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In Africa, rapid urbanization covers archaeological sites and limits the utility of traditional archaeological field survey methods. This is not only a crisis for archaeological heritage conservation, but it also distinctly impacts anthropological understandings of African urban trajectories since much evidence for precolonial urbanism lies within areas of rapid expansion. However, high-resolution multitemporal satellite data may facilitate reconstructions of urban growth in African cities, enabling archaeological surveys to target undeveloped areas for prospection within the interstices of modern urban development. This paper describes an application of satellite remote sensing for archaeological prospection within the rapidly urbanizing hinterland of Zanzibar Stone Town, a UNESCO World Heritage site. Survey results reveal settlement trajectories around the city over the last millennium, drawing attention to the role of rural agricultural land as a factor in the emergence of precolonial urbanism and the continued significance of rural places as urbanization progressed into the Colonial era. [ABSTRACT FROM AUTHOR]

For the last seven years, PlanetScope satellites have started near-daily imaging of parts of the Earth's surface, making high-density multitemporal, multispectral, 3-m pixel imagery accessible to researchers. Multitemporal satellite data enables landscape archaeologists to examine changes in environmental conditions at time scales ranging from daily to decadal. This kind of temporal resolution can accentuate landscape features on the ground by de-emphasizing non-permanent signatures caused by seasonal or even daily changes in vegetation. We argue that the availability of high spatial and temporal resolution multispectral imagery from Planet Inc. will enable new approaches to studying archaeological visibility in landscapes. While palimpsests are discrete overlapping layers of material accumulation, multitemporal composites capture cyclical and seasonal time and can be used to interpret past landscape histories at multiple scales. To illustrate this perspective, we present three case studies using PlanetScope imagery in tropical environments on the Indian Ocean islands of Madagascar, Mauritius, and Zanzibar. [ABSTRACT FROM AUTHOR]

Satellite remote sensing (SRS) is a technique that can provide effective method on surface water quality assessment at large spatial scale studie. The analysis research involves: (1) analysis of changes in surface water quality in the Vam Co Tay River, Long An province, Vietnam in the period 2015–2020, (2) selection a model to estimate water quality assessment index from remote sensing data based on Bayesian Model Averaging (BMA); and (3) quantitative assessment of surface water pollution risks in the study area. The results show that the predictive coefficients of determination (R2) for water quality (BOD5, COD, and TSS) are higher than 0.70 for all three parameters. In particular, the upstream of Vam Co Tay river with "very high risk" in 2015 tended to decrease to "high risk" in 2020. Besides, the results also show an increasing risk in downstream from "low risk" in 2015 to "moderate risk" in 2020. The study demonstrated the potential of SRS for providing an overall assessment of the spatial distribution of risks associated with surface water pollution and forecasting the concentration changing trends in the future, and supporting to overcome data shortage in water monitoring. [ABSTRACT FROM AUTHOR]

BACKGROUND: Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat‐producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control. RESULTS: Three Sentinel‐2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period‐specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period‐targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model‐group‐result‐based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing‐to‐heading period and the heading‐to‐filling period reached 98.2% and 84.4% respectively. CONCLUSIONS: Our jointing, heading, and filling period‐targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single‐phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In order to solve the issues of obtaining the basic topographic data in the comprehensive management planning and design of small watershed, we established the Qilongwan small watershed digital twin baseplate model, including SAR satellite remote sensing, tilt photography and LiDAR. We extracted the longitudinal section of the main channel, the boundary of the two terrace plots, and the depression degree of the 10 woodland sample areas in the model, and then compared the accuracy test with the manual interpretation or measured results. The results show that the extraction accuracy of lidar as the data source, tilt photography as the second of the data source, and SAR satellite re- mote sensing as the data source. In view of the advantages and disadvantages of the three data source models, the multi-source data fusion can be carried out according to the actual work needs to improve the application effect of digital twin technology in the planning and design of small watershed comprehensive management. In the small watershed of Qilongwan, the shikan terrace plot is selected for SAR satellite remote sensing and tilt photography data fusion, and the popular science exhibition hall is selected for tilt photography and lidar data fusion, which has achieved good results. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and its associated response measures have profoundly impacted both the environment and human life, posing significant challenges to the achievement of the Sustainable Development Goals (SDGs). Several studies have utilized satellite remote sensing to evaluate COVID-19 impacts. In this study, a bibliometric analysis is conducted to reveal the research hotspots limiting to COVID-19 and remote sensing, and further to explore the impacts on SDGs. Results show that the TOP 3 countries of publication amounts are ranked as the United States, China, India. There is a wide range of collaboration in scientific research during this global pandemic, especially in Europe. The publication amounts of research related to SDG 11 are the most, followed by SDG 3, SDG 13, SDG 6, SDG 8, SDG 14, etc. The prevalent topics include the COVID-19 impacts on air quality, water quality, agriculture and food security, climate change, forest ecosystem, and socio-economy. This pandemic brought enormous losses to the socio-economy, which hinders the progress of SDG 8 and SDG 11.5, while had positive or negative effects on goals involving environment and ecosystem, such as SDG 2, SDG 6, SDG 11.6, SDG 13 and SDG 15. Generally, the impacts of COVID-19 on SDGs are comprehensive and systemic, and may depend on the local conditions and management capacity. With satellite remote sensing increasingly vital, global disaster risks can be monitored and managed more effectively to support SDGs in the future. [ABSTRACT FROM AUTHOR]

[Objective] The types of soil and water conservation measures and their configuration modes are complicated. Accurate identification and fine extraction of detailed configuration information of soil and water conservation measures are the basis for obtaining the factor values of soil and water conservation measures. Methods The information acquisition methods of soil and water conservation measures mainly include traditional field surveys, satellite remote sensing images? and UAV close-range photography. The identification and extraction methods mainly include visual interpretation? traditional machine learning? object-oriented classification methods? and deep learning models. By combing the research results of identification and extraction methods of soil and water conservation measures at home and abroad, the existing shortcomings are summarised and the research prospects are put forward. Results］ Tn semantic segmentation, future feature fusion and multimodal learning, weak supervision and semi-supervised learning, integrated learning and meta-learning can be applied to the extraction of soil and water conservation measures. -Conclusion］ At present, there are few reports on the results of identification and extraction of soil and water conservation tillage measures. However > tillage measures a re com mon in agricultural practice, and the research on identification and extraction of tillage measures should be strengthened in the future. Artificial intelligence combined with big data technology is the development direction of efficient and accurate identification and extraction of soil and water conservation measures in the future. Tt is necessary to further study the use of serni-supervised and weakly supervised learning methods. combined with multi-modal learning. small sample labels and other methods to obtain high-quality labeled sample data for soil and water conservation. Extraction of point and linear engineering measures； the combination of deep learning algorithms such as multimodal learning and instance segmentation methods with object-oriented classification methods is applied to the identification and extraction of soil and water conservation plant measures to improve the classification and extraction accuracy of different soil and water conservation plant measures. So as to improve the information extraction method of various soil and water conservation measures, and provide support for accurately obtaining the factor value of soil and water conservation measures and calculating the carbon sink capacity of soil and water conservation. [ABSTRACT FROM AUTHOR]

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation. [ABSTRACT FROM AUTHOR]

This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) share the same optics and, thus, cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors. [ABSTRACT FROM AUTHOR]

Estimation of anthropogenic carbon dioxide (CO2) emission sources and natural sinks (i.e., CO2 fluxes) is essential for the development of climate policies. Satellite observations provide an opportunity for top-down inversion of CO2 fluxes, which can be used to improve the results of bottom-up estimation. This study proposes to develop a new top-down CO2 flux estimation method based on deep learning, as well as satellite observations, and an atmospheric chemical transport model. This method utilizes two deep learning models: the concentration correction model and the concentration–flux inversion model. The former optimizes the GEOS-Chem-simulated CO2 concentration using Orbiting Carbon Observatory-2 (OCO-2) satellite observations, while the latter establishes the complicated relationship between CO2 concentration and CO2 flux. Results showed that both deep learning models demonstrated excellent prediction performance, with a mean bias of 0.461 ppm for the concentration correction model and an annual mean correlation coefficient of 0.920 for the concentration–flux inversion model. A posterior CO2 flux was obtained through a two-step optimization process using these well-trained models. Our findings indicate that the posterior estimations of CO2 flux sources in eastern China and northern Europe have been significantly reduced compared to the prior estimations. This study provides a new perspective on top-down CO2 flux inversion using satellite observation. With advancements in deep learning algorithms and increased satellite observations, this method may become an effective approach for CO2 flux inversion in the future. [ABSTRACT FROM AUTHOR]

Fire significantly contributes to greenhouse gas emissions. The current global burned area (BA) products mainly have coarse native spatial resolution, which leads to underestimation of global BA and carbon emissions from biomass burning. Performances of BA products in Africa from GABAM (30 m), MCD64A1 (500 m), GFED4s (0.25°), FireCCI51 (250 m), and GFED5 (0.25°) were compared. From 2014 to 2020, GFED5 detected the most BA, 1.58 times more than GABAM during the same period. GABAM detected 0.09 Mkm2 more burned area than FireCCI51 on average. From 2014 to 2016, GABAM detected an average of 2.99 Mkm2 of BA in Africa, which was 1.03 times more than GFED4s. From 2014 to 2021, the average African BA derived from GABAM was 2.89 Mkm2, 1.22 times more than MCD64A1. The increase in BA will inevitably lead to an increase in the estimation of carbon emissions from biomass burning. Based on GABAM products and GFED framework, we estimated the average vegetation burning carbon emissions in Africa from 2014 to 2021 to be 1113.25 Tg, which is higher than GFED4s' carbon emissions in the same time period. This shows that the use of high‐resolution (30 m) burned area products to estimate carbon emissions can effectively avoid the underestimation of overall fire carbon emissions. Plain Language Summary: Biomass burning is a significant contributor to climate change, and Africa is a hotspot for fire activity. Africa accounts for around 70% of the global burned area (BA) and 50% of the global carbon emissions from biomass burning. However, current coarse native spatial resolution burned area products over Africa underestimate the true burning situation in Africa and mask the full impact of African biomass burning on global greenhouse gases and aerosols. To address this issue, we employ novel high spatial resolution (30 m) BA products of Africa from 2014 to 2021, which markedly enhance the identification of burned area and estimation of biomass burning carbon emissions. This study yields novel data and research conclusions that contribute to a better understanding of burned area and fire‐induced carbon emissions in Africa. Key Points: Carbon emissions from biomass combustion in Africa during 2014–2021 are estimated using 30 m BA products30 m BA products enhance small fire detection and boundary delineationAfrica still needs higher spatial and temporal resolution burned area products [ABSTRACT FROM AUTHOR]

The use of satellite‐based thermal infrared remote sensing has facilitated the assessment of surface water temperature on a large scale. However, the inherent limitations of this remote sensing technique make it difficult to assess rivers unless ambient conditions are cloud‐free, devoid of steep terrain and the rivers are at least 60 m wide. To address these challenges that limit the spatiotemporal continuity of satellite‐based hydro‐thermal data, we harnessed the extensive coverage from the Landsat missions' thermal infrared sensors and data‐driven techniques to estimate surface water temperature of rivers. Out of the tested data‐driven techniques, we selected the Random Forest Regressor as our prime non‐linear approach for estimation of surface water temperature in rivers. Using the selected technique, proposed as THORR (Thermal History of Regulated Rivers), we successfully reconstructed a multi‐decadal, continuous spatiotemporal surface water temperature record for regulated rivers in the Columbia River Basin. Using 42 years of data, the surface water temperature could be predicted on average with 0.71° C of absolute error regardless of the dam's potential thermal influence in the downstream reaches. The reconstructed hydro‐thermal behavior generated from THORR revealed a long‐term downstream warming trend along the Columbia River. The open‐source THORR tool can be extended to any river system around the world that is not gauged with in‐situ temperature measurements for the reconstruction of hydro‐thermal behavior. Plain Language Summary: Surface water temperature of rivers, which is an important ecological parameter for more robust water management, can be estimated in a cost‐effective and globally scalable way from satellite thermal infrared (TIR) remote sensing. However, such a technique is limited to rivers of sufficient width and cloud‐free conditions. In this study, data‐driven techniques were explored to overcome this limitation and reconstruct surface water temperature of rivers in the continuum of space and time. By using a comprehensive record spanning 42 years of satellite remote sensing, we demonstrate that it is possible to recreate robust estimates of long spatiotemporal trends of river temperature to understand how surface waters are being altered thermally due to water management and climate change. Key Points: Thermal Infrared remote sensing from Landsat was used to estimate the surface water temperature of the highly regulated Columbia River BasinUsing 42 years of data, the surface water temperature could be predicted on average with 0.7°C of absolute error regardless of the dam's potential thermal influenceThe reconstructed hydro‐thermal behavior indicated a long‐term downstream warming trend along the Columbia River [ABSTRACT FROM AUTHOR]

Abstract The COVID-19 pandemic and its associated response measures have profoundly impacted both the environment and human life, posing significant challenges to the achievement of the Sustainable Development Goals (SDGs). Several studies have utilized satellite remote sensing to evaluate COVID-19 impacts. In this study, a bibliometric analysis is conducted to reveal the research hotspots limiting to COVID-19 and remote sensing, and further to explore the impacts on SDGs. Results show that the TOP 3 countries of publication amounts are ranked as the United States, China, India. There is a wide range of collaboration in scientific research during this global pandemic, especially in Europe. The publication amounts of research related to SDG 11 are the most, followed by SDG 3, SDG 13, SDG 6, SDG 8, SDG 14, etc. The prevalent topics include the COVID-19 impacts on air quality, water quality, agriculture and food security, climate change, forest ecosystem, and socio-economy. This pandemic brought enormous losses to the socio-economy, which hinders the progress of SDG 8 and SDG 11.5, while had positive or negative effects on goals involving environment and ecosystem, such as SDG 2, SDG 6, SDG 11.6, SDG 13 and SDG 15. Generally, the impacts of COVID-19 on SDGs are comprehensive and systemic, and may depend on the local conditions and management capacity. With satellite remote sensing increasingly vital, global disaster risks can be monitored and managed more effectively to support SDGs in the future.

This paper presents a web-based software tool designed to process, analyze, and visualize satellite laser altimetry data, specifically from the Ice, Cloud, and land Elevation Satellite (ICESat) mission, which collected data from 2003 to 2009, and ICESat-2, which was launched in 2018 and is currently operational. These data are crucial for studying and understanding changes in Earth’s surface and cryosphere, offering unprecedented accuracy in quantifying such changes. The software tool ICEComb provides the capability to access the available data from both missions, interactively visualize it on a geographic map, locally store the data records, and process, analyze, and explore the data in a detailed, meaningful, and efficient manner. This creates a user-friendly online platform for the analysis, exploration, and interpretation of satellite laser altimetry data. ICEComb was developed using well-known and well-documented technologies, simplifying the addition of new functionalities and extending its applicability to support data from different satellite laser altimetry missions. The tool’s use is illustrated throughout the text by its application to ICESat and ICESat-2 laser altimetry measurements over the Mirim Lagoon region in southern Brazil and Uruguay, which is part of the world’s largest complex of shallow-water coastal lagoons.

Grassland ecosystems play a critical role in global carbon cycling and environmental health. Understanding the intricate link between grassland vegetation traits and underlying soil properties is crucial for effective ecosystem monitoring and management. This review paper examines advancements in utilizing Radiative Transfer Models (RTMs) and hyperspectral remote sensing to bridge this knowledge gap. We explore the potential of vegetation spectra as an integrated measure of soil characteristics, acknowledging the value of other remote sensing sources. Our focus is on studies leveraging hyperspectral data from proximal and airborne sensors, while discussing the impact of spatial scale on trait retrieval accuracy. Finally, we explore how advancements in global satellite remote sensing contribute to vegetation trait detection. This review concludes by identifying current challenges, outlining future research directions, and highlighting opportunities for improved understanding of the vegetation-soil property interaction. [ABSTRACT FROM AUTHOR]

Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides due to its wide coverage and instantaneous imaging capabilities. Additionally, drift prediction techniques can forecast the location of future green tides based on remote sensing monitoring information. This monitoring and prediction information is crucial for developing an effective plan to intercept and remove green tides. One key aspect of this monitoring information is the green tide distribution envelope, which can be generated automatically and quickly using buffer analysis methods. However, this method produces a large number of envelope vertices, resulting in significant computational burden during prediction calculations. To address this issue, this paper proposes a simplification method based on azimuth difference and side length (SM-ADSL). Compared to the isometric and Douglas–Peucker methods with the same simplification rate, SM-ADSL exhibits better performance in preserving shape and area. The simplified distribution envelope can shorten prediction times and enhance the efficiency of emergency decision-making for green tide disasters. [ABSTRACT FROM AUTHOR]

With the rapid advancement of satellite remote sensing technology, many scientists and organizations, including NASA, ESA, NAOC, and Roscosmos, observe and study significant changes in the geomagnetic field, which has greatly promoted research on the geomagnetic field and made it an important research direction in Earth system science. In traditional geomagnetic field research, tesseroid cells face degradation issues in high-latitude regions and accuracy limitations. To overcome these limitations, this paper introduces the Discrete Global Grid System (DGGS) to construct a geophysical model, achieving seamless global coverage through multi-level grid subdivision, significantly enhancing the processing capability of multi-source and multi-temporal spatial data. Addressing the challenges of the lack of analytical solutions and clear integration limits for DGGS cells, a method for constructing shape functions of arbitrary isoparametric elements is proposed based on the principle of isoparametric transformation, and the shape functions of isoparametric DGGS cells are successfully derived. In magnetic vector forwarding, considering the potential error amplification caused by Poisson's formula, the DGGS grid is divided into six regular triangular sub-units. The triangular superconvergent point technique is adopted, and the positions of integration points and their weight coefficients are accurately determined according to symmetry rules, thereby significantly improving the calculation accuracy without increasing the computational complexity. Finally, through the forward modeling algorithm based on tiny tesseroid cells, this study comprehensively compares and analyzes the computational accuracy of the DGGS-based magnetic vector forwarding algorithm, verifying the effectiveness and superiority of the proposed method and providing new theoretical support and technical means for geophysical research. [ABSTRACT FROM AUTHOR]

With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized "top-down" approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO2. It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth's carbon-cycle processes. [ABSTRACT FROM AUTHOR]

War-related urban destruction is a significant global concern, impacting national security, social stability, people's survival and economic development. The effects of urban geomorphology and complex geological contexts during conflicts, characterized by different levels of structural damage, are not yet fully understood globally. Here we report how integrating deep learning with data from the independently developed LuoJia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel–Palestine conflict. We found that the damage continually increased from 17 October 2023 to 2 March 2024. We found 3747 missile craters with precision positions and sizes, and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024, providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination. Our findings reveal a significant increase in damage to residential and educational structures, accounting for 58.4% of the total—15.4% destroyed, 18.7% severely damaged, 11.8% moderately damaged and 12.5% slightly damaged—which exacerbates the housing crisis and potential population displacement. Additionally, there is a 34.1% decline in the cultivated area of agricultural land, posing a risk to food security. The LuoJia3-01 satellite data are crucial for impartial conflict monitoring, and our innovative methodology offers a cost-effective, scalable approach to assess future conflicts in various global contexts. These first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts. [ABSTRACT FROM AUTHOR]

This paper presents a web-based software tool designed to process, analyze, and visualize satellite laser altimetry data, specifically from the Ice, Cloud, and land Elevation Satellite (ICESat) mission, which collected data from 2003 to 2009, and ICESat-2, which was launched in 2018 and is currently operational. These data are crucial for studying and understanding changes in Earth's surface and cryosphere, offering unprecedented accuracy in quantifying such changes. The software tool ICEComb provides the capability to access the available data from both missions, interactively visualize it on a geographic map, locally store the data records, and process, analyze, and explore the data in a detailed, meaningful, and efficient manner. This creates a user-friendly online platform for the analysis, exploration, and interpretation of satellite laser altimetry data. ICEComb was developed using well-known and well-documented technologies, simplifying the addition of new functionalities and extending its applicability to support data from different satellite laser altimetry missions. The tool's use is illustrated throughout the text by its application to ICESat and ICESat-2 laser altimetry measurements over the Mirim Lagoon region in southern Brazil and Uruguay, which is part of the world's largest complex of shallow-water coastal lagoons. [ABSTRACT FROM AUTHOR]

Owing to rapid urbanization, the Beijing–Tianjin–Hebei region in China faces considerable urban heat island (UHI) effects, which can be mitigated by blue-green space construction. In this study, we used multi-source remote sensing products and the InVEST model's urban cooling module to analyze the spatiotemporal changes in blue-green space cooling effects from 1990 to 2020. The wavelet coherence theory was used to explore these changes, as well as the environmental factors that affect cooling. The key findings indicate that the cooling effect is closely related to urbanization, as similar trends and significant temporal differences in cooling indices were observed in central urban areas, the urban fringe, and the city center. In addition, climatic factors such as temperature and precipitation substantially influenced cooling, with an average wavelet coherence of 0.88. Seasonal variations in cooling were notable, with temperature exhibiting the best coherence across all time–frequency scales (averaging 0.55). The findings highlight the critical role of blue-green spaces for mitigating UHI effects, which provides scientific insights for urban planning and environmental management. [ABSTRACT FROM AUTHOR]

Antarctica has an active subglacial hydrological system, with interconnected subglacial lakes fed by subglacial meltwater. Subglacial hydrology can influence basal sliding, inject freshwater into the sub‐ice‐shelf cavity, and impact sediment transport and deposition which can affect the stability of grounding lines (GLs). We used satellite altimetry data from the ICESat, ICESat‐2, and CryoSat‐2 missions to document the second recorded drainage of Engelhardt Subglacial Lake (SLE), which began in July 2021 and discharged more than 2.3 km3 of subglacial water into the Ross Ice Shelf cavity. We used differential synthetic aperture radar interferometry from RADARSAT‐2 and TerraSAR‐X alongside ICESat‐2 repeat‐track laser altimetry (RTLA) and REMA digital elevation model strips to detect 2–13 km of GL retreat since the previous drainage event in 2003–06. Combining these satellite observations, we evaluated the mechanism triggering SLE drainage, the cause of the observed GL retreat, and the interplay between subglacial hydrology and GL dynamics. We find that: (a) SLE drainage was initiated by influx from a newly identified upstream lake; (b) the observed GL retreat is mainly driven by the continued retreat of Engelhardt Ice Ridge and long‐term dynamic thinning that caused a grounded ice plain to reach flotation; and (c) SLE drainage and GL retreat were largely independent. We also discuss the possible origins and influence of a 27 km grounded promontory found to protrude seaward from the GL. Our observations demonstrate the importance of high‐resolution satellite data for improving the process‐based understanding of dynamic and complex regions around the Antarctic Ice Sheet margins. Plain Language Summary: Large volumes of water flow beneath the Antarctic Ice Sheet through an interconnected network of rivers and lakes. This water system impacts slipperiness at the base of the ice, affecting how fast it moves. It also delivers freshwater into the ocean, directly contributing to sea‐level rise and increasing melt beneath the floating ice shelves. In this study, we use satellite data to track the 2021–24 drainage of Engelhardt Subglacial Lake in West Antarctica. This lake is located close to the Ross Ice Shelf grounding line, the point at the edge of the ice sheet where the ice first lifts off the bedrock and starts to float on the ocean. This region of the grounding line has retreated by up to 13 km since the last lake drainage in 2003–06. Here, we investigate what caused the drainage, the reasons for the grounding line retreat, and whether the two processes are connected. We also report the growth of a grounded promontory extending 27 km out to sea, which may be evidence of a former ice stream moraine or melt channel. These findings help to improve our limited understanding of the relationship between subglacial hydrology and grounding line dynamics in Antarctica. Key Points: Satellite altimetry detects second recorded drainage of Engelhardt Subglacial Lake in July 2021, triggered by an influx from an upstream lakeThe grounding line retreated by 2–13 km since the last drainage in 2003, linked to the retreat of Engelhardt Ice Ridge and ice plain ungroundingSatellite observations suggest that the 2021–24 lake drainage and grounding line retreat were largely independent processes [ABSTRACT FROM AUTHOR]

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Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources 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.)

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semiautomated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Nd sat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period from March to May 2020, shows that droplet closure can be achieved to within ±33.4%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km Ã--4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies. [ABSTRACT FROM AUTHOR]

The article presents the correction for a article titled "Towards reliable retrievals of cloud droplet number for non-precipitating planetary boundary layer clouds and their susceptibility to aerosol" published on August 12, 2024.

As a signature phenomenon of massive hypervelocity impacts (HVIs) in space, debris clouds provide critical optical information for satellite remote sensing and the assessment of large-scale impacts. However, studies of the optical scattering properties of debris clouds remain limited, and existing vector radiative transfer (VRT) methods struggle to accurately simulate the optical characteristics of these complex scatterers. To address this gap, this paper presents an improved Monte Carlo VRT program (PGS–MC) for multicomponent polydisperse scatterers to precisely evaluate the radiation and polarization characteristics of complex scatterers. Based on the Monte Carlo ray tracing (MCRT) method, our program introduces a particle grouping strategy (PGS) to further emphasize the importance of accounting for optical property discrepancies between different materials and particle sizes, thus significantly improving the fidelity of VRT simulations. Moreover, our program, developed using the compute unified device architecture (CUDA), can be run parallelly on graphics processing units (GPUs), which effectively reduces the computational time. The validation results indicated that the developed PGS–MC program can accurately and efficiently simulate the polarization of complex 3D scatterers. A further investigation showed that the polarization characteristics of debris clouds are highly sensitive to parameters such as the angle between the incident and detection directions, number density, particle size distribution, debris material, and wavelength. In addition, the polarization imaging of debris clouds offers distinct advantages over intensity imaging. This study offers guidance for analyzing the VRT properties of massive HVI debris clouds. Additionally, it provides a practical tool and concrete ideas for modeling the polarization characteristics of various complex scatterers, such as aircraft contrails and clouds, etc. [ABSTRACT FROM AUTHOR]

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. [ABSTRACT FROM AUTHOR]

An independent evaluation of methane emissions data from GHGSat, a private company that operates a constellation of small microsatellites flying Fabry‐Perot spectrometers operating at 1.6 µm, was performed. Data from multiple GHGSat commercial satellites, consisting of retrieved methane, diagnostics, and, where detected, plume and emissions information from roughly 250 scenes across Canada were analyzed. From these, 10 scenes contained methane plumes with a 2% detection rate for oil and gas scenes, and 10% for landfills. Methane precision was found to be 5%/2% on average for the C1/C2–C5 designs, with some variability due to scene albedo, terrain roughness, and airmass. Synthetic GHGSat plumes, generated using Lagrangian plume dispersion model and GHGSat characteristics, indicates typical detection limits of 240/180 kg/hr(C1/C2–C5), with a best case of roughly 100 kg/hr. Emissions and their uncertainties calculated using an alternative approach were in broad agreement with GHGSat‐reported emissions. Overall, the performance of the GHGSat C2 design (also used for C3 onward) for favorable‐viewing conditions was found to be largely consistent with company‐advertised performance. Plain Language Summary: We performed an independent evaluation of methane emissions data from GHGSat, a private company that owns and operates its own group of satellites, in order to evaluate the precision of their methane data products and compare these findings with that advertised. Roughly 250 GHGSat data scenes were acquired with 10 plumes detected, 2 for oil and gas and 8 for landfills. These, combined with advanced plume models, statistical analyses, and alternative data processing software, were used in the evaluation. It was found that the GHGSat‐reported detection limit—the quantity of methane release that was is the edge of detectability—of 100 kg/hr was possible, but only for ideal conditions. Ideal in this case means a surface that is quite reflective (at the wavelengths detected by the GHGSat sensors, roughly 1.6 μm) and flat near the source of the emissions. A detection limit of 180 kg/hr is more representative of average conditions. Key Points: GHGSat methane precision was found to be 1%–5%, varying with surface reflectance, terrain variability, and solar zenith angleGHGSat methane detection limit can be as low as 100 kg/hr under ideal conditions, but more typically is about 200 kg/hrGHGSat performance was found to be generally consistent with that reported by the company [ABSTRACT FROM AUTHOR]

Long-term, accurate, stable, and continuous aerosol records from space are a major requirement for climate and atmospheric environment research. Due to the limited period span of the single satellite mission, solving the problem requires the combination of multiple satellite missions. In this study, a novel aerosol retrieval algorithm, named enhanced Land General Aerosol Retrieval (e-LaGA), for Visible/Infrared Imager Radiometer Suite (VIIRS) was developed to continue Moderate-resolution Imaging Spectro-radiometer (MODIS) aerosol retrieval. e-LaGA utilizes a Surface Reflectance (SR) relationship model map to more accurately estimate the SR parameter, optimizes the priori aerosol-type map, and improves the multi-band retrieval strategy using the residual-interpolation approach. Additionally, e-LaGA expands the retrieval ability of the traditional Dark Target (DT) algorithm over bright surfaces, and can more accurately retrieve Aerosol Optical Depth (AOD), Fine-mode AOD (AODF), and Fraction (FMF). The validation using global 533 AERONET sites shows that the volume of matchups of AOD (AODF) is approximately 80000, the correlation coefficient (R) is 0.902 (0.879) and the fraction of meeting the expected error envelope (±0.05 ± 0.15τ) is 0.806 (0.804). The inter-comparison indicates that the accuracy of e-LaGA AOD retrievals is comparable to seven commonly used AOD products. The validation performance and spatial distribution pattern of e-LaGA AODF and FMF are in good agreement with the POLDER (Polarization and Directionality of the Earth's Reflectances) products. e-LaGA is also applied to MODIS sensors. The VIIRS e-LaGA AOD, AODF, and FMF retrievals have high consistency with MODIS e-LaGA. Their average bias of AOD is 0.006, which is smaller than 0.024 for the Deep Blue and 0.011 for the DT. These preliminary results demonstrate the robustness of the e-LaGA algorithm and its potential for establishing a long-term climate record of total and fine-mode aerosol by combining multiple satellite missions, which is expected to reduce the uncertainty of climate change research. [ABSTRACT FROM AUTHOR]

Vegetation canopy water content (CWC) crucially affects stomatal conductance and photosynthesis and, consequently, is a key state variable in advanced ecosystem models. Remote sensing has been shown to be an effective tool for retrieving CWCs. However, the retrieval of the CWC from satellite remote sensing data is affected by the vegetation canopy structure and soil background. This study proposes a methodology that combines a modified spectral down-scaling model with a high-universality leaf water content inversion model to retrieve the CWC through constraining the impacts of canopy structure and soil background on CWC retrieval. First, canopy spectra acquired by satellite sensors were down-scaled to leaf reflectance spectra according to the probabilities of viewing the sunlit foliage (PT) and background (PG) and the estimated spectral multiple scattering factor (M). Then, leaf water content, or equivalent water thickness (EWT), was obtained from the down-scaled leaf reflectance spectra via a leaf-scale EWT inversion model calibrated with PROSPECT simulation data. Finally, the CWC was calculated as the product of the estimated leaf EWT and canopy leaf area index. Validation of this coupled model was performed using satellite-ground synchronous observation data across various vegetation types within the study area, affirming the model's broad applicability. Results indicate that the modified spectral down-scaling model accurately retrieves leaf reflectance spectra, aligning closely with site-level measured spectra. Compared to the direct inversion approach, which performs poorly with Hyperion satellite images, the down-scale strategy notably excels. Specifically, the Similarity Water Index (SWI)-based canopy EWT coupled model achieved the most precise estimation, with a normalized Root Mean Square Error (nRMSE) of 15.28% and an adjusted R2 of 0.77, surpassing the performance of the best index Shortwave Angle Normalized Index (SANI)-based model (nRMSE = 15.61%, adjusted R2 = 0.52). Given its calibration using simulated data, this coupled model proved to be a potent method for extracting canopy EWT from satellite imagery, suggesting its applicability to retrieve other vegetative biochemical components from satellite data. [ABSTRACT FROM AUTHOR]

Summary: Leaf phenology variations within plant communities shape community assemblages and influence ecosystem properties and services. However, questions remain regarding quantification, drivers, and productivity impacts of intra‐site leaf phenological diversity.With a 50‐ha subtropical forest plot in China's Heishiding Provincial Nature Reserve (part of the global ForestGEO network) as a testbed, we gathered a unique dataset combining ground‐derived abiotic (topography, soil) and biotic (taxonomic diversity, functional diversity, functional traits) factors. We investigated drivers underlying leaf phenological diversity extracted from high‐resolution PlanetScope data, and its influence on aboveground biomass (AGB) using structural equation modeling (SEM).Our results reveal considerable fine‐scale leaf phenological diversity across the subtropical forest landscape. This diversity is directly and indirectly influenced by abiotic and biotic factors (e.g. slope, soil, traits, taxonomic diversity; r2 = 0.43). While a notable bivariate relationship between AGB and leaf phenological diversity was identified (r = −0.24, P < 0.05), this relationship did not hold in SEM analysis after considering interactions with other biotic and abiotic factors (P > 0.05).These findings unveil the underlying mechanism regulating intra‐site leaf phenological diversity. While leaf phenology is known to be associated with ecosystem properties, our findings confirm that AGB is primarily influenced by functional trait composition and taxonomic diversity rather than leaf phenological diversity. [ABSTRACT FROM AUTHOR]

Gravity wave (GW) momentum flux spectra help to uncover the mechanisms through which GWs influence momentum transfer in the atmosphere and provide crucial insights into accurately characterizing atmospheric wave processes. This study examines the momentum flux spectra of GWs in the troposphere (2–14 km) and stratosphere (18–28 km) over Koror Island (7.2°N, 134.3°W) using radiosonde data from 2013–2018. Utilizing hodograph analysis and spectral methods, the characteristics of momentum flux spectra are discussed. Given that the zonal momentum flux spectra of low-level atmospheric GWs generally follow a Gaussian distribution, Gaussian fitting was applied to the spectral structures. This fitting further explores the seasonal variations of the zonal momentum flux spectra and the average spectral parameters for each month. Additionally, the GW energy is analyzed using SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) satellite data and compared with the results of the momentum flux spectra from radiosonde data, revealing the close negative correlation between wave energy and wave momentum for stratospheric GW changing with time. The findings indicate that the Gaussian peak shifts more eastward in both the troposphere and stratosphere, primarily due to the absorption of eastward-propagating GWs by the winter tropospheric westerly jet and critical layer filtering. The full width at half maximum (FWHM) in the stratosphere is larger than in the troposphere, especially in June and July, as the spectrum broadens due to propagation effects, filtering, and interactions among waves. The central phase speed in the stratosphere exceeds that in the troposphere, reflecting the influences of Doppler effects and background wind absorption. The momentum flux in the stratosphere is lower than in the troposphere, which is attributed to jet absorption, partial reflection, or the dissipation of GWs. [ABSTRACT FROM AUTHOR]

We present an analysis of Antarctic polar winters from 2005 to 2023 as observed by the Atmospheric Chemistry Experiment (ACE). The unique broad band infrared spectral features in ACE "residual" spectra are used to classify the spectra of polar aerosols by composition into polar stratospheric clouds (PSCs) and sulfate aerosols. The spectra of PSCs are further classified into nitric acid trihydrate, supercooled ternary solutions, supercooled nitric acid, ice‐mix, and mixtures of PSCs. A breakdown of PSC composition is presented for each year. Antarctic winter seasons with unusual compositions are: 2011, in which volcanic ash mixed with PSCs was observed from July to August; 2019, which experienced a stratospheric warming event; 2020, the PSC season following the Australian Black Summer pyrocumulonimbus event; and 2023, which had unusually large sulfate aerosols following the Honga‐Tonga Honga Ha'apai eruption of 2022. Plain Language Summary: Polar Stratospheric Clouds (PSCs) are clouds that form during polar night when temperatures in the stratosphere drop to extreme lows. These clouds play a crucial role in the annual depletion of ozone (the Antarctic "ozone hole"). We present a summary of PSC observations made from the Atmospheric Chemistry Experiment (ACE), which is a satellite‐based mission that has been operational since 2004. Key Points: Antarctic polar stratospheric cloud composition distribution during August and September observed by the Atmospheric Chemistry Experiment (ACE)Polar stratospheric cloud (PSC) composition impacted by large scale stratospheric events such as Australian Black Summer pyrocumulonimbus event and volcanic eruptionsPSC composition distribution is mostly governed by temperature [ABSTRACT FROM AUTHOR]

Water scarcity in arid regions poses significant livelihood challenges and necessitates proactive measures such as rainwater harvesting (RWH) systems. This study focuses on identifying RWH sites in Dera Ghazi Khan (DG Khan) district, which recently experienced severe water shortages. Given the difficulty of large-scale ground surveys, satellite remote sensing data and Geographic Information System (GIS) techniques were utilized. The Analytic Hierarchy Process (AHP) approach was employed for site selection, considering various criteria, including land use/land cover, precipitation, geological features, slope, and drainage. Landsat 8 OLI imagery, GPM satellite precipitation data, soil maps, and SRTM DEM were key inputs. Integrating these data layers in GIS facilitated the production of an RWH potential map for the region. The study identified 9 RWH check dams, 12 farm ponds, and 17 percolation tanks as suitable for mitigating water scarcity, particularly for irrigation and livestock consumption during dry periods. The research region was classified into four RWH zones based on suitability, with 9% deemed Very Good, 33% Good, 53% Poor, and 5% Very Poor for RWH projects. The generated suitability map is a valuable tool for hydrologists, decision-makers, and stakeholders in identifying RWH potential in arid regions, thereby ensuring water reliability, efficiency, and socio-economic considerations. [ABSTRACT FROM AUTHOR]

As recent geopolitical conflicts and climate change escalate, the effects of war on the atmosphere remain uncertain, in particular in the context of the recent large-scale war between Russia and Ukraine. We use satellite remote sensing techniques to establish the effects that reduced human activities in urban centers of Ukraine (Kharkiv, Donetsk, and Mariupol) have on Land Surface Temperatures (LST), Urban Heat Islands (UHI), emissions, and nighttime light. A variety of climate indicators, such as hot spots, changes in the intensity and area of the UHI, and changes in LST thresholds during 2022, are differentiated with pre-war conditions as a reference period (i.e., 2012–2022). Findings show that nighttime hot spots in 2022 for all three cities cover a smaller area than during the reference period, with a maximum decrease of 3.9% recorded for Donetsk. The largest areal decrease of nighttime UHI is recorded for Kharkiv (− 12.86%). Our results for air quality changes show a significant decrease in carbon monoxide (− 2.7%, based on the average for the three cities investigated) and an increase in Absorbing Aerosol Index (27.2%, based on the average for the three cities investigated) during the war (2022), compared to the years before the war (2019–2021). The 27.2% reduction in nighttime urban light during the first year of the war, compared to the years before the war, provides another measure of conflict-impact in the socio-economic urban environment. This study demonstrates the innovative application of satellite remote sensing to provide unique insights into the local-scale atmospheric consequences of human-related disasters, such as war. The use of high-resolution satellite data allows for the detection of subtle changes in urban climates and air quality, which are crucial for understanding the broader environmental impacts of geopolitical conflicts. Our approach not only enhances the understanding of war-related impacts on urban environments but also underscores the importance of continuous monitoring and assessment to inform policy and mitigation strategies. [ABSTRACT FROM AUTHOR]

The regional differences in carbon dioxide (CO2) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO2 annual growth rate was about 2.63 ppm year−1, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO2 concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO2 concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO2 concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R2) > 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO2 concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO2 concentration (like the southeast of Jing-Jin-Jin). [ABSTRACT FROM AUTHOR]

The quantity and quality of surface water are inherently connected yet are overwhelmingly studied separately in the field of remote sensing. Remotely observable water quantity (e.g., water extent, water elevation, lake/reservoir volume, and river discharge) and water quality (e.g., color, turbidity, total suspended solids, chlorophyll a, colored dissolved organic matter, and temperature) parameters of inland waterbodies interact through a series of hydrological and biogeochemical processes. In this review, we analyzed trends in remote sensing publications to understand the prevalence of studies on the quantity versus quality of open‐surface inland waterbodies (rivers, streams, lakes, and reservoirs) as well as identified opportunities for integrating both water quality and quantity sensing in future work. Our bibliometric analysis found that despite the increasing number of publications using remote sensing for inland waterbodies, few studies to date have used remote sensing tools or approaches to simultaneously study water quantity and quality. Ultimately, by providing insights into potential integration of the water quality and quantity studies, we aim to identify a pathway to advance the understanding of inland water dynamics and freshwater resources through remote sensing. This article is categorized under:Water and Life > MethodsScience of Water > Water QualityWater and Life > Nature of Freshwater EcosystemsScience of Water > Methods [ABSTRACT FROM AUTHOR]

Remotely sensed solar-induced chlorophyll fluorescence "SIF" is applicable as an indicator of changing photosynthetic activity in terrestrial ecosystems. The vegetation of Borneo has previously been affected by drought and fire during El Niño events. Changes in satellite-based SIF data during an El Niño event in Borneo in 2015 were examined using three satellites-GOSAT, GOME-2, and OCO-2-covering the whole island and its southern and northern areas, respectively. Relationships between environmental factors and vegetation damage, precipitation, fire incidence, vegetation indices, and gross primary production "GPP", which were determined using machine-learning methods, were also examined for the period 2007-2018. SIF tended to be low in dry seasons, even in normal years, possibly because of increased drought stress and / or a higher incidence of fires with less precipitation. During the dry season of 2015, there were significant reductions in SIF in southern Borneo where fires were frequent. Other vegetation indices and GPP were also lower. Serious drought conditions with frequent fires during the El Niño event might have caused ecological degradation throughout Borneo, with a significant decrease in SIF. [ABSTRACT FROM AUTHOR]

With a growing global population and intensifying regional conflicts, the need for food is more urgent than ever. Rice, as one of the world's major staple crops especially in Asia, sustains over 50 percent of the global population. Accurate rice mapping is fundamental to ensuring global food security and sustainable agricultural development. Remote sensing has become an essential tool for mapping rice cultivation due to its ability to cover large areas and provide timely observation. Existing reviews mainly focus on the paddy rice mapping methods. However, it lacks a comprehensive understanding on the quality of different paddy rice maps from regional to global scales. This paper provides a comprehensive review of existing satellite-based rice mapping methods and products. Firstly, we categorized all previous methods into four classes: 1) spatial statistical method; 2) traditional machine learning method; 3) phenology-based method; and 4) deep learning method. Secondly, we summarized 25 products, including 3 global products and 22 regional products. Furthermore, we examined the consistency and discrepancy among different products in China, Heilongjiang China and Vietnam respectively and explored the underlying reasons. We found that 1) rice fields with simple cropping patterns and intensive cultivation can be correctly recognized using various algorithms; 2) different products share low consistency in fragmented rice fields 3) the prevalence of clouds and complicated rice cropping patterns or diverse growing environments in subtropical and tropical regions poses challenges to accurate rice mapping. Due to these challenges, currently it still lacks paddy rice maps with both large spatial coverage, high spatial resolution, and long time series. Moreover, deficiency of ground-truth samples impedes product development and validation. For improved paddy rice mapping at large scale, we suggest to apply sample-free rice mapping techniques and remote sensing foundation models to leverage the strengths of phenology-based methods and deep learning methods.

The complex topography in mountainous regions, exemplified by factors like slope aspect, leads to noticeable variations in vegetation patterns, which are fundamental for understanding mountain ecosystems. However, a consistent global-scale quantification of topography's influence on vegetation patterns is still lacking. Here, we utilize two phenological metrics as proxies for vegetation-maximum vegetation greenness and seasonal greenness amplitude-computed from Sentinel-2 images, to quantify differences across three topographic factors: slope aspect, steepness, and elevation within each 0.15°×0.15° mountain grid. Our mapping reveals clear geographic patterns indicating that topography strongly influences vegetation in arid and polar ecosystems, with an influence approximately 1.9 times higher than in temperate ecosystems. Topography is also important in humid regions, as demonstrated by diverse vegetation types growing on different slope aspects, steepness levels, and elevations. Additionally, the impacts of slope aspect, steepness, and elevation vary within the same region. In 25.9% of mountain grids, slope aspect causes the largest difference in vegetation patterns, while elevation and steepness account for 43.1% and 31%, respectively. Our study highlights the hotspot areas where topographic effects on vegetation patterns are most pronounced, enabling researchers to focus on these regions for better parameterization of Earth system models.

This literature review discusses specific challenges associated with drought risk assessments (DRA) within agricultural systems, particularly concerning the justification for indicator selection, aggregation methods, and DRA-informed adaptation strategies. Employing a multifaceted approach combining quantitative and qualitative methods, we systematically reviewed existing DRA literature. The PRISMA methodology and bibliometric analysis were employed to quantitatively reveal trends, key contributors, and publication patterns in DRA research, offering insights into the research evolution in the field. Simultaneously, the qualitative aspect of this review involved an examination of selected papers to identify critical gaps. Our analysis reveals that 67% of the studies lack validation for their research methodology, underscoring the need for rigorous validation of DRA results to enhance research credibility among decision-makers. Additionally, a significant 88% of the reviewed papers primarily focus on risk identification, with less emphasis on adaptation strategies derived from DRA results. Therefore, we emphasize the imperative of bridging the gap between DRA research and practical applications, advocating for linking drought risk findings to adaptation strategies. This targeted approach allows for the identification of sector-specific indicators, methodologies, and adaptation strategies to address the unique vulnerabilities of agriculture, ultimately enhancing the effectiveness of drought risk management and adaptation efforts in agricultural contexts.

ABSTRACTWith hourly measurements available during daytime between local times of 09:00–16:00, ocean color data derived from the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological (COMS) satellite have been useful for research and surveillance of diurnal processes in the western Pacific Ocean region. However, in early morning and late afternoon measurements, there are significant errors in GOCI-derived ocean color products as the solar-zenith angle (θ0) goes beyond 70°, especially in autumn and winter seasons. In this study, we employ a neural network (NN) model to make corrections on the GOCI-measured normalize water-leaving radiance spectra, nLw(λ), with high θ0 (>70°) in open oceans. Results show that NN-corrected nLw(λ) are consistent with the previous-hour nLw(λ) and make the diurnal variations in the region much more stable and reasonable. Specifically, the GOCI-measured nLw(λ) with θ0 ≤65° in earlier hours of the day (including some nLw(λ) diurnal variation) are considered accurate and used as the ground truth to train NN models for nLw(λ) correction with high θ0. Further analysis of the relationship of ratios (between the NN-corrected and original nLw(λ)) with θ0 shows that the nLw(λ) ratios increase as θ0 increase, which indicates that there are more significant corrections with larger θ0 (>70°). The performance evaluation of the NN models is based on the comparison of NN-corrected nLw(λ) with the original previous hour nLw(λ) data. The ratios of NN-corrected nLw(λ) to the original previous hour nLw(λ) are 0.968–1.045 for the short blue/blue and green bands, and the performance of nLw(λ) correction at 14:00 and 15:00 is slightly better than that at 16:00, due to significantly large θ0 at late afternoon hours. The NN-corrected nLw(λ) data are also used to derive chlorophyll-a (Chl-a) concentration, showing significantly improved Chl-a in GOCI’s late afternoon measurements with θ0 >70°.

Abstract The use of satellite‐based thermal infrared remote sensing has facilitated the assessment of surface water temperature on a large scale. However, the inherent limitations of this remote sensing technique make it difficult to assess rivers unless ambient conditions are cloud‐free, devoid of steep terrain and the rivers are at least 60 m wide. To address these challenges that limit the spatiotemporal continuity of satellite‐based hydro‐thermal data, we harnessed the extensive coverage from the Landsat missions' thermal infrared sensors and data‐driven techniques to estimate surface water temperature of rivers. Out of the tested data‐driven techniques, we selected the Random Forest Regressor as our prime non‐linear approach for estimation of surface water temperature in rivers. Using the selected technique, proposed as THORR (Thermal History of Regulated Rivers), we successfully reconstructed a multi‐decadal, continuous spatiotemporal surface water temperature record for regulated rivers in the Columbia River Basin. Using 42 years of data, the surface water temperature could be predicted on average with 0.71° C of absolute error regardless of the dam's potential thermal influence in the downstream reaches. The reconstructed hydro‐thermal behavior generated from THORR revealed a long‐term downstream warming trend along the Columbia River. The open‐source THORR tool can be extended to any river system around the world that is not gauged with in‐situ temperature measurements for the reconstruction of hydro‐thermal behavior.

Abstract Fire significantly contributes to greenhouse gas emissions. The current global burned area (BA) products mainly have coarse native spatial resolution, which leads to underestimation of global BA and carbon emissions from biomass burning. Performances of BA products in Africa from GABAM (30 m), MCD64A1 (500 m), GFED4s (0.25°), FireCCI51 (250 m), and GFED5 (0.25°) were compared. From 2014 to 2020, GFED5 detected the most BA, 1.58 times more than GABAM during the same period. GABAM detected 0.09 Mkm2 more burned area than FireCCI51 on average. From 2014 to 2016, GABAM detected an average of 2.99 Mkm2 of BA in Africa, which was 1.03 times more than GFED4s. From 2014 to 2021, the average African BA derived from GABAM was 2.89 Mkm2, 1.22 times more than MCD64A1. The increase in BA will inevitably lead to an increase in the estimation of carbon emissions from biomass burning. Based on GABAM products and GFED framework, we estimated the average vegetation burning carbon emissions in Africa from 2014 to 2021 to be 1113.25 Tg, which is higher than GFED4s' carbon emissions in the same time period. This shows that the use of high‐resolution (30 m) burned area products to estimate carbon emissions can effectively avoid the underestimation of overall fire carbon emissions.

The editorial in Frontiers in Veterinary Science discusses the use of satellite Earth observation data for animal health and vector-borne diseases. The research topic explores the application of Earth Observations (EO) for understanding disease ecosystems and leveraging big data for public and animal health benefits. Studies highlighted in the editorial demonstrate the value of EO data in characterizing environmental conditions, predicting disease vectors' distribution, and informing surveillance strategies. The editorial emphasizes the importance of making EO data easily accessible to epidemiologists and integrating insights from various fields to enhance real-time epidemiological monitoring and response to emerging health threats. [Extracted from the article]