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Climate change impacts manifest differently worldwide, with many African countries, including Senegal, being particularly vulnerable. The decline in ground observations and limited access to these observations continue to impede research efforts to understand, plan, and mitigate the current and future impacts of climate change. This occurs at a time of rapid growth in Earth observations (EO) data, methodologies, and computational capabilities, which could potentially augment studies in data-scarce regions. In this study, we utilized satellite remote sensing data leveraging historical EO data using Google Earth Engine to investigate spatio-temporal rainfall and temperature patterns in Senegal from 1981 to 2020. We combined CHIRPS precipitation data and ERA5-Land reanalysis datasets for remote sensing analysis and used the Mann–Kendall and Sen's Slope statistical tests for trend detection. Our results indicate that annual temperatures and precipitation increased by 0.73°C and 18 mm in Senegal from 1981 to 2020. All six of Senegal's agroecological zones showed statistically significant upward precipitation trends. However, the Casamance, Ferlo, Eastern Senegal, Groundnut Basin, and Senegal River Valley regions exhibited statistically significant upward trends in temperature. In the south, the approach to climate change would be centered on the effects of increased rainfall, such as flooding and soil erosion. Conversely, in the drier northern areas such as Podo and Saint Louis, the focus would be on addressing water scarcity and drought conditions. High temperatures in key crop-producing regions, such as Saraya, Goudiry, and Tambacounda in the Eastern Senegal area also threaten crop yields, especially maize, sorghum, millet, and peanuts. By acknowledging and addressing the unique impacts of climate change on various agroecological zones, policymakers and stakeholders can develop and implement customized adaptation strategies that are more successful in fostering resilience and ensuring sustainable agricultural production in the face of a changing climate. [ABSTRACT FROM AUTHOR]

The analysis of marine environmental parameters plays an important role in areas such as sea surface simulation modeling, analysis of sea clutter characteristics, and environmental monitoring. However, ocean observation remote sensing satellites typically deliver large volumes of data with limited spatial resolution, which often does not meet the precision requirements of practical applications. To overcome challenges in constructing high-resolution marine environmental parameters, this study conducts a systematic comparison of various interpolation techniques and deep learning models, aiming to develop a highly effective and efficient model optimized for enhancing the resolution of marine applications. Specifically, we incorporated adaptive global attention (AGA) mechanisms and a spatial gating unit (SGU) into the model. The AGA mechanism dynamically adjusts the weights of different regions in feature maps, enabling the model to focus more on critical spatial features and channel features. The SGU optimizes the utilization of spatial information by controlling the information transmission pathways. The experimental results indicate that for four types of marine environmental parameters from ERA5, our model achieves an overall PSNR of 44.0705, an SSIM of 0.9947, and an MAE of 0.2606 when the resolution is increased by a upscale factor of 2, as well as an overall PSNR of 35.5215, an SSIM of 0.9732, and an MAE of 0.8330 when the resolution is increased by an upscale factor of 4. These experiments demonstrate the model's effectiveness in enhancing the spatial resolution of satellite-derived marine environmental parameters and its ability to be applied to any marine region, providing data support for many subsequent oceanic studies. [ABSTRACT FROM AUTHOR]

Water quality monitoring is essential to assess and manage anthropogenic eutrophication, especially for coastal estuaries in heavily populated areas. Current monitoring techniques rely on in situ sampling, which can be expensive and limited in spatial and temporal coverage. Satellite remote sensing, using the Landsat 8 (Operational Land Imager, OLI) platform, has the potential to provide more extensive coverage than traditional methods. Coastal waters are optically more complex and often shallower and more enclosed than the open ocean, presenting conditions that pose challenges to remote sensing approaches. Here, we compared in situ data from 18 stations around Pleasant Bay, Massachusetts, USA from the years 2014–2021 to contemporaneous observations with Landsat 8 OLI. Satellite-derived estimates of chlorophyll-a and Secchi depth were acquired using various algorithms including the "Case-2 Regional/Coast Color" (C2RCC), "Case-2 Extreme" (C2X), l2gen processor, and a random forest machine learning algorithm. Based on our results, predictions of water quality indices from both C2RCC and random forest techniques can be a useful addition to existing water quality monitoring efforts, potentially expanding both spatial and temporal coverage of monitoring efforts. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is one of the main greenhouse gases and has become a major concern as its concentration has been growing in recent years. Satellite remote sensing is an efficient way to monitor CO2 in the atmosphere, and several satellites are already used for CO2 monitoring. It is imperative to investigate the spatial coverage and spatio-temporal trends of satellite products, as well as identify the satellites with higher levels of accuracy. Additionally, examining the disparities between the older and new generations of satellites would be meaningful. Therefore, this paper provides a comprehensive evaluation and inter-comparison for the commonly used satellite column-averaged dry-air mole fraction of CO2 (XCO2) products. Specifically, the temporal trends and monthly coverage of the Greenhouse Gases Observing SATellite (GOSAT), Greenhouse Gases Observing SATellite-2 (GOSAT-2), Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are investigated. The accuracy of these satellite products is evaluated and analyzed based on Total Carbon Column Observing Network (TCCON) data. The results indicate that the XCO2 of all the satellite products show a year-by-year increase, with seasonal periodicity. In terms of overall accuracy, the OCO series satellites exhibit a slightly higher level of accuracy compared to the GOSAT series. The products of the new generation of satellites are less stable than those of the older generation, probably due to the impacts of the inversion algorithm and platforms. [ABSTRACT FROM AUTHOR]

Urban growth in sub-Saharan Africa presents significant challenges to sustainable development, food security, and environmental conservation. The rapid urban expansion and impact on agricultural land reduction in central Ethiopian metropolitan areas (Addis Ababa and Sheger city) exemplify these issues while simultaneously offering opportunities for sustainable development. This study aims to quantify and characterize the spatiotemporal dynamics of urban expansion in Addis Ababa and the surrounding Sheger city, explicitly focusing on understanding the impact of urban expansion on farmlands. The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. The study employed an analytical framework incorporating multiple methodologies: intensity analysis at interval, categorical, and transitional levels to quantify urban growth trajectories; gradient direction and distance analyses to examine spatial expansion patterns; and Land Expansion Index (LEI) and Landscape Dynamic Typology (LDT) metrics to characterize the urban morphology and spatial dynamics of the study area. The results revealed that edge expansion is the predominant mode of urban development, primarily affecting farmlands in the eastern section. Built-up areas quadrupled between 1990 and 2023, whereas arable land declined. Intensity analysis revealed significant changes, particularly affecting farmlands. Our LDT analysis showed reduction in stable areas and increased in LULC changes from 1990 to 2023. The findings highlight the need for revised urban development strategies in Ethiopia to focus on compact and efficient growth while safeguarding agricultural lands, aligning with SDGs 2, 11, and 15 to promote balanced development that ensures urban and agricultural sustainability. [ABSTRACT FROM AUTHOR]

The equatorial Pacific serves as the world's largest oceanic source of CO2. The contrasting ocean environment in the eastern (i.e., upwelling) and western (i.e., warm pool) regions makes it difficult to fully characterize its CO2 dynamics with limited in situ observations. In this study, we addressed this challenge using monthly surface partial pressure of CO2 (pCO2sw) and air-sea CO2 fluxes (FCO2) data products reconstructed from satellite and reanalysis data at a spatial resolution of 1° × 1° in the period of 1982–2021. We found that during the very strong El Niño events (1997/1998, 2015/2016), both pCO2sw and FCO2 showed a significant decrease of 41–58 μatm and 0.5–0.8 mol·m−2·yr−1 in the eastern equatorial Pacific, yet they remained at normal levels in the western equatorial Pacific. In contrast, during the very strong La Niña events (1999/2000, 2007/2008, and 2010/2011), both pCO2sw and FCO2 showed a strong increase of 40–48 μatm and 1.0–1.4 mol·m−2·yr−1 in the western equatorial Pacific, yet with little change in the eastern equatorial Pacific. In the past 40 years, pCO2sw in the eastern equatorial Pacific was increasing at a higher rate (2.32–2.51 μatm·yr−1) than that in the western equatorial Pacific (1.75 μatm·yr−1), resulting in an accelerating CO2 outgassing (at a rate of 0.03 mol·m−2·yr−2) in the eastern equatorial Pacific. We comprehensively analyzed the potential effects of different factors, such as sea surface temperature, sea surface wind speed, and ΔpCO2 in driving CO2 fluxes in the equatorial Pacific, and found that ΔpCO2 had the highest correlation (R ≥ 0.80, at p ≤ 0.05), highlighting the importance of accurate estimates of pCO2sw from satellites. Further studies are needed to constrain the retrieval accuracy of pCO2sw in the equatorial Pacific from satellite remote sensing. [ABSTRACT FROM AUTHOR]

Urban overheating significantly affects thermal comfort and livability, making it essential to understand the relationship between urban form and land surface temperature (LST). While the horizontal dimensions of urban form have been widely studied, the vertical structures and their impact on LST remain underexplored. This study investigates the influence of three-dimensional urban form characteristics on LST, using ECOSTRESS sensor data and four machine learning models. Six urban morphology variables—building density (BD), mean building height (MH), building volume (BVD), gross floor area (GFA), floor area ratio (FAR), and sky view factor (SVF)—are analyzed across different seasons and times of day. The results reveal that MH, BD, and FAR are season-stable factors, with higher MH correlated with lower LST ((e.g., an observed reduction of approximately 3 °C in spring), while higher BD is associated with higher LST (e.g., an increase of about 3.5 °C in autumn). In contrast, BVD, GFA, and SVF are season-varying factors with variable impacts depending on the time of year. Higher BVD is generally associated with elevated LST, while GFA and SVF are linked to lower LST. These associations reflect absolute changes in LST, measured directly from ECOSTRESS data. These findings offer valuable insights into the complex interactions between urban morphology and LST, helping to inform strategies for urban heat mitigation and sustainable planning. [ABSTRACT FROM AUTHOR]

The rapid development of nanosatellite technologies, their low development cost, and their economical launching due to their small size have made them an excellent option for Earth Observation (EO) and remote sensing. Nanosatellites are widely used in generic applications, such as education, vegetation monitoring, natural disasters, oceanography, and specialized applications, such as disaster response, and they serve as an Internet of Things (IoT) communications platform. This paper presents a review of the latest public nanosatellite EO missions, their applications, and their propulsion systems. Furthermore, we discuss specialized applications of the nanosatellites and their use in remote sensing for EO. Likewise, we aim to present the limitations of the nanosatellites in remote sensing, a comprehensive taxonomy according to propulsion systems, and directions for future research. [ABSTRACT FROM AUTHOR]

To address the challenge of detecting small objects in aerial and satellite remote sensing images with low-resolution, we propose a high-precision object detection method based on PFRNet. PFRNet incorporates parallel feature extraction branches and a progressive feature refinement mechanism, significantly enhancing the model’s ability to perceive detailed features. In addition, PFRNet introduces the spatial pyramid pooling fusion with spatial attention (SPPFSPA) module, which integrates multi-scale features with an attention mechanism, enabling the model to better focus on areas of interest, thereby improving detection performance. Results demonstrate that PFRNet achieves outstanding detection accuracy, markedly outperforming other algorithms, particularly in small object detection. Visualization analysis reveals that the PFR module effectively captures richer and more comprehensive visual features in images, providing robust input for subsequent detection tasks, which is crucial for PFRNet’s superior performance. Overall, the proposed PFRNet model makes significant strides in small object detection in UAV aerial and satellite remote sensing images, offering strong support for applications such as intelligent transportation and precision agriculture.

Abstract Urban growth in sub-Saharan Africa presents significant challenges to sustainable development, food security, and environmental conservation. The rapid urban expansion and impact on agricultural land reduction in central Ethiopian metropolitan areas (Addis Ababa and Sheger city) exemplify these issues while simultaneously offering opportunities for sustainable development. This study aims to quantify and characterize the spatiotemporal dynamics of urban expansion in Addis Ababa and the surrounding Sheger city, explicitly focusing on understanding the impact of urban expansion on farmlands. The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. The study employed an analytical framework incorporating multiple methodologies: intensity analysis at interval, categorical, and transitional levels to quantify urban growth trajectories; gradient direction and distance analyses to examine spatial expansion patterns; and Land Expansion Index (LEI) and Landscape Dynamic Typology (LDT) metrics to characterize the urban morphology and spatial dynamics of the study area. The results revealed that edge expansion is the predominant mode of urban development, primarily affecting farmlands in the eastern section. Built-up areas quadrupled between 1990 and 2023, whereas arable land declined. Intensity analysis revealed significant changes, particularly affecting farmlands. Our LDT analysis showed reduction in stable areas and increased in LULC changes from 1990 to 2023. The findings highlight the need for revised urban development strategies in Ethiopia to focus on compact and efficient growth while safeguarding agricultural lands, aligning with SDGs 2, 11, and 15 to promote balanced development that ensures urban and agricultural sustainability.

The near-surface air temperature (${{T}_{air}}$) is a principal variable describing energy exchange and water circulation between the land surface and the atmospheric environment. The estimation of ${{T}_{air}}$ by satellite land surface temperature (LST) is challenging due to the variable magnitude of the difference between ${{T}_{air}}$ and LST in both space and time, as well as the restriction of estimated ${{T}_{air}}$ to clear-sky conditions because of the penetration of infrared wavelengths. Moreover, the estimation suffers from low temporal resolution and primarily focuses on daily minimum, maximum, and two instantaneous ${{T}_{air}}$ per day. This study proposes a method for estimating all-sky gridded diurnal ${{T}_{air}}$ at regional scale from FY-4B/AGRI measurements. The multiscale geographically weighted regression model was investigated to establish the dynamic relationships between ground station observed ${{T}_{air}}$ and satellite LST under clear-sky conditions by employing different spatial values for each explanatory variable in localized regressions. A moving window loop based multiple linear regression was employed to establish the relationship between satellite-derived clear-sky ${{T}_{air}}$ and other variables to extrapolate ${{T}_{air}}$ in cloudy-sky pixels. The results showed that the proposed method captures the trend of ${{T}_{air}}$ variations well in hourly profiles with R values greater than 0.95. RMSE was 1.75 °C, 1.38 °C, 1.95 °C, and 2.19 °C in April, July, October, and January, respectively. The demonstration of heatwave monitoring showed that satellite-estimated ${{T}_{air}}$ provide an excellent representation of the spatial and temporal evolution of the heatwave.

This study utilizes a high-resolution emission inventory and the WRF-CMAQ modeling system to analyze the temporal and spatial evolution of tropospheric NO₂ vertical column density （VCD） derived from TROPOMI satellite data. It also provides a preliminary assessment of the uncertainty in the NOₓ emission inventory for the Sichuan Basin in 2019. The findings reveal elevated tropospheric NO₂ VCD in areas with intense anthropogenic activity， including the Chengdu Plain， southern Sichuan's urban clusters， and Chongqing， while the central Sichuan Basin remains relatively clean. Seasonal variations， influenced by both meteorological conditions and anthropogenic emissions， show significantly higher NO₂ VCD in winter and spring compared to summer and autumn. A comparison between the WRF-CMAQ model and TROPOMI satellite data for January 2019 indicates strong agreement in cleaner regions， though TROPOMI reports notably higher NO₂ VCD in high-emission cities such as Chengdu and Chongqing， suggesting that the emission inventory may underestimate NOₓ emissions in megacities. This work underscores the need for stringent NOₓ emission controls in major cities， such as Chengdu and Chongqing， while also emphasizing the urgency of enhancing emission controls in medium-sized cities across the Sichuan Basin.

[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 summarized and the research prospects are put forward. [Results] In 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 are common 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. It is necessary to further study the use of semi-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 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.

We call on conservation funders, technology developers, and practitioners to explore how digital technologies can transform conservation practice. Actors supporting, developing, and funding digital technologies for conservation must address digital inequity and reduce the societal risks of digital technologies that may undermine conservation goals. We highlight the challenges in leveraging digital conservation technologies and recommend approaches to increase access to digital technologies for uptake by diverse users while supporting equitable participation from diverse user communities to shape digital technologies and their applications. Improving access to and use of tools may be achieved through strategic funding for digital design that recognizes and supports local solutions and diverse practices and perspectives. With increasing digital access, funders must also emphasize adherence to safeguards and protocols to reduce risks associated with digital technologies. By adopting more ethical methodologies related to digital technologies, we not only enhance global sustainability but also foster collaborative relationships with communities, recognizing the intrinsic value of their expertise in conservation initiatives and jointly safeguarding the environment to ensure the well‐being of all. Encouraging more equitable approaches to conservation technologies underpins global priorities for sustainable development by centering and supporting the communities most directly involved in conservation action. [ABSTRACT FROM AUTHOR]

We analyzed the temporal trends (1998–2022) of surface phytoplankton Chlorophyll (Chl) concentration in the Arctic at the local, regional, and pan‐Arctic scales. We used four empirically derived Chl satellite ocean color products: two global merged products and two MODIS products, one calibrated to the Arctic. At the local level, between 10% and 40% of the area with valid pixels showed statistically significant Chl trends, with ∼2/3 ${\sim} 2/3$ of those pixels showing increases, and the other third indicating a decrease. At the regional level, only the Barents and Chukchi Seas had consistent Chl increases across products. At the pan‐Arctic level, most products showed Chl increases in the months of July and September (0.3%–0.9% Chl year−1 ${\text{year}}^{-1}$), even after removing the effect of new open water pixels. Overall, Chl is changing in the Arctic, although trends vary threefold depending on the product and spatial‐averaging assumptions used. Plain Language Summary: The Arctic is undergoing critical physical changes that can affect marine ecosystems. Here we analyzed how the concentration of phytoplankton (microorganisms at the base of the marine food‐web) has changed since 1998. To do so, we investigated the temporal trends of chlorophyll (a signature of phytoplankton) as derived from satellites. We found that about 10%–40% of the area with valid satellite pixels had statistically significant phytoplankton trends, with some regions increasing and others decreasing. Over the entire Arctic, Chl has been increasing since 1998, however, the magnitude and statistical significance of the trends varied depending on the satellite product used. Key Points: Depending on the month and product, 10%–40% of the area with valid pixels showed statistically significant Chl trendsChl trends in the Arctic are heterogeneous, with some regions increasing and other decreasingMagnitude and significance of Chl trends varied depending on the satellite product used [ABSTRACT FROM AUTHOR]

Oil pollution is the main environmental problem of the Caspian Sea, and a significant contribution to the total oil pollution is made by natural hydrocarbon showings at the seabed. In this paper, we discuss the spatial and temporal variability of the trajectories of natural oil slicks (NOSs) after their emerging to the surface. The study is based on satellite synthetic aperture radar data and data from multispectral satellite sensors in the optical range obtained over 5 years of a survey from 2017 to 2021 in two test areas in the southern part of the Caspian Sea. These areas are a water area near the southwest coast eastward of Cape Sefid Rud (Gilan Province, Iran) and a water area westward of the Cheleken Peninsula, which administratively belongs to Turkmenistan. Natural hydrocarbon seepages at the seabed were discovered in these regions through satellite data. Our main results include the discovery of significant seasonal variability in the NOS distribution directions in both test regions caused by the influence of local winds and surface currents that prevail in different seasons. Various types of NOS distribution trajectories were considered, and assumptions were made on the mechanisms of their formation. The impact of vortex dynamics on the spreading of the NOS and its contribution to the cross-shelf transport of oil pollution was noted. [ABSTRACT FROM AUTHOR]

Satellite remote sensing enables monitoring of regenerative agriculture practices, such as crop rotation, cover cropping, and conservation tillage to allow tracking and quantification at unprecedented scales. The Monitor system presented here capitalizes on the scope and scale of these data by integrating crop identification, cover cropping, and tillage intensity estimations annually at field scales across the contiguous United States (CONUS) from 2014 to 2023. The results provide the first ever mapping of these practices at this temporal fidelity and spatial scale, unlocking valuable insights for sustainable agricultural management. Monitor incorporates three datasets: CropID, a deep learning transformer model using Sentinel-2 and USDA Cropland Data Layer (CDL) data from 2018 to 2023 to predict annual crop types; the living root data, which use Normalized Difference Vegetation Index (NDVI) data to determine cover crop presence through regional parameterization; and residue cover (RC) data, which uses the Normalized Difference Tillage Index (NDTI) and crop residue cover (CRC) index to assess tillage intensity. The system calculates field-scale statistics and integrates these components to compile a comprehensive field management history. Results are validated with 35,184 ground-truth data points from 19 U.S. states, showing an overall accuracy of 80% for crop identification, 78% for cover crop detection, and 63% for tillage intensity. Also, comparisons with USDA NASS Ag Census data indicate that cover crop adoption rates were within 20% of estimates for 90% of states in 2017 and 81% in 2022, while for conventional tillage, 52% and 25% of states were within 20% of estimates, increasing to 75% and 67% for conservation tillage. Monitor provides a comprehensive view of regenerative practices by crop season for all of CONUS across a decade, supporting decision-making for sustainable agricultural management including associated outcomes such as reductions in emissions, long term yield resiliency, and supply chain stability. [ABSTRACT FROM AUTHOR]

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This paper explores the civil liability issues arising from the commercial use of satellite remote sensing data, a rapidly growing sector in the space industry. With the increasing reliance on satellite data for various applications, such as agriculture, disaster response, and climate monitoring, legal challenges have emerged, particularly concerning the accuracy and commercialization of satellite data. The study examines the concept and characteristics of satellite remote sensing, focusing on the legal relationships between data providers, users, and third parties. It analyzes the legal framework regulating this business across different jurisdictions, including the United States, Canada, Germany, France, and Japan. Key issues addressed include liability for inaccurate data, licensing agreements, and the rights and obligations of parties involved in satellite data transactions. Through this analysis, the paper offers legal and institutional recommendations to support the development and stability of the commercial satellite data industry, contributing to the establishment of a comprehensive legal framework for the space sector. [ABSTRACT FROM AUTHOR]

The vertically generalized production model (VGPM) is one of the most important methods for estimating marine net primary productivity (PP) using remote sensing. However, different data sources and parameterization schemes of the input variables for the VGPM can introduce uncertainties to the model results. This study compared the PP results from different data sources and parameterization schemes of three major input variables (i.e., chlorophyll-a concentration ( C o p t ), euphotic depth ( Z e u ), and maximum photosynthetic rate ( P o p t B )) and evaluated the sensitivity of VGPM in the Yellow and Bohai Seas on the inputs. The results showed that the sensitivity in the annual mean PP was approximately 40%. Seasonally, the sensitivity was lowest in the spring (35%), highest in the winter (70%), and approximately 60% in the summer and autumn. Spatially, the sensitivity in nearshore water (water depth < 40 m) was more than 60% and around two times higher than that in deep water areas. Nevertheless, all VGPM results showed a decline trend in the PP from 2003 to 2020 in the Yellow and Bohai Seas. The influence of P o p t B and C o p t was important for the magnitude of annual mean PP. The PP seasonal variation pattern was highly related to the parameterization scheme of P o p t B , whereas the spatial distribution was mostly sensitive to the data sources of C o p t . [ABSTRACT FROM AUTHOR]

The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman's correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (rs = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile's optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties. [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]

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]

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]

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]

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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]

The planktonic copepod, Calanus finmarchicus, plays a pivotal role in the Gulf of Maine (GoM) pelagic food web as a primary food source for many species, including the critically endangered North Atlantic right whale (NARW). Thus, observing C. finmarchicus on a Gulf-wide scale via satellite could be beneficial for understanding changes in the migration patterns of the NARW. This study investigated the application of ocean color remote sensing to detect the surface population levels of C. finmarchicus in the GoM. Using remote sensing reflectance data from the MODIS Aqua sensor, we processed enhanced RGB (eRGB) imagery to detect and quantify the presence of C. finmarchicus, which is identifiable by its red astaxanthin pigment. This study employed a refined approach from the method originally developed off the coast of Norway, which integrates eRGB imagery and radiative transfer modeling to generate optical anomaly maps that are used for quantifying surface C. finmarchicus concentrations in the GoM. We detected surface swarms of C. finmarchicus in the ocean color imagery and estimated their concentrations. However, due to the method’s reliance on astaxanthin/red pigment-based detection, other astaxanthin-rich red/brown plankton were misidentified as C. finmarchicus. While the approach presented is effective for identifying astaxanthin anomalies in ocean color and holds potential for quantifying the surface populations of C. finmarchicus, it requires local knowledge to accurately quantify the C. finmarchicus abundances.

Abstract The Earth's rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Here, we use monthly simulations of river stores and fluxes to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale‐dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. By filtering reaches by estimated river widths, we quantify the relative contribution of global river reaches by size and estimate that over 17% of global discharge to ocean and nearly 9% of the world's river storage lies within rivers smaller than 100 m—hence revealing both strengths and limitations of current observational capabilities.

ABSTRACT Plant diversity is important in enhancing an ecosystem's drought resistance. However, the relationship between plant diversity and drought resistance has historically aroused much controversy. Given that most previous studies on the relationship were conducted with in situ data at a small or point scale, this study explored the relationship with satellite remote sensing, taking Yunnan Province of China as the study area. Specifically, Sentinel‐2 remote sensing data were used to estimate plant diversity. The temporal correlation between the standardized vegetation index (SVI) and standardized precipitation evapotranspiration index (SPEI) was used to express the vegetation sensitivity to drought or drought resistance. A moving window method was developed to explore the relationship between plant diversity and drought resistance. MODIS and SPEI data from 2000 to 2018, as well as Meteorological reanalysis data from 1990 to 2020, were utilized. Results indicated that (1) the remotely sensed plant diversity index was found significantly correlated with field investigations of plant diversity in the study area, with a correlation coefficient of around 0.43–0.49 and p‐value

Climate change impacts manifest differently worldwide, with many African countries, including Senegal, being particularly vulnerable. The decline in ground observations and limited access to these observations continue to impede research efforts to understand, plan, and mitigate the current and future impacts of climate change. This occurs at a time of rapid growth in Earth observations (EO) data, methodologies, and computational capabilities, which could potentially augment studies in data-scarce regions. In this study, we utilized satellite remote sensing data leveraging historical EO data using Google Earth Engine to investigate spatio-temporal rainfall and temperature patterns in Senegal from 1981 to 2020. We combined CHIRPS precipitation data and ERA5-Land reanalysis datasets for remote sensing analysis and used the Mann–Kendall and Sen's Slope statistical tests for trend detection. Our results indicate that annual temperatures and precipitation increased by 0.73°C and 18 mm in Senegal from 1981 to 2020. All six of Senegal's agroecological zones showed statistically significant upward precipitation trends. However, the Casamance, Ferlo, Eastern Senegal, Groundnut Basin, and Senegal River Valley regions exhibited statistically significant upward trends in temperature. In the south, the approach to climate change would be centered on the effects of increased rainfall, such as flooding and soil erosion. Conversely, in the drier northern areas such as Podo and Saint Louis, the focus would be on addressing water scarcity and drought conditions. High temperatures in key crop-producing regions, such as Saraya, Goudiry, and Tambacounda in the Eastern Senegal area also threaten crop yields, especially maize, sorghum, millet, and peanuts. By acknowledging and addressing the unique impacts of climate change on various agroecological zones, policymakers and stakeholders can develop and implement customized adaptation strategies that are more successful in fostering resilience and ensuring sustainable agricultural production in the face of a changing climate.

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]

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]

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]

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.