Detailed Scale Research Articles

Exploring the innovation potential of urban space at the micro scale: a case study of Suzhou’s main urban area

As urban areas serve as spatial carriers for innovation activities, exploring the interaction between innovation and urban spaces has become a research hotspot. Previous studies have primarily focused on urban innovation capacity assessment and measurement, with less attention given to the urban innovation potential and issues such as insufficient granularity of research and weak stability in factor weighting. In this study, an urban spatial innovation potential measurement system was proposed from a demand-side perspective, the structural equation model (SEM) was employed to investigate the direct and indirect influences of factors, and micro-scale empirical analysis was conducted in the main urban area of Suzhou, China. Coupling and coordination with innovation outputs were analyzed. The results revealed that (1) the innovation potential exhibits significant spatial clustering characteristics; (2) there is clear core-periphery spatial differentiation in the innovation potential, demonstrating decreasing spatial diffusion effects with distance from the center; and (3) only 8.18% of the plots in the study area occur at the harmonized innovation coupling stage, indicating that the overall innovation conversion efficiency should be improved. This study aimed to provide a sound basis and practical references for optimizing the layout and improving the quality of urban innovation spaces from a detailed scale perspective, thereby enhancing urban competitiveness.

Mesoscopic Modeling of Bio-Compatible PLGA Polymers with Coarse-Grained Molecular Dynamics Simulations.

A challenging topic in materials engineering is the development of numerical models that can accurately predict material properties with atomistic accuracy, matching the scale and level of detail achieved by experiments. In this regard, coarse-grained (CG) molecular dynamics (MD) simulations are a popular method for achieving this goal. Despite the efforts of the scientific community, a reliable CG model with quasi-atomistic accuracy has not yet been fully achieved for the design and prototyping of materials, especially polymers. In this paper, we describe a CG model for polymers, focusing on the biocompatible poly(lactic-co-glycolic acid) (PLGA), based on a general parametrization strategy with a potentially broader field of applications. In this model, polymers are represented with finite-size ellipsoids, short-range interactions are accounted for with the generalized Gay-Berne potential, while electrostatic and long-range interactions are accounted for with point charges within the ellipsoids. The model was validated against its atomistic counterpart, obtained through a back-mapping process, by comparing physical properties such as glass transition temperature, thermal conductivity, and elastic moduli. We observed quantitative agreement between the atomistic and CG representations, thus opening up the possibility of adopting the proposed model to expand the domain size of typical MD simulations to dimensions comparable to those of experimental setups.

Assessing the grey water footprint of pesticide use in the Mendoza wine region (Argentina): implications for sustainable water resources management

This paper assesses the impact of viticulture on water resource quality in the Mendoza wine region using the grey water footprint (GWF) approach to estimate the amount of water required to dilute pesticides commonly used in local vineyards. Our analysis indicates that to progress towards sustainable water management in viticulture, limiting or replacing pesticides with high GWF values is essential. We provide detailed results for 24 fungicides, 7 insecticides, and 7 herbicides, assessed at both microregion and district levels, offering insights into pesticide impacts across both detailed and broader spatial scales. At the microregion level, the herbicide Fluroxypyr-meptyl was found to have the highest GWF (1.10 m³ kg-1), followed by the fungicide Fosetyl-aluminium (0.59 m³ kg-1) and the insecticide Imidacloprid (0.41 m³ kg-1). Our findings also show that pesticide impacts vary at the district level, highlighting the need for localised management strategies. Additionally, the significant variability in GWFs at the local level underscores the necessity for region-specific water quality standards to more accurately assess and manage the environmental impact of pesticide use. This study provides a framework for similar assessments in other viticultural regions, aiding in the development of more informed pesticide management to enhance water resource sustainability.

Charged critical behavior and nonperturbative continuum limit of three-dimensional lattice SU(Nc) gauge Higgs models

We consider three-dimensional (3D) lattice SU(Nc) gauge Higgs theories with multicomponent (Nf>1) degenerate scalar fields and U(Nf) global symmetry, focusing on systems with Nc=2, to identify critical behaviors that can be effectively described by the corresponding 3D SU(Nc) gauge Higgs field theory. The field-theoretical analysis of the RG flow allows one to identify a stable charged fixed point for large values of Nf, that would control transitions characterized by the global symmetry-breaking pattern U(Nf)→SU(2)⊗U(Nf−2). Continuous transitions with the same symmetry-breaking pattern are observed in the SU(2) lattice gauge model for Nf≥30. Here we present a detailed finite-size scaling analysis of the Monte Carlo data for several large values of Nf. The results are in substantial agreement with the field-theoretical predictions obtained in the large-Nf limit. This provides evidence that the SU(Nc) gauge Higgs field theories provide the correct effective description of the 3D large-Nf continuous transitions between the disordered and the Higgs phase, where the flavor symmetry breaks to SU(2)⊗U(Nf−2). Therefore, at least for large enough Nf, the 3D SU(Nc) gauge Higgs field theories with multicomponent scalar fields can be nonperturbatively defined by the continuum limit of lattice discretized models with the same local and global symmetries. Published by the American Physical Society 2024

Determination of key resource variables for a model of technological capability management in the alternative electricity generation industry of Colombia and Mexico, through a decision tree

The study focuses on identifying key variables to develop a model for managing technological capabilities in the alternative energy generation industry in Colombia and Mexico. The objective is to identify critical factors that influence strategic decisions in this sector. Researchers MsC Neider Duan Barbosa Castro, PhD PD Fabiola Sáenz Blanco, Dr. Francisco Zorrilla Briones, and MsC Evy Fernanda Tapias Forero contribute their expertise in technological capability management and university-industry collaboration.Advanced data mining techniques were used, integrating simulations and algorithms such as decision trees, to extract pertinent information and determine the most influential variables. Detailed Likert scale surveys were conducted in various power generation organizations in Colombia and Mexico, providing a comprehensive data set for analysis.The paper discusses the need for effective management of technological capabilities. It emphasizes strategic collaboration between internal and external stakeholders. The importance of adopting innovative strategies and using a multi-level analytical approach to managing technology in the power generation industry is highlighted. This iterative and cyclical approach enables dynamic adaptability to technological and market changes. It continuously identifies areas for improvement and new opportunities.The study's conclusions highlight the critical importance of innovation and market adaptation, as well as university-industry collaboration in the management of technological capabilities. The need for strategic and dynamic management to maintain a sustainable competitive advantage in the energy sector is evident. The integration of emerging technologies and operational flexibility are emphasized. In addition, the utility of data mining techniques in identifying and prioritizing key variables is highlighted. This provides a solid foundation for informed decision making in managing technological capabilities.

Multiscale optoacoustic assessment of skin microvascular reactivity in carotid artery disease

Microvascular endothelial dysfunction may provide insights into systemic diseases, such as carotid artery disease. Raster-scan optoacoustic mesoscopy (RSOM) can produce images of skin microvasculature during endothelial dysfunction challenges via numerous microvascular features. Herein, RSOM was employed to image the microvasculature of 26 subjects (13 patients with single carotid artery disease, 13 healthy participants) to assess the dynamics of 18 microvascular features at three scales of detail, i.e., the micro- (<100 μm), meso- (≈100–1000 μm) and macroscale (<1000 μm), during post-occlusive reactive hyperemia challenges. The proposed analysis identified a subgroup of 9 features as the most relevant to carotid artery disease because they achieved the most efficient classification (AUC of 0.93) between the two groups in the first minute of hyperemia (sensitivity/specificity: 0.92/0.85). This approach provides a non-invasive solution to microvasculature quantification in carotid artery disease, a main form of cardiovascular disease, and further highlights the possible link between systemic disease and microvascular dysfunction.

Stigma assessment scale for adolescent idiopathic scoliosis: A new tool for spine deformity stigma evaluation.

Stigma is the negative attitudes towards an illness or difference. The literature lacks detailed scales specifically addressing stigmatization in AIS. This study aimed to develop Adolescent Idiopathic Scoliosis Stigma Assessment Scale (AIS-SAS) to measure stigmatization in AIS patients. In creating the AIS-SAS, we developed items via literature review and expert input, confirmed content validity, and conducted a field test for construct validity and reliability. Our experts designed 38 items in three subscales: Scoliosis, Brace, and Surgery-related stigma. Exploratory factor analysis (EFA) and convergent validity were examined for construct validity. The convergent validity of AIS-SAS was assessed through its correlations with the Rosenberg Self-Esteem Scale (RSES), SRS-22, and Spinal Appearance Questionnaire (SAQ). Reliability was gauged via internal consistency and test-retest methods. Sixty-seven adolescents with AIS were included in the study. EFA results confirmed one-dimensionality for each subscale, with factor loadings from 0.51 to 0.96, leading to the removal of 8 items with loadings below 0.3 from the AIS-SAS. The final AIS-SAS has three subscales: scoliosis (18 items), brace (5 items), and surgery-related stigma (7 items). Regarding convergent validity, the scoliosis-related stigma scores significantly correlated with SRS-22 self-image (r= -0.413 p = 0.001) and SAQ-Appearance (r = 0.441 p = 0.002). The RSES-Total score shows a negative correlation with Scoliosis, Brace, and Surgery-related stigma scores: -0.500 (p < 0.001), -0.435 (p = 0.049), and -0.498 (p = 0.042), respectively. AIS-SAS subscales showed good reliability (Cronbach-α > 0.8 ICC > 0.8). AIS-SAS is the first valid and reliable high-quality scale assessing stigmatization from AIS, measuring scoliosis, brace, and surgery-related stigma via three subscales.

Conceptual Model of Graph-based Individual Tree and Its Utilization in Digital Twin and Metaverse of Urban Forest

Abstract. 3D city models are an important cornerstone in the development of digital twin cities, allowing for various analyses and simulations. CityGML, as an open standard for 3D city models, emphasizes five main aspects: scale or level of detail (LoD), semantics, geometry, topology, and appearance. One of the important elements in CityGML 3D city models is representation of vegetation. Vegetation in CityGML, especially individual trees, is still limited in terms of detail and resolution, reducing its effectiveness for specific applications. This paper proposes a graph-based conceptual model for individual trees that conforms to the CityGML v2.0 specification. The model refers to the morphological structure of a tree consisting of roots, trunk, and crown (including branches, twigs, and leaves), and considers the five main aspects of CityGML. By enhancing semantic information and geometric details, the model aims to provide an information-rich and realistic representation of individual trees in a 3D city environment. The 3D tree model created based on this conceptual model may be applicable in the development of digital twin urban forests and virtual forest simulations that utilize immersive technologies such as the metaverse. This research opens up new opportunities in the development of solitary vegetation objects through CityGML ADE by including more detailed semantic and topological information.

Enhancing forensic applications through the mineralogy of clays in surface soil horizon: A case study of baixada fluminense, Southeast Brazil

Clay minerals possess chemical, mineralogical, and crystallographic characteristics that make them potential forensic markers, as they can reflect geochemical and weathering processes on a detailed scale. Therefore, this study aims to use the mineralogy of the clay fraction to distinguish samples from the surface soil horizons in four municipalities in Brazil, to assess the possibility of geographical tracking for forensic applications. Studies characterizing clays in environments with a variety of soil types in forensic pedology are necessary but still in their early stages. Furthermore, due to the high incidence of homicides involving the displacement of bodies in the region and the low resolution of these crimes, this research is crucial for geoscientific applications, with the potential for global replication. The samples were separated by centrifugation and analyzed using X-ray diffractometry, X-ray fluorescence, and thermogravimetry. Minerals were determined through quantitative mineralogical analyses using Rietveld and Biscaye methods. Additionally, in conjunction with the crystallographic parameters of kaolinite (Full Width at Half Maximum – FWHM), it was possible to form 8 Clay Mineral Assemblages and Gibbsite (CMAG), whose geographical delimitation considered geological and hydrographic aspects. The determination of CMAGs is based on mineralogy and is defined as follows: CMAG 1: kaolinite+(illite); CMAG 2: kaolinite+(gibbsite); CMAG 3: kaolinite + illite + montmorillonite; CMAG 4: kaolinite + illite+(gibbsite); CMAG 5: ordered kaolinite+(illite)+(gibbsite); CMAG 6: kaolinite + gibbsite+(illite); CMAG 7: disordered kaolinite + illite + gibbsite; CMAG 8: kaolinite+(illite)+(chlorite). The results indicate that the quantification of clay minerals in samples from surface soil horizons, FWHM, and the % of Al in goethite can represent valuable tools for forensic pedology. Moreover, through the characterization of iron and titanium (hydr)oxides, it was possible to distinguish samples with a distance of 335 m between them, further expanding the capability to identify the geographical origin of samples, making them potential forensic markers.

Reference vegetation for restoration? Three vegetation maps compared across 76 nature reserves in Uganda and Kenya

AbstractForest and landscape restoration are increasingly popular nature‐based solutions to mitigate climate change and safeguard biodiversity. Restoration planning and monitoring implies that a reference ecosystem has been defined to which the restored site can be compared, but how to best select such reference? We tested three different potential natural vegetation (PNV) maps of the same areas in Kenya and Uganda for their utility as ecological references with independent data that were not used when those maps were made. These independent datasets included presence observations of woody species from 76 sites in forest reserves in Kenya and Uganda, and classification of surveyed species into a system that included “forest‐only” and “nonforest‐only” ecological types. Our tests show that (1) the three vegetation maps largely agree on the environmental envelopes/ranges within which forests occur. (2) There are large differences in how well the maps predict the presence of forest‐only species. (3) Two maps, based on empirical observations (V4A and White), predict forest types well, whereas the third, based on climate envelopes only (NS), performs poorly. (4) A large area in Uganda is potentially in one of two alternative stable states. We conclude that it is possible to evaluate the utility of PNV maps at a more detailed scale than the level of biome and ecoregion. This indicates that it is possible to map PNV at scales required for reference for restoration and management of forest vegetation. We recommend that empirically based maps of potential natural vegetation are used in restoration planning (biome and PNV maps based on climate envelopes alone may be unreliable tools) as a baseline model for predicting the distribution of reference ecosystems under current and future conditions. It could conveniently be done by deconstructing the existing biome maps, supported by rapid botanical surveys.

Nowcasting Floods in Detailed Scales Considering the Uncertainties Associated with impact-based Practical Applications

Nowcasting Floods in Detailed Scales Considering the Uncertainties Associated with impact-based Practical Applications

Картографирование почв на основе аэрофотосъемки с БПЛА

The article presents the results of research on the creation of a detailed soil map (scale 1:2500) using the example of a land plot of the Federal State Budgetary Educational Institution of Higher Education Perm State Agrarian University. The purpose of the research is to improve the methodology of detailed soil mapping using an unmanned aerial vehicle (UAV) and methods of geoinformation analysis of aerial photographs. The total area of the plot is 13.4 hectares, on which a UAV survey was carried out on September 12, 2023, and a soil survey was carried out on a detailed scale from June 1 to 24, 2024. Soil mapping was carried out on the basis of field data and a digital elevation model created by photogrammetry methods based on UAV survey materials. The map creation process is automated by creating geoprocessing models in QGIS model builder. Based on the results of creating a detailed soil map, a conclusion was made about the close relationship of soils with characteristic relief elements, which are reflected in classes according to the topographic position index TPI. Keywords: UAV, GEOINFORMATIONAL MAPPING, SOIL MAP

Photographic scale for the characterization of actinic keratosis through reflectance confocal microscopy: a quantitative approach to cellular transformation.

Actinic keratosis (AK) is a highly prevalent pre-cancerous skin lesion that often leads to cutaneous squamous cell carcinoma. There are different stages of evolution of the disease and several features that characterize keratosis. This study aimed to develop a qualitative and quantitative visual diagnostic tool to facilitate the identification of the characteristics and severity of the main cellular attributes of AK and to show its applicability in evaluating the evolution or treatment through image analysis. Literature research on the main scientific databases and in the institute's database was carried out to gather all the different levels of cellular transformation. To validate the scale, a preliminary characterization study was carried out with 21 subjects who had clinically diagnosed AK lesions to classify the attributes in each skin layer and test the accuracy of the diagnosis of the scale. Afterward, and to show the possibility of a follow-up with a topical treatment, the subjects were divided into two treatment groups, receiving either a cream formulation containing retinoic acid, or a placebo formula. The evaluation was carried out through confocal reflectance microscopy and a digital camera with dermoscopic quality before and after 90 days of treatment. A table detailing the 18 attributes of AK, and a photographic scale containing RCM images graded by scores established for each characteristic and the frequency of spreading were developed. The results of the validation presented good repeatability, correlation with clinical evaluation, and capacity for differentiating treatments demonstrated by the significant improvement after topical treatment by the reduction of the score for 10 out of the 18 attributes. The preliminary study, evaluated by the detailed transformation scale highlights important differences in the subclinical approach that allows a deeper evaluation of the aspects of the lesion's re-incidence even after fully treated skin sites. This study brings an innovative method based on RCM, to assist in the quantification of cell transformation level, provide early diagnosis, and deliver a powerful treatment evaluation tool to provide smoother treatment, as well as prevent re-incidence in the cases.

The Study of Environmental Exposure of Mothers and Infants Impacted by Large-Scale Agriculture (SEMILLA): Description of the Aims and Methods of a Community-Based Birth Cohort Study.

Women of childbearing age not only reside in agricultural communities but also form an integral part of the agricultural labor force. Limited research investigates the impact of prenatal fungicide exposure on infant health, specifically ethylenebisdithiocarbamates and their toxic by-product, ethylenethiourea (ETU), particularly in occupational settings. This paper describes the background, aims, protocol, and baseline sample characteristics for the SEMILLA study, which investigates prenatal ETU exposure, neonatal thyroid function, infant growth, and neurobehavioral development in an agricultural region of Ecuador. This cohort study follows pregnant women and their infants up to 18 months of age, incorporating urinary biomarkers and survey data on ETU exposure and infant growth and neurodevelopmental measures. Data collection includes detailed questionnaires, scales, and physical examinations on maternal and infant health and development, as well as environmental factors. Descriptive statistics on key characteristics of the study population at baseline are presented. SEMILLA enrolled 409 participants (72% enrollment rate): 111 agricultural workers (mostly floricultural), 149 non-agricultural workers, and 149 non-workers. Baseline characteristics show comparability between work sector groups, with some economic differences. SEMILLA will provide key evidence on prenatal fungicide exposure and infant development and encompass comprehensive multistage data collection procedures in pregnancy and infancy, focusing on structural and social determinants of health as well as individual-level chemical exposures. The community-based approach has proven essential, even amid challenges like the COVID-19 pandemic. The medium-term objective is to inform sustainable interventions promoting maternal and child health, with a long-term goal to reduce community exposures and improve worker health policies, particularly for women and pregnant workers.

Runoff Coefficient in the Air Bengkulu Watershed and the Evaluation of the Existing Spatial Planning

Runoff coefficient plays a crucial role in estimating the peak discharge of a river basin. Therefore, this research aimed to investigate runoff coefficient in the Air Bengkulu watershed based on temporal land cover data and evaluate the existing spatial planning. Land cover data from 1998, 2002, 2016, and 2023, with spatial patterns derived from current regional planning were used. The temporal and spatial pattern-based runoff coefficients were determined using land cover data and spatial pattern function, respectively. Meanwhile, descriptive and comparative methods were adopted based on time. The calculated runoff coefficient was 0.073, 0.093, 0.276, and 0.273 for 1998, 2002, 2016, and 2023, while the value obtained based on spatial patterns was 0.306. Additionally, a general trend of increasing the values over time was observed. Land cover change, particularly the decline of forest areas and the expansion of settlement and plantation, contributed to the rising runoff coefficient. The results showed that runoff coefficient (0.306) exceeded the current land cover, similar to other analyses with higher runoff in the predicted scenario. This research suggested a need for a more detailed classification system and scale to accommodate land cover types with relatively low runoff coefficient. In risk assessment, land cover-like spatial patterns with low runoff coefficient should be placed as capacity other than vulnerability components.

On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

Charting Competence: A Holistic Scale for Measuring Proficiency in Artificial Intelligence Literacy

The rapid evolution of AI technologies has reshaped our daily lives. As AI systems become increasingly prevalent, AI literacy, the ability to comprehend and engage with these technologies, becomes paramount in modern society. However, existing research has yet to establish a comprehensive framework for AI literacy. This study aims to fill this gap by developing a holistic AI literacy scale. Three levels of dimensions are considered: individual, interactive, and sociocultural. The scale includes cognitive, behavioral, and normative competencies. After rigorous reliability and validity assessments, the final AI literacy scale comprises six dimensions: AI features, AI processing, algorithm influences, user efficacy, ethical consideration, and threat appraisal. Detailed scale development, validation, and dimension-specific items are thoroughly explained. This comprehensive scale equips individuals with the competencies needed to navigate and critically engage with AI in today’s multifaceted AI landscape.

How building arrangements can improve outdoor thermal comfort and indoor sunlight?

In the context of frequent extreme hot weather conditions, high-dense urban areas posed more life-threatening challenges. Such as university dormitories, as important residential spaces, have received very little attention. Moreover, how building arrangements can simultaneously impact internal and external environments remain poorly understood. To bridge this gap, student residential areas of Jiangsu University in Zhenjiang, China were considered as a case study, with the aim of optimal building arrangements that can simultaneously improve the outdoor thermal comfort (OTC) and indoor sunlight (ISL). A combination of on-site measurement and numerical models were employed to simulate various scenarios with different building orientations, building heights and layouts. The result revealed that: first, the building orientation emerged as a dominant factor influencing both OTC and ISL, with the optimal value identified as SE-30°. Second, in the context of extremely hot weather conditions, increasing building height or floor-area ratio contributed to improved OTC. Finally, the building arrangement with inconsistent heights of north-to-south descending order, exhibited a notable reduction in hot outdoor spaces by 18.6%, and attributed to enhanced airflow. The findings provide clear guidelines for improved strategies at the detailed urban design scale, and can further contribute to the development of sustainable cities.

A global-scale applicable framework of landslide dam formation susceptibility

The formation and failure of landslide dams is an important and understudied, multi-hazard topic. A framework of landslide dam formation susceptibility evaluation was designed for large-scale studies to avoid the traditional dependence on landslide volume calculations based on empirical relationships, which requires comprehensive local inventories of landslides and landslide dams. The framework combines logistic regression landslide susceptibility models and global fluvial datasets and was tested in Italy and Japan based on landslide and landslide dam inventories collected globally. The final landslide dam formation susceptibility index identifies which river reach is most prone to landslide dam formation, based on the river width and the landslide susceptibility in the adjacent delineated slope drainage areas. The logistic regression models showed good performances with area under the receiver operating characteristics curve values of 0.89 in Italy and 0.74 in Japan. The index effectively identifies the probability of landslide dam formation for specific river reaches, as demonstrated by the higher index values for river reaches with past landslide dam records. The framework is designed to be applied globally or for other large-scale study regions, especially for less studied data-scarce regions. It also provides a preliminary evaluation result for smaller catchments and has the potential to be applied at a more detailed scale with local datasets.

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the &lt; 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the &gt; 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the &gt; 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).