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Understanding the spatial and temporal variations of CO2 column concentration (CO2-CCs) is crucial for tackling climate change and promoting sustainable human development. This study provides an in-depth analysis of CO2 dynamics in the Yellow River Basin, an area significantly affected by both natural and anthropogenic factors. Using data from the Orbiting Carbon Observatory 2 (OCO-2) and the Fourier transformation spectrometer (FTS) of the GOSAT satellite remote sensing sensors, supplemented with ground station data from the Waliguan station, we scrutinized the CO2 levels in the region from 2013 to 2022. The regional CO2-CC displayed a 12-month cyclical variation and a continuous upward trend, escalating by approximately 4.26% over the 10-year period. Spatiotemporal differences were evident in the monthly variation of CO2-CC, with peak and minimum values occurring in May and August respectively. Geographically, the highest CO2-CC was found in the central part of the basin, while the lowest was in the northern part of Inner Mongolia. This study underscores the increased significance of the region's CO2-CC, which showed an increase from 17.0 ppm at the start of the period to 21.0 ppm by the end, representing an overall growth of between 4.35 and 5.25%. The findings highlight the urgency of targeted measures to mitigate CO2 emissions and adapt to their consequences in the Yellow River Basin, contributing to the global efforts against climate change and towards sustainable development. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) caused by lake eutrophication and climate change have become one of the most serious problems for the global water environment. Timely and comprehensive data on HABs are essential for their scientific management, a need unmet by traditional methods. This study constructed a novel digital twin lake framework (DTLF) aiming to integrate, represent and analyze multi-source monitoring data on HABs and water quality, so as to support the prevention and control of HABs. In this framework, different from traditional research, browser-based front ends were used to execute the video-based HAB monitoring process, and real-time monitoring in the real sense was realized. On this basis, multi-source monitored results of HABs and water quality were integrated and displayed in the constructed DTLF, and information on HABs and water quality can be grasped comprehensively, visualized realistically and analyzed precisely. Experimental results demonstrate the satisfying frequency of video-based HAB monitoring (once per second) and the valuable results of multi-source data integration and analysis for HAB management. This study demonstrated the high value of the constructed DTLF in accurate monitoring and scientific management of HABs in lakes. [ABSTRACT FROM AUTHOR]

The application of differential optical absorption spectroscopy (DOAS) for the diagnostics of localized methane emission based on data from satellite spectral measurements is discussed. It is shown that the application of DOAS in a scattering atmosphere can lead to significant errors in estimates of the concentration of methane. It is proposed that it be supplemented by a filtering procedure for signals distorted by atmospheric scattering effects which takes into account the correlation of the errors in the recovered concentrations of methane and water vapor owing to the uncertainty in the trajectory of the photons propagating in a scattering atmosphere. This correlation has been estimated by numerical calculations of these errors for a model atmosphere that includes cirrus clouds and two types of surface aerosols. [ABSTRACT FROM AUTHOR]

Scarce water resources across the globe have prompted the development of data‐driven water budgets to account for and distribute limited water more effectively across various land uses and purposes. Data‐driven approaches for estimating individual water budget components have been extensively developed and subsequently reviewed (e.g., evapotranspiration, precipitation, groundwater, surface water, runoff), but the state of the art of data‐driven approaches for estimating and integrating complete water budgets has not been the subject of a review paper to our knowledge. In this review, we fill this void by reviewing 81 systematically identified publications from the last decade (2012–2022) on data‐driven water budget approaches. We describe the current state of measurements and data products for data‐driven water budgets for various spatiotemporal scales. Our analysis suggests that spatiotemporal parameters drive the approach for data‐driven water budgets, with larger spatiotemporal scales relying more on satellite remote sensing data products and smaller spatiotemporal scales relying more on ground‐based monitoring. The incorporation of satellite remote sensing data products and ground‐based monitoring was common across various spatiotemporal scales and enabled the estimation of complete water budgets in areas of limited data availability. We conclude that improved reporting of simplifying assumptions, uncertainty analysis methods, and data sources are required for the alignment of water budget estimations between resource managers at varied spatiotemporal scales. Our review calls for the standardization of data‐driven water budget reporting protocols to improve the interpretability of data‐driven water budgets across decision‐makers working at various spatiotemporal scales. Plain Language Summary: Scarce water resources across the globe have prompted the monitoring of water budgets using data, which we refer to as a data‐driven water budget. We review the current state of measurements and data products for data‐driven water budgets of various scales—from field to region and over minutes to decades. We conclude that satellite remote sensing data products are suitable for data‐driven water budgets over large areas and long periods, but ground‐based data are more suitable over small areas and shorter periods. We identify challenges for data‐driven water budgets, which call for standardized reporting protocols for data‐driven water budgets. Key Points: The frequency and spatial scale of data‐driven water budgets differ in the integration of ground‐based and satellite remote sensing dataThe simplifying assumptions, uncertainty, and data sources should be reported in a data‐driven water budgetThe standardization of data‐driven water budget reporting protocols is needed for decision‐makers working at various spatiotemporal scales [ABSTRACT FROM AUTHOR]

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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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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.

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.

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 glaciers of the Val Veny (Italian side of the Mont-Blanc Massif) have been the site of numerous field observations during the last decades, in particular for the study of glacial fluctuations or surface processes related to the debris cover. Here, we propose to examine how satellite observations can complement field measurements on the state and fate of the Val Veny glaciers. Indeed, satellite products obtained in a quasi-systematic way allow to account not only for the loss of surface and volume, but also for the changes in their flow velocities. The overall pattern we document is a glacier thinning and slowdown of the ice flow, with an estimated shrinkage of 25% by 2050 and a volume loss ranging between 30 and 43% depending on the data source used for the estimation of the initial volume. In such a context, a portion of the upper reaches of Brenva Glacier shows an unexpected pattern of thickening and increase in ice flow that rises questions on its origin. Finally, the uncertainties in the estimation of ice thicknesses remain important and have repercussions on the future evolution of the glaciers and their contribution from a hydrological point of view. By 2050, we estimate that the water contribution due to the volume loss of Val Veny glaciers could decrease by 40%.

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]

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]

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]

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]

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.)

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.)

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.

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.