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Understanding the distribution and dynamics of particulate organic carbon (POC) is essential for assessing marine ecosystem health and carbon cycling. This study analyzed the spatial and temporal variability of POC in the northeastern coastal waters to the continental shelf of the Gulf of Mexico using satellite remote sensing data within the United States. Over 19 years, surface water POC averaged 155.57 ± 13.56 mg/m3, with values ranging from 127.17 to 188.81 mg/m3, and an insignificant annual increase of 0.78 mg/m3. Spatial analysis across five transects (T1–T5) revealed significant variability, with mean POC values ranging from 125.19 mg/m3 (T5) to 169.23 mg/m3 (T1) and standard deviations from 136.76 mg/m3 (T5) to 193.64 mg/m3 (T3). Transects near the estuaries of the Mississippi and Atchafalaya Rivers (T1–T4) exhibited elevated POC due to terrestrial organic matter and nutrient inputs, while the west Florida shelf (T5) had lower concentrations. Seasonal variations were influenced by wind-driven currents, sea surface temperature (SST), and mesoscale eddies. Analysis of Variance showed a significant seasonal effect on POC concentrations (F-value: 96.51, p < 0.001). A strong negative correlation between SST and POC during winter and fall suggests cooler temperatures enhance vertical mixing and nutrient upwelling, increasing POC. In contrast, warmer SSTs in summer were positively correlated with POC due to stratification and increased biological activity. Chlorophyll-a showed a strong positive correlation with POC, highlighting the role of primary productivity. These results emphasize the importance of satellite-based POC monitoring for a comprehensive understanding of carbon cycling and its biological impacts on marine ecosystems. [ABSTRACT FROM AUTHOR]

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]

Air pollution is amongst the top environmental threats that affect human health and that receive considerable attention in cities. Recent studies have demonstrated the efficacy of early warning techniques in avoiding harmful pollution effects. Thus, air quality monitoring is a necessity to grant a sustainable livability. Deep Learning methods are usually used in smart cities to monitor and forecast air pollutants concentrations. This study proposes a generalization of a deep learning model under the transfer learning paradigm to overcome the limitations of a small in-situ measurements network. More specifically, Nitrogen dioxide levels were estimated in Luxembourg, which has a limited number of ground stations. The initial fine-tuning yielded unsatisfactory outcomes. Consequently, adapted augmentation techniques were applied to improve the model performance. Specifically, the R-squared value improved from 0.12 to 0.79, the Mean Absolute Error dropped from 7.4 to 3.54, and the Mean Squared Error decreased from 93.4 to 19.17. The proposed network framework in this paper can be applied to any geographic area worldwide, enabling the estimation of pollution maps with high spatial resolution. Moreover, the effectiveness of satellite images in predicting the abnormal temporal patterns of Nitrogen dioxide has been proven. [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]

Timely and accurate information on crop productivity is essential for characterizing crop growing status and guiding adaptive management practices to ensure food security. Terrestrial biosphere models forced by satellite observations (satellite-TBMs) are viewed as robust tools for understanding large-scale agricultural productivity, with distinct advantages of generalized input data requirement and comprehensive representation of carbon–water-energy exchange mechanisms. However, it remains unclear whether these models can maintain consistent accuracy at field scale and provide useful information for farmers to make site-specific management decisions. This study aims to investigate the capability of a satellite-TBM to estimate crop productivity at the granularity of individual fields using harmonized Sentinel-2 and Landsat-8 time series. Emphasis was placed on evaluating the model performance in: (i) representing crop response to the spatially and temporally varying field management practices, and (ii) capturing the variation in crop growth, biomass and yield under complex interactions among crop genotypes, environment, and management conditions. To achieve the first objective, we conducted on-farm experiments with controlled nitrogen (N) fertilization and irrigation treatments to assess the efficacy of using satellite-retrieved leaf area index (LAI) to reflect the effect of management practices in the TBM. For the second objective, we integrated a yield formation module into the satellite-TBM and compared it with the semi-empirical harvest index (HI) method. The model performance was then evaluated under varying conditions using an extensive dataset consisting of observations from four crop species (i.e., soybean, wheat, rice and maize), 42 cultivars and 58 field-years. Results demonstrated that satellite-retrieved LAI effectively captured the effects of N and water supply on crop growth, showing high sensitivity to both the timing and quantity of these inputs. This allowed for a spatiotemporal representation of management impacts, even without prior knowledge of the specific management schedules. The TBM forced by satellite LAI produced consistent biomass dynamics with ground measurements, showing an overall correlation coefficient (R) of 0.93 and a relative root mean square error (RRMSE) of 31.4 %. However, model performance declined from biomass to yield estimation, with the HI-based method (R = 0.80, RRMSE = 23.7 %) outperforming mechanistic modeling of grain filling (R = 0.43, RRMSE = 43.4 %). Model accuracy for winter wheat was lower than that for summer crops such as rice, maize and soybean, suggesting potential underrepresentation of the overwintering processes. This study illustrates the utility of satellite-TBMs in crop productivity estimation at the field level, and identifies existing uncertainties and limitations for future model developments. [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.

River regulation in mountainous and high-precipitation regions with hydropower dams often struggles to find the right balance between hydropower generation while ensuring flood protection for downstream inhabitants. The goal of hydropower generation is to keep reservoirs at the maximum pool as often as possible while for flood control, it is to maintain sufficient cushion in available storage to absorb an incoming flood wave. Using weather forecasts to proactively manage reservoir operations for such conflicting goals is now a well-known solution. However, this challenge of applying forecast-informed reservoir operations is magnified in developing regions where there is a paucity of ground data to track reservoir dynamics. In this study, we explore the utility of using publicly available precipitation forecasts from the Global Ensemble Forecasting System (GEFS) with a fully satellite-based reservoir tracking framework called reservoir assessment tool (RAT) to understand the potential of forecast-informed operations in highly mountainous and high-precipitation regions that are mostly ungauged. We apply our investigation to the case of damaging floods that took place in 2018 in the Southern Indian state of Kerala where river regulation is carried out with a fleet of hydropower dams. Our results show that the precipitation forecast from GEFS has sufficient skill, if focused on trends and bias adjustment, to predict reservoir inflow peaks up to a week ahead of time where the trend for the timing of the peak and rate of rise match well. Using our satellite-based RAT framework, we explore the range of actionable scenarios for dam operators that could potentially minimize downstream flood risk with this forecast-informed reservoir operations scheme. [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.

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]

Since the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies. [ABSTRACT FROM AUTHOR]

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]

The El Niño-Southern Oscillation (ENSO) is a critical climate phenomenon influencing global weather patterns, marine ecosystems, and socio-economic conditions. Recent advancements in observational techniques, including satellite remote sensing, ocean buoys, and advanced climate modeling, have significantly enhanced our understanding of ENSO dynamics. This study reviews the latest progress in monitoring ENSO events, focusing on improved predictive capabilities and real-time data collection. Enhanced satellite observations have provided high-resolution sea surface temperature and atmospheric pressure data, while buoy networks have facilitated continuous monitoring of oceanic conditions. Additionally, machine learning algorithms are increasingly employed to analyze complex datasets for better forecasting accuracy. These advancements not only improve our ability to predict the onset and intensity of El Niño and La Niña events but also aid in assessing their impacts on global weather systems. Continued interdisciplinary collaboration is essential for further refining ENSO observation techniques and mitigating its socio-economic impacts. [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]

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]

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]

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]