truth high-resolution aerial imagery. Implications This case study showed that monitoring the phenology of vegetation in a peat wetland can be supplemented by the inclusion of a CS programme. This under-utilised resource can increase coverage and frequency of data observations, lower costs as well as create community awareness, capability and engagement in scientific research.

Analysis of CYGNSS Soil Moisture Retrieval Based on ANN over a Selected Region in Ethiopia

Soil moisture (SM) plays a vital role in agriculture, ecosystem functioning, water conservation, weather predictions and climate models. High spatial and temporal frequency data of soil moisture is crucial for agricultural and other important applications. Recent advancements have brought attention to the possibility of using GNSS reflectometry (GNSS-R) for applications on land such as snow sensing, soil moisture retrieval, sea surface monitoring and other applications in addition to positioning, navigation, and timing applications of GNSS. Cyclone Global Navigation Satellite System (CYGNSS) is designed to improve hurricane forecasting by studying the interaction between the ocean and the atmosphere within tropical cyclones. However recent studies show the opportunity of this system for high spatio-temporal soil moisture retrieval. This study presents a machine learning-based approach to get SM at a selected region in Ethiopia using CYGNSS data and analysis of the result. Artificial Neural Network (ANN) model is developed and used to predict soil moisture. The Soil Moisture Active Passive (SMAP) global soil moisture data have been used as reference data in the ML algorithm. The proposed approach has achieved a good correlation between predicted values of soil moisture and reference values from SMAP.

Use of satellite imagery to categorize vegetation on the French railway network (SNCF Réseau)

In order to ensure the safety and regularity of train traffic, as well as the good condition of the railway infrastructures, SNCF Réseau must control the vegetation with efficiency on the network. For this purpose, the French railway infrastructure manager needs to know which kind of vegetation grows on the network. To make the inventory of the vegetation, a processing chain of very high spatial resolution satellite imagery has been developed. The use of a machine learning algorithm for supervised classification and the automation of the processing chain as well as the reuse of pre-trained models allowed to industrialize at a high temporal frequency the inventory of the vegetation along the French railway network.

Flow partitioning modelling using high-resolution electrical conductivity data during variable flow conditions in a tropical montane catchment

Tracer-aided hydrological models (TAHMs) are one of the most powerful tools to identify new (event) and old (pre-event) water fractions contributing to stormflow because they account both for streamflow and tracer mixing dynamics in model calibration. Nevertheless, their representativeness of hydrograph dynamics is often limited due to the unavailability of high-resolution conservative tracer data (e.g., water stable isotopes or chloride). Hence, there is a need to identify alternative tracers yielding similar flow partitioning results than “ideal” ones while requiring fewer financial resources for high-frequency monitoring (e.g., sub-hourly). Here, we compare flow partitioning results of a TAHM calibrated using high-frequency electrical conductivity (EC) and water stable isotope (18O) data collected during 37 rainfall-runoff events monitored during variable hydrometeorological conditions in the Zhurucay Ecohydrological Observatory, a tropical alpine catchment located in southern Ecuador. When the model was calibrated using the sampling resolution of stables isotopes (6-hours to 1-hour), no statistically significant differences of pre-event water fractions (PEWFs) using both tracers for model calibration were found. PEWF differences between both tracers for 89% of the events were < 20% regardless of the events’ antecedent moisture and rainfall conditions. Model transfer functions were also similar suggesting that catchment internal processes inferred using both tracers are comparable. Events presenting larger differences (n = 4; up to 27% PEWF difference) had no samples collected during peak flow. Calibration of the model using EC data collected at sub-hourly intervals (every 5-minutes) showed a significant increase in model performance as compared to the frequency of collection of isotopic data. Similarity in flow partitioning results can be attributed to a quasi-conservative nature of EC due to the presence of organic-rich riparian soils (peat-type) overlying compact bedrock across the catchment. Findings also highlight the importance of capturing rapidly occurring catchment mixing processes though high-temporal frequency monitoring of tracer data. Our study encourages the value of assessing the use of alternative tracers, such as EC, to identify fast occurring rainfall-runoff processes, while lowering the costs needed to implement and sustain tracer data collection for long time periods.

Stereopsis for rapidly moving targets.

Stereoscopic depth perception is possible with luminance-defined target velocities at least as high as 600° s-1, up to the limit of 30 Hz imposed by the high-temporal frequency cut-off of the eye. The limitation for perceiving depth from stereo disparity of moving targets is not their velocity but the temporal frequency bandwidth of the eye, which is affected by adaption state. Stereoacuity for a depth shift in a horizontally moving grating depends not on spatial disparity between corresponding luminance points in spatial units of arc min, but on the spatial shift as a fixed proportion of the period of the grating, in other words, on the phase angle difference between the two eyes, as is also the case for obliquely orientated, stationary gratings. Phase differences explain not only the classic Pulfrich stereophenomenon but its equivalent with dynamic visual noise, and a new effect in which depth results from interocular phase differences in luminance modulation. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

An assessment of high temporal frequency satellite data for historic environment applications. A case study from Scotland

AbstractThis paper assesses the value of high temporal frequency satellite data with various spatial sampling resolutions for multi‐scalar historic environment survey and management use cases in Scotland, specifically for broad‐brush landscape characterisation, for monitoring the condition of monuments and for the discovery of otherwise unknown sites. Dealing with a part of the world where applications of satellite imagery are almost entirely unexplored, this study takes a real‐world approach, which foregrounds the purpose at hand rather than presenting a case study from an optimal setting. The study highlights the importance of detailed imagery to support interpretation in some instances, and the challenges of obtaining time‐critical optical imagery in a part of the world that experiences significant periods of cloud cover. The real‐world availability of data in such settings is assessed, highlighting that even with daily revisits, useable imagery cannot be guaranteed. The implications of current and past tasking patterns for availability of high‐resolution data now and in the future are discussed. The study identifies the complementary roles that satellite imagery can fulfil, while identifying the limitations that remain to fuller applications of such data, in a study that will be relevant to many parts of Europe and beyond.

Shock Boundary Layer Interaction and Aero-Optical Effects in a Transonic Flow over Hemisphere-on-Cylinder Turrets

Hemisphere-on-cylinder turrets are the main airborne optical platform structure. However, an unsteady shock boundary layer interaction (SBLI) would act on flow separation and turbulent wake, which causes serious aero-optical effects with high spatial and temporal frequency characteristics. In this paper, the SBLI phenomenon of a hemisphere-on-cylinder turret is recorded in a wind tunnel at Ma = 0.7 using shadowing and Mach-Zehnder interferometer measurements. Its wavefront distortion is measured using the Shack-Hartmann measurement. The detached eddy simulation (DES) based on SST k - ω turbulence model and ray-tracing methods are used to reproduce the transonic flow and optical aberration. Experiments and simulations suggest that the SBLI causes the flow to separate earlier relative to a subsonic flow over the turret. The time-averaged root-mean-square of optical path difference (OPD) over the beam aperture is 0.56 λ∼0.59 λ with λ as the wavelength, while the root-mean-square of the time-averaged OPD is about 0.45 λ. The local shock and wavefront distortion have dual peak frequencies at S t D = f D / U ∞ = 0.24 and 0.34, different from the single-peak-frequency phenomenon of a subsonic flow over turrets. Fast model decomposition of wavefront can be performed by proper orthogonal decomposition (POD) of its Zernike coefficients. The first two modes contain the shock’s reciprocating motion.

A theoretical model reveals specialized synaptic depressions and temporal frequency tuning in retinal parallel channels.

In the Outer Plexiform Layer of a retina, a cone pedicle provides synaptic inputs for multiple cone bipolar cell (CBC) subtypes so that each subtype formats a parallelized processing channel to filter visual features from the environment. Due to the diversity of short-term depressions among cone-CBC contacts, these channels have different temporal frequency tunings. Here, we propose a theoretical model based on the hierarchy Linear-Nonlinear-Synapse framework to link the synaptic depression and the neural activities of the cone-CBC circuit. The model successfully captures various frequency tunings of subtype-specialized channels and infers synaptic depression recovery time constants inside circuits. Furthermore, the model can predict frequency-tuning behaviors based on synaptic activities. With the prediction of region-specialized UV cone parallel channels, we suggest the acute zone in the zebrafish retina supports detecting light-off events at high temporal frequencies.

Near real-time mapping of burned area by synergizing multiple satellites remote-sensing data

ABSTRACT Wildfires have significant impacts on human lives, critical infrastructures, and Earth’s ecosystems. Accurate and timely information on burned area (BA) affected by wildfires is vital to better understand the drivers of wildfire events, as well as its relevance for biogeochemical cycles, climate, and air quality, and to aid wildfire management. Single satellite data have been used to detect the characteristics of wildfires, retrospectively mapping BAs at a variety of spatial resolutions in previous studies. However, due to the trade-off between spatial and temporal resolutions, single-source satellite data are not sufficient to characterize the explicit dynamics of BAs at high resolutions in both space and time. Thus, a two-stage near real-time BA mapping method was developed in this study to take advantage of the high temporal frequency of coarse resolution sensors and the fine spatial resolution of medium resolution sensors in BA mapping by synergizing freely available coarse and medium spatial resolution (MSR) sensors. First, high temporal frequency sensors such as MODIS and VIIRS were used to identify wildfires and potential BAs. Then, multiple MSR sensors such as Sentinel-2A/2B, Landsat OLI, and Resourcesat AWiFS were synthesized for extracting the BAs with more spatial details in near real-time. We applied the method in California, USA, where wildfires occurred in northern and southern parts in 2017. The results showed that the proposed method is promising for BA mapping with an overall accuracy of 0.84 and 0.85 for wildfires in northern and southern California, respectively. Additionally, the proposed method greatly improved the frequency and reduced the latency, with an average interval of 3.5 days (3 days) and latency of 4 days (sub-daily) for wildfires in southern (northern) California. The extracted BAs illustrated accurate spatial details with MSR sensors. Our method can significantly take advantage of multi-source remote-sensing observations to accurately map the BAs of active wildfires in near real-time. More importantly, the method can be applied to other geographic regions where wildfires risk humans and ecosystems.