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Satellite-based PM2.5 estimation is an effective means to achieve large-scale and long-term PM2.5 monitoring and investigation. Currently, most of methods retrieve PM2.5 from satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PM2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PM2.5 concentrations from satellite infrared data, thus achieve the PM2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PM2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PM2.5 concentrations correlates with ground-based data well with R2 of 0.79 and RMSE of 15.43 μg · m−3 for hourly PM2.5 estimation. Spatiotemporal distributions of model-estimated PM2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PM2.5 concentrations during daytime and nighttime in real-time monitoring. [ABSTRACT FROM AUTHOR]

Hydrothermal systems can generate phreatic and/or phreatomagmatic explosions with little warning. Understanding the temporal and spatial evolution of geophysical and geochemical signals at hydrothermal systems is crucial for detecting precursory signs to unrest and to inform on hazard. Thermal signatures of such systems are poorly defined because data records are often too short or discrete compared to activity timescales, which can be decadal. La Fossa system of Vulcano has been monitored since the 1980s and entered a period of unrest in 2021. We assessed the thermal signature of La Fossa using ground- and satellite-based data with various temporal and spatial scales. While continuously-recording stations provided continuous but point-based measurements, fumarole field vent surveys and infrared images obtained from satellite-flown sensors (ASTER and VIIRS) allowed lower temporal resolution but synoptic records to be built. By integrating this multi-resolution data set, precursory signs of unrest could retrospectively be detected from February to June 2021. Intensity of all unrest metrics increased during summer 2021, with an onset over a few days in September 2021. By September, seismic, CO2, SO2 and other geochemical metrics also indicated unrest, leading Civil Protection to raise the alert level to yellow on October 1. Heat flux, having been 4 MW in May 2019, increasing to 90 MW by September, and peaking at 120 MW in March 2022. We convolved our thermal data sets with all other monitoring data to validate a Vulcano Fossa Unrest Index (VFUI), framework of which can be potentially applied to any hydrothermal system. The VFUI highlighted four stages of unrest, none of which were clear in any single data set: background, precursory, onset and unrest. Onset was characterized by sudden release of fluids, likely caused by failure of sealed zones that had become pressurized during the precursory phase that began possibly as early as February 2021. Unrest has been ongoing for more than 18 months, and may continue for several more years. Our understanding of this system behavior has been due to hindsight, but demonstrates how multiparametric surveys can track and forecast unrest. [ABSTRACT FROM AUTHOR]

A cryptogenic, invasive‐like red macroalga, Chondria tumulosa, was first observed in 2016 forming thick mats on the forereef of Manawai Atoll within Papahānaumokuākea Marine National Monument. Subsequent expeditions revealed an increased abundance of this alga. In 2021, unattached C. tumulosa was observed forming a network of dark, meandering accumulations throughout the atoll's inner lagoon. High‐resolution satellite imagery revealed that these accumulations became visible in 2015 (length: ~0.74 km; area: ~0.88 km2) and increased 56‐fold in length and 115‐fold in area by 2021 (length: 41.32 km; area: 101.34 km2). An exponential expansion rate of ~16.02 km · y−1 (length), ~44.75 km2 · y−1 (area). This study presents the comprehensive temporal and spatial expansion of C. tumulosa accumulations for Manawai Atoll since its discovery, providing ecologist and resource managers with a proxy to gauge the overall abundance trend of this invasive‐like alga. [ABSTRACT FROM AUTHOR]

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors' retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction's impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction. [ABSTRACT FROM AUTHOR]

Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation. [ABSTRACT FROM AUTHOR]

The movement of active dunes is tightly linked to climatic conditions (e.g., wind regime, temperature and precipitation) as well as human influence (e.g., grazing, dune fixation and greening). Dune migration rates can be studied to draw conclusions of changing wind conditions over time. The Gonghe Basin (GB), located on the north‐eastern Tibetan Plateau (TP), offers a good testing ground for these assumptions. The intramontane basin is highly influenced by two major wind regimes: the mid‐latitude Westerlies and the East Asian summer monsoon. To investigate environmental changes, this study combines optical remote sensing techniques with climatic datasets. High‐resolution satellite images of the last five decades, such as CORONA KH‐4B, are used to map dunes and calculate their respective migration rates. Further, height information was extracted as well. Climatic changes from the ERA‐5 reanalysis dataset and normalized difference vegetation index (NDVI) values were processed alongside. Relating the dunes' surface processes to climate model data shows an accordance between slowing migration, expanding vegetation and a decrease in sand drift potential. From 1968 to present time, an average dune migration rate of 7.3 m a−1 was extracted from the satellite images, with an overall reduction of −1.81 m a−1. The resultant drift potential (RDP) values for the GB are calculated to be below 10 m3 s−3 with a spatial decrease, following a direction from the NW to the SE, fitting well with a corresponding decrease in the migration rates. Our results indicate a good agreement between the development of aeolian landforms and the ERA‐5 climate reanalysis model data, even in a high‐altitude setting with complex topography, which is known to influence such datasets. [ABSTRACT FROM AUTHOR]

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Abstract Rewilding has been suggested as an effective strategy for addressing environmental challenges such as the intertwined biodiversity and climate change crises, but there is little information to guide the monitoring of rewilding projects. Since rewilding focuses on enhancing ecosystem functionality, with no defined endpoint, monitoring strategies used in restoration are often inappropriate, as they typically focus on assessing species composition, or the ecological transition of an ecosystem towards a defined desired state. We here discuss how satellite remote sensing can provide an opportunity to address existing knowledge and data gaps in rewilding science. We first discuss how satellite remote sensing is currently being used to inform rewilding initiatives and highlight current barriers to the adoption of this type of technology by practitioners and scientists involved with rewilding. We then identify opportunities for satellite remote sensing to help address current knowledge gaps in rewilding, including gaining a better understanding of the role of animals in ecosystem functioning; improving the monitoring of landscape‐scale connectivity; and assessing the impacts of rewilding on the conservation status of rewilded sites. Though significant barriers remain to the widespread use of satellite remote sensing to monitor rewilding projects, we argue that decisions on monitoring approaches and priorities need to be part of implementation plans from the start, involving both remote sensing experts and ecologists. Making use of the full potential of satellite remote sensing for rewilding ultimately requires integrating species and ecosystem perspectives at the monitoring, knowledge‐producing and decision‐making levels. Such an integration will require a change in know‐how, necessitating increased inter‐disciplinary interactions and collaborations, as well as conceptual shifts in communities and organizations traditionally involved in biodiversity conservation.

The results show that the lidar in cold orbital conditions has a temperature increase of about 38 degrees Celsius due to thermal design. Also, the range of temperature fluctuations before applying thermal design in the cold state of temperature changes in a circuit is about 14 degrees and after designing these fluctuations have been reduced to about 5 degrees. In hot conditions, the temperature conditions have improved a lot after the design and the maximum operating temperature is about 27 degrees the average temperature has decreased by about 22 degrees, while the temperature fluctuations have also decreased by 21 degrees. A significant temperature increase has occurred in the receiver after applying the thermal design in cold conditions, which is still within the allowed range. This is despite the fact that in hot conditions, after applying the thermal design, there was no significant change in the temperature of the receiver. In the case of the reflector, the conditions are completely different, so the minimum temperature in cold conditions has increased by 42 degrees and the maximum temperature has decreased by 7 degrees in hot conditions. In addition, temperature changes have become more uniform in both cases.

Satellite remote sensing is an important means for humans to achieve high-resolution earth observation, and it has gradually become an important component supporting economic and social development as well as national defense and security.6G pursues deep integration of communication, perception, and computation, and communication and remote sensing integration has become an important feature of it.The development of satellite remote sensing at home and abroad were reviewed, the existing system architecture of satellite remote sensing systems and the integrated system architecture of communication and remote sensing in the 6G era were outlined, and the typical satellite remote sensing technologies and the latest research progress driven by artificial intelligence were elaborated on.In response to the trend of integrating satellite communication and remote sensing in the 6G era, the technical challenges and development trends of realizing the combination of “one satellite, multiple uses, multi-satellite networking, multi-network integration, and intelligent services” were discussed.

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.

In the context of the continuous degradation of the global environment, ecological restoration has become a primary task in global environmental governance. In this process, remote sensing technology, as an advanced monitoring and analysis tool, plays a key role in monitoring ecological restoration. This article reviews the application of remote sensing technology in ecological restoration monitoring. Based on a comprehensive analysis of the literature in the field of ecological remote sensing, it systematically summarizes the major in-orbit spaceborne and airborne sensors and their related products. This article further proposes a series of evaluation indicators for ecological restoration from four aspects: forests, soil, water, and the atmosphere, and elaborates on the calculation methods for these indicators. In addition, this paper also summarizes the methods for evaluating the effectiveness of ecological restoration, including subjective evaluation, objective evaluation, and comprehensive evaluation methods. Finally, we analyze the challenges faced by remote sensing technology in evaluating ecological restoration effectiveness, such as issues with the precision of indicators extraction, the limitations of spatial resolution, and the diversity of evaluation methods. This review also looks forward to future ecological restoration technologies, such as the potential applications of integrated aerospace and terrestrial remote sensing, multi-data fusion, and machine learning technologies. This study reveals the effectiveness of remote sensing technology in ecological restoration monitoring, aiming to provide efficient tools and innovative strategies for future remote sensing monitoring and assessment of ecological restoration.

Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA’s Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity.

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research.

The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amount of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatiotemporal resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on the SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m2 and MBE of 1.59 W/m2 compared to ERA5. The corresponding RMSE and MBE value compared to CERES was 6.52 W/m2 and 2.39 W/m2. Additionally, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to varied cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation. The results suggest that high-height clouds exert a greater degree of radiative forcing more frequently, while low-height clouds are more frequently found in the lower forcing range.

The article presents the discussion on PAR estimates indicate that a variety of physiological and ecological conditions can control the onset of spring blooms. Topics include satellite observations have started to report optimal light intensities for phytoplankton blooming in various physiological and ecological environments; and investigating the optimal light intensities required for phytoplankton spring bloom development in the East Sea (ES).

Real-time detection of satellite remote sensing images is one of the key technologies in the field of remote sensing, which requires not only high-efficiency algorithms, but also low-power and high-performance hardware deployment platforms. At present, the image processing hardware acceleration platform mainly uses an image processing unit (GPU), but the GPU has the problem of large power consumption, and it is difficult to apply to micro-nano satellites and other devices with limited volume, weight, computing power, and power consumption. At the same time, the deep learning algorithm model has the problem of too many parameters, and it is difficult to directly deploy it on embedded devices. In order to solve the above problems, we propose a YOLOv4-MobileNetv3 field programmable gate array (FPGA) deployment scheme based on channel layer pruning. Experiments show that the acceleration strategy proposed by us can reduce the number of model parameters by 91.11%, and on the aerial remote sensing dataset DIOR, the average accuracy of the design scheme in this paper reaches 82.61%, the FPS reaches 48.14, and the average power consumption is 7.2 W, which is 317.88% FPS higher than the CPU and reduces the power consumption by 81.91%. Compared to the GPU, it reduces power consumption by 91.85% and improves FPS by 8.50%. Compared with CPUs and GPUs, our proposed lightweight algorithm model is more energy-efficient and more real-time, and is suitable for application in spaceborne remote sensing image processing systems. [ABSTRACT FROM AUTHOR]

Satellite remote sensing (RS) has been drawing considerable research interest in land-cover classification due to its low price, short revisit time, and large coverage. However, clouds pose a significant challenge, occluding the objects on satellite RS images. In addition, snow coverage mapping plays a vital role in studying hydrology and climatology and investigating crop disease overwintering for smart agriculture. Distinguishing snow from clouds is challenging since they share similar color and reflection characteristics. Conventional approaches with manual thresholding and machine learning algorithms (e.g., SVM and Random Forest) could not fully extract useful information, while current deep-learning methods, e.g., CNNs or Transformer models, still have limitations in fully exploiting abundant spatial/spectral information of RS images. Therefore, this work aims to develop an efficient snow and cloud classification algorithm using satellite multispectral RS images. In particular, we propose an innovative algorithm entitled UCTNet by adopting a dual-flow structure to integrate information extracted via Transformer and CNN branches. Particularly, CNN and Transformer integration Module (CTIM) is designed to maximally integrate the information extracted via two branches. Meanwhile, Final Information Fusion Module and Auxiliary Information Fusion Head are designed for better performance. The four-band satellite multispectral RS dataset for snow coverage mapping is adopted for performance evaluation. Compared with previous methods (e.g., U-Net, Swin, and CSDNet), the experimental results show that the proposed UCTNet achieves the best performance in terms of accuracy (95.72%) and mean IoU score (91.21%) while with the smallest model size (3.93 M). The confirmed efficiency of UCTNet shows great potential for dual-flow architecture on snow and cloud classification. [ABSTRACT FROM AUTHOR]

The contextual surface temperature-vegetation index (TVX) models have been widely used for the retrieval of soil moisture (SM) and evapotranspiration (ET). One of the key premises for their application is to determine quantitatively the theoretical boundaries of this contextual TVX space. Usually these theoretical boundaries are determined based on land surface energy balance principle. Although sound in physical mechanism, the complex parameterization involved has hampered its remote sensing (RS) applications. Our recent studies show that ground-based SM observations can be used for a quick and continuous retrieval of these theoretical boundaries. The main disadvantage lies in its reliance on in-situ SM observations. Fortunately the development of microwave RS technology has made it possible to monitor SM at both regional and global scale. Under this background, a practical framework based on the contextual TVX models was proposed in this paper for continuous ET estimation through the combination of microwave-based SM products and MODIS (Moderate Resolution Imaging Spectroradiometer) products. Daily actual ET over the Southern Great Plains of the United States of America was calculated as the product of net radiation and evaporative fraction (EF). The focus of this framework was to investigate how optical and microwave RS can be combined to achieve a quick and continuous retrieval of EF independently of ground observations. Specifically, EF was retrieved from the contextual TVX models, the theoretical boundaries of which were calibrated at the annual scale based on an optimization scheme. Assuming the seasonal variation of the theoretical dry edge follows a cosine function of solar zenith, the essence of this optimization scheme is to search for the optimal amplitude parameter that maximizes the correlation between microwave-based SM observations and soil moisture index retrieved from the contextual TVX models. Two microwave-based SM products including SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were adopted to demonstrate this optimization scheme. The correlation coefficient, root mean square error and bias of EF estimates calibrated by SMAP were 0.700, 0.146 and 0.047, and those achieved by SMOS were 0.663, 0.174 and 0.168, respectively. The optimization scheme has not only made it possible to conduct a continuous monitoring of ET based entirely on RS observations but also achieved the downscaling of microwave satellite SM products. Thus the combination of optical and microwave RS holds great potential for coupled and continuous estimation of SM and ET over large scale. [ABSTRACT FROM AUTHOR]

Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies. [ABSTRACT FROM AUTHOR]

Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 0~0.375 m, 0~0.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale. [ABSTRACT FROM AUTHOR]

Although wildfires are a common disturbance factor to the environment, some of them can cause significant environmental and socioeconomic losses, affecting ecosystems and people worldwide. The wildfire identification and assessment of their effects on damaged forest areas is of great importance for provision of effective actions on their management and preservation. Forest regrowth after a fire is a continuously evolving and dynamic process, and the accuracy assessment of different remote sensing indices for its evaluation is a complicated task. The implementation of this task cannot rely on the standard procedures. Therefore, we suggested a method involving delineation of dynamic boundaries between conditional categories within burnt forest areas by application of spectral reflectance characteristics (SRC). This study compared the performance of firmly established for fire monitoring differenced vegetation indices—Normalized Difference Vegetation Index (dNDVI) and Normalized Burn Ratio (dNBR) and tested the capabilities of tasseled cap-derived differenced Disturbance Index (dDI) for post-fire monitoring purposes in different forest environments (Boreal Mountain Forest (BMF), Mediterranean Mountain Forest (MMF), Mediterranean Hill Forest (MHF)). The accuracy assessment of the tree indices was performed using Very High Resolution (VHR) aerial and satellite data. The results show that dDI has an optimal performance in monitoring post-fire disturbances in more difficult-to-be-differentiated classes, whereas, for post-fire regrowth, the more appropriate is dNDVI. In the first case, dDI has an overall accuracy of 50%, whereas the accuracy of dNBR and dNDVI is barely 35% and 36%. Moreover, dDI shows better performance in 16 accuracy metrics (from 17). In the second case, dNDVI has an overall accuracy of 59%, whereas those of dNBR and dDI are 55% and 52%, and the accuracy metrics in which dNDVI shows better performance than the other two indices are 11 (from 13). Generally, the studied indices showed higher accuracy in assessment of post-fire disturbance rather than of the post-fire forest regrowth, implicitly at test areas—BMF and MMF, and contrary opposite result in the accuracy at MHF. This indicates the relation of the indices' accuracy to the heterogeneity of the environment. [ABSTRACT FROM AUTHOR]

The ocean is the most important carbon pool in the surface environment. Accurate estimation of CO2 exchange between the sea and the atmosphere is of great significance to further clarify the mechanism of its change process. Using a variety of remote sensing inversion data of sea surface temperature, wind speed, and sea level pressure in the South China Sea (SCS) from 2011 to 2020, a remote sensing estimation model of air-sea CO2 flux was developed based on the air-sea partial pressure difference algorithm, and its temporal and spatial variation characteristics were analyzed. It was shown that: (1) the remote sensing estimation model was highly applicable across the SCS. Compared with the measured regional data, the average absolute error and root mean square error of the estimation results were 1.04 and 1.37 mmol · m-2 · d-1, respectively. The recognition accuracy for identifying source and sink areas was 90.63%. (2) Seasonal variation of CO2 flux in the SCS exhibited higher values in summer and autumn, while lower values were observed in winter and spring. Additionally, there were significant spatial differences between the northern and southern regions of the SCS. The northern area consistently served as a high-value carbon sink, particularly during winter, while the southeastern area of the Indochina Peninsula acted as a carbon source during summer, shifting to the northeast of the SCS during autumn. (3) The CO2 fluxes in three distinct areas of the SCS, namely the northern shelf and slope, central basin, and southern slope, exhibited a decreasing trend over time, with the highest rate of decrease observed in the northern region. From 2011 to 2020, the average annual net carbon release from the SCS to the atmosphere was 1.51 × 107 tons; however, this release exhibited a decreasing trend at a rate of 2.03 × 106 tons per year. Consequently, the overall intensity of the SCS as a carbon source has weakened, suggesting the need for considering these research results when formulating carbon emission and carbon trading policies. [ABSTRACT FROM AUTHOR]

Sugarcane is the major sugar producing crop of India and the prediction of sugarcane yield has immense importance for sugar industries. Satellite remote sensing has the potential to provide periodical information on the morphology throughout the growing season of sugarcane over a large area at various spatial scales and thus has the capability to be used for spatial and regional scale yield prediction. Agrometeorological indicators like crop evapotranspiration (ET), rainfall and crop response factors and the crop health indicators like spectral vegetation index from satellite data influence the crop water and drymatter content and hence can be used as the yield predictors. This study was carried out to generate these agrometeorological and health indicators from the available satellite image and develop yield models based on these indicators for estimating sugarcane yield at a regional scale. The study was conducted over 22 sugarcane growing districts of Uttar Pradesh, India. The bivariate regression model based on ET and Normalized Difference Vegetation Index representing the water relation and the spectral behavior of sugarcane crop was found to be the best (r = 0.79) having RMSE of 7.9% in terms of consistency and performances as compared to univariate models that account only spectral behaviour or only water relations. Moreover, the moisture availability in terms of ET when used along with the crop response factor based on the relative yield proved to be a better yield predictor than the ET alone. This study brought out the options to choose the model depending on the availability and accessibility of input data products from space-based optical and thermal remote sensing. [ABSTRACT FROM AUTHOR]

Computational skills are advantageous for teaching students to investigate environmental change using satellite remote sensing. This focus is especially relevant given the disproportionate underrepresentation of minorities and women in STEM fields. This study quantified the effects in both a STEM and a non-STEM class of Earth science remote sensing modules in Google Earth Engine on students' self-efficacy in coding, understanding remote sensing, and interest in science and a career in environmental research. Additionally, the STEM students engaged in a course-based undergraduate research experience (CURE) on water quality. Satellite imagery was used to visualize water quality changes in coastal areas around the world due to the COVID-19 pandemic shutdown. Pre- and post-surveys reveal statistically significant changes in most students' confidence to apply coding skills to investigate environmental change and understand remote sensing. The intervention was not sufficient to lead to significant changes in interest in science or a career in environmental research. There is great benefit in incorporating remote sensing labs to teach environmental concepts to STEM and non-STEM students and to bolster the confidence of underrepresented minorities and females in STEM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km 2 in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information. [ABSTRACT FROM AUTHOR]

Ocean color data are essential for developing our understanding of biological and ecological phenomena and processes, and also important sources of input for physical and biogeochemical ocean models. Chlorophyll-a (Chl-a) is a critical variable of ocean color in the marine environment. Quantitative retrieval from satellite remote sensing is a main way to obtain large-scale oceanic Chl-a. However, data missing is a major limitation in satellite remote sensing-based Chl-a products, due mostly to the influence of cloud, sun glint contamination, and high satellite viewing angles. The common methods to reconstruct (gap filling) missing data often consider spatiotemporal information of initial images alone, such as data interpolation empirical orthogonal function, optimal interpolation, Kriging interpolation, and extended Kalman filter. However, these methods do not perform well in the presence of large-scale missing values in the image and ignore the potential of other information on missing pixels in the data reconstruction. Here we developed a convolutional neural network (CNN) named OCNET for Chl-a concentration data reconstruction in open ocean areas, considering environmental variables that are associated with ocean phytoplankton growth and distribution. Sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) from reanalysis data and satellite observations were selected as the input of OCNET to correlate with the environment and phytoplankton mass. The developed OCNET model achieves good performance in the reconstruction of global ocean Chl-a concentration data, and captures temporal variations of these features. This study also shows the potential of machine learning in large-scale ocean color data reconstruction and offers the possibility to predict Chl-a concentration trends under a changing environment. [ABSTRACT FROM AUTHOR]

Agriculture, being a labor-intensive sector, plays a crucial role in ensuring food security. Scientific advancements have rapidly influenced various fields, including agriculture, with significant impacts on genetics, plant science, environmental studies, climate research, land management, machinery, technology, and remote sensing. These advancements have resulted in improved resource management, leading to a reduction in food crises and famines. The focus of the study was on utilizing satellites images to monitor changes in vegetative growth grapevines, specifically using Sentinel-1 and Satellites-2. The study confirmed the effectiveness of these satellites data in accurately monitoring leaf changes by analyzing the VH and VV bands of Sentinel-1, as well as the NDVI bands 8-4, 5-6, 5-7, and 5-8a of Sentinel-2. The relationships between these bands and leaf changes showed R² significant values of 0.72, 0.42, 0.51, 0.51, and 0.52, respectively. However, lower accuracy was observed for chlorophyll. These findings highlight the high precision of satellites in monitoring changes in vegetative growth of grapevines and underscore the importance of developing improved techniques for monitoring and analyzing chlorophyll. That leads in future, to apply pressing Agriculture. [ABSTRACT FROM AUTHOR]

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon‐climate feedbacks. Here, we develop a satellite‐based rapid attribution workflow and demonstrate its use at a 1–2‐month latency to attribute drivers of the carbon cycle feedbacks during the 2020–2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil–plant–atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture‐deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions. [ABSTRACT FROM AUTHOR]

In order to study the impact of gas released from coal fire combustion on the spatial–temporal distribution of CO2 and CH4 and other greenhouse gas emissions, the impact of regional coal fire on CO2 and CH4 emission flux was comprehensively evaluated using Landsat 8 and GOSAT satellite data in Xinjiang. In addition, typical fire areas are selected, a single-channel algorithm is used to invert the surface temperature of the coal field, the spatial distribution of the coal fire area is extracted by setting the threshold, and the influence law of CO2 and CH4 emissions in the typical fire area is accurately analyzed. The results show that during 2017–2018, CO2 and CH4 emissions in Xinjiang were generally dispersed and locally concentrated, while CO2-O and CH4-O were at low levels in most regions, fluctuating in the ranges of 0.01 ~ 0.14 g·m−2·day−1 and 0.001 ~ 0.003 g·m−2·day−1, respectively. However, the emission intensity of CO2-O and CH4-O in coal fire concentrated areas is higher, which are 1.6 ~ 3.8 g·m−2 day−1 and 0.013 ~ 0.026 g·m−2·day−1, respectively. CO2-F and CH4-ag have similar laws. The fire area of Daquan Lake is scattered, and there are four areas with the surface temperature over 35 °C: A, B, C, and D, respectively. The Sandaoba fire area is more concentrated, and only two areas are E and F when the surface temperature exceeds 35 °C. CO2 and CH4 released by burning in Daquan Lake and Sandaoba fire areas increased CO2-F and CH4-ag by 2.08 and 0.89 times, respectively. The results provide a reference for coal fire control and carbon emission reduction. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) Indicators Tiger Team collaboration between NASA-supported scientists and civil society stakeholders aimed to develop satellite-derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

True-color imagery is essential for an intuitive comprehension of atmospheric data. However, the Geostationary Environment Monitoring Spectrometer (GEMS) of the geostationary Korea multipurpose satellite (GK) 2B lacks green and red bands, which limits its ability to monitor atmospheric environments. To mitigate this issue, we suggest an innovative method of generating virtual GEMS green and red bands using conditional generative adversarial networks with data observed in the blue-green-red (RGB) bands of the Advanced Meteorological Imager sensor, a payload of the GK-2A satellite. The paired datasets of the AMI blue band and the AMI RGB bands were used to train and test the data-to-data (D2D) translation model. Using the GEMS blue band as input data, the D2D model generated GEMS hypothetical radiance data at the green and red bands. Our results show that the D2D model generated hypothetical GEMS green and red bands with outstanding performance. The averaged values of the correlation coefficient, root-mean-square error, and bias between the observed and D2D-generated GEMS blue band were 0.999, 3.450 W/cm2/cm/sr, and −1.858 W/cm2/cm/sr, respectively. This research is expected to significantly contribute to the monitoring and comprehension of atmospheric environments in Asia and potentially improve the GEMS's global ability to monitor air quality. Additionally, the proposed method has the potential to enhance the capabilities of other satellites with limited spectral bands.

The Atmospheric Boundary Layer (ABL) links the free atmosphere and the earth surface (land and ocean), and plays a significant role in the initiation and development of rainfall. The ABL height is a key parameter of ABL, and usually applied to parameterizations of the ABL processes in numerical weather prediction and climate models. Obtaining accurate ABL height data can contribute to improving prediction skills of numerical weather prediction and climate models. The present paper introduces main methods in evaluating the ABL heights from a variety of profile data, such as conventional radiosonde data, satellite remote sensing data, aerosol backscattering intensity data, and global navigation satellite system (GNSS) radio occultation (RO) sounding data. Additionally, some progresses are presented. Three rules are suggested for dealing with the ABL with different ABL height estimates. Particular attention is paid to the application of the parameterization schemes of the ABL processes in numerical weather (or climate) prediction models, such as Weather Research and Forecasting (WRF), which includes application of ABL height data in solving the equation of turbulent eddy dispersion. And some prospects are presented finally.