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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]

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]

In order to deeply understand the spatio-temporal distribution patterns of forest fires and reduce the adverse effects of forest fires on the ecological environment and human activities, the key parameters and dynamics of multi-source satellite for fire point identification was established using data from 8 domestic and foreign meteorological satellites and based on the classic context method. The satellite monitoring buffer zone radius verification method was used to verify the authenticity of forest hot spots retrieved from multi-source satellites, and the real forest hot spot data during the fire prevention period in 2021—2022 were used to analyze the spatio-temporal characteristics of forest fires. The results show as follows. The accuracy of satellite fire spot monitoring is 84. 42%, and the fire point classification accuracy is 89. 90% . The established inversion method is reasonable and reliable. The spatial distribution of forest fires in Hunan is “more in the southwest and less in the northeast”. At the same time the high-incidence areas are mainly distributed in southern Hunan, and the second-highest-incidence area is western Hunan. In summary, the risk of forest fires during the autumn prevention period is much greater than that during the spring prevention period. During the extreme high temperature and drought in 2022, forest fires were mainly distributed in the southern Hunan region and the Hengshao Basin. From the perspective of process distribution, the distribution of forest fires can be divided into four stages. The number of forest fires in the first three stages showed a significant increase trend, and in the third stage, the number of forest fires increased significantly. Fire risk is the most serious. In the fourth stage, due to the dual impact of precipitation and the province,s fire ban, the risk of forest hot spots was significantly reduced. [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]

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

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

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]

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]

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

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.

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.

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]

Aiming at scientific promotion of regional ecological conservation, maintaining the stability of regional ecological patterns, and promoting socio-economic high-quality development, the accurate understanding of the spatiotemporal evolution characteristics of land use patterns and the contribution of influencing factors is of great significance. Taking Tianjin city as an example, the spatiotemporal changes in land use during its urban expansion process were analyzed via dynamic degree models, based on annual high-resolution land cover datasets. The aim is to explore and reveal the changing trends, spatial distribution patterns, and influencing factors of land use types in Tianjin city. The results indicate that from 1992 to 2021, the main changes in land use types in Tianjin city were the continuous decrease in arable land, the continuous expansion of construction land centered on the urban and new urban centers, and the increase first and then decrease in water body area. The land use degree index continues to increase but remains at a moderate level, suggesting room for further development. The rapid development of Tianjin city has outpaced its own structural adjustment, resulting in poor stability. The slow increase in equilibrium indicates a slow trend in land use structure change, while the slow decline in dominance implies a gradual weakening of arable land dominance and an increase in construction land dominance. Natural environmental factors, humanities and social factors, and accessibility factors contribute significantly to changes in various land cover types. Among them, elevation contributes significantly to arable land, woodland, and impervious surface area, while slope contributes significantly to bare land. Additionally, factors such as gross domestic product (GDP), population, and distance to secondary roads contribute significantly to arable land, water body, and impervious surface area, but has different constraints direction on different land types. [ABSTRACT FROM AUTHOR]

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

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