Mixing Region Research Articles

Zwilch kinetochore protein affects the prognosis of cancer patients by participating in cell proliferation, enhancing cell communication, and reshaping the tumor microenvironment

BackgroundZwilch Kinetochore Protein(ZWILCH) has been reported to prevent cells from prematurely exiting mitosis. However, the underlying mechanisms or involvement of ZWILCH in the tumor immune microenvironment in various cancers remain largely unknown.MethodsGeneralized dysregulation of ZWILCH was observed through the whole transcriptome analysis in this study. The spatial transcriptome analysis was utilized to identify expressed regions of ZWILCH. Next, cells that mainly expressed ZWILCH in the tumor microenvironment were determined using the single-cell transcriptome analysis. Also, the “cellchat” R package was applied to estimate the effect of ZWILCH on malignant cell communication. Combining multiple analytic approaches including GSEA, GSVA, KEGG enrichment analysis, and Aucell, with TCPA functional protein data, Genome-wide CRISPR screening, potential functions of ZWILCH and the pathways in which ZWILCH participated were thoroughly exploited. Univariate Cox regression analysis calculated the association between ZWILCH and cancer patients’ adverse outcomes.ResultsZWILCH is universally highly expressed in tumors. The spatial transcriptome analysis showed that ZWILCH overexpression comes from the tumoral region or mixed tumoral region. At the single-cell level, ZWILCH is chiefly expressed by malignant cells and proliferative T cells. The expression of ZWILCH mRNA is positively correlated with cell proliferation, repair of DNA damage, and cell cycle score. Plenty of metabolic pathways are inhibited in patients with high expression of ZWILCH. Moreover, after ZWILCH knockout, a large number of cancer cell lines are stagnated, inhibited, or died. Additionally, the malignant cells with positive expression of ZWILCH have a stronger ability for cell communication. In short, ZWILCH is meant to be a risk factor for clinical outcomes of multiple tumors.ConclusionsZWILCH is a promising therapeutic target that influences patient prognosis by participating in cell proliferation, cell communication, and reshaping the tumor microenvironment across different cancers.

Water mass mixing controls methane cycling and emission in highly hydrodynamic regions of the open ocean

Abstract Ocean circulations and water mass exchange can exert significant influence on seawater biogeochemistry, microbial communities and carbon cycling in marine systems. However, the detailed mechanisms of how physical processes impact the cycle of greenhouse gases, particularly methane, remains poorly understood in the open ocean. Here, we integrated high-resolution underway observation, experimental incubations, radioisotope labelling and molecular analysis to constrain the controls of methanogenic pathways, methanotrophic activity and emission fluxes in the highly hydrodynamic Kuroshio and Oyashio Extension (KOE) region of the Northwest Pacific. The mixing of high-temperature, nutrient-rich Kuroshio waters with methane-rich Oyashio currents not only significantly affected methane abundance, but also methane production pathways and oxidation rates. Water mass mixing caused the changes in dominance of phytoplankton communities to Bacillariophyta with less production of the methane precursor dimethylsulphoniopropionate (DMSP), thus reducing DMSP-dependent methanogenesis. The alteration of nutrient level due to mixing of Kuroshio and Oyashio at KOE also likely affected microbial utilization of dissolved organic phosphorus, thus influencing methane production from the C − P cleavage of methylphosphonate. Furthermore, the abundance of methanotrophs, such as Methylocystis and Methylosinus, were much higher at the KOE sites than the Oyashio Extension, which contributed to the elevated methane oxidation (MOx) rates in mixing region. Microbial oxidation as a biological sink of methane accounted for ~43.7 ± 28.8% of the total methane loss, reducing methane emissions to the atmosphere. These data highlight the physical controls on biogeochemical methane cycling, implying that intensive mixing of water masses can regulate methane emissions from the open oceans.

Probing the cores of subdwarf B stars: How they compare to cores in helium core-burning red giants

The mixing of material from stellar convective cores into their adjacent radiative layers has been a matter of long-standing debate. Pulsating subdwarf B stars offer excellent conditions to advance our understanding of this problem. In this work we use a model-independent approach to infer information about the cores of three subdwarf B stars and compare it with similar inferences from an earlier analysis of red giants in the helium core-burning phase. This is achieved by fitting an analytical description of the gravity-mode pulsation periods to pulsation data collected by the Kepler satellite. From the fits we infer the reduced asymptotic period spacings and the amplitude and position of sharp structural variations associated with chemical discontinuities in the stellar interiors. Our results indicate the presence of sharp structural variations with similar properties in all three stars, located near the edge of the gravity-mode propagation cavity and likely associated with the C-O/He transition. We find that these structural variations differ systematically from those of helium core-burning red giant stars, having larger amplitudes and being located at a larger buoyancy radius. This suggests that chemical mixing beyond the adiabatically stratified core into the radiatively stratified layers may be more extensive in subdwarf B stars than in helium core-burning red giants. Alternatively, the stratification of the mixing region beyond the adiabatically stratified core may differ significantly between the two types of stars. The model-independent constraints set on the structural variations inside these three stars are the first of a kind and will be key to enhancing the modelling of layers adjacent to stellar convective cores and to testing non-canonical stellar evolution channels leading to the formation of hot subdwarf stars.

Enhancing Land Cover Classification: Fuzzy Similarity Approach Versus Random Forest

This study presents a comparative analysis of two advanced classification techniques applied to Landsat 8 and Sentinel-2 imagery. The first technique is based on the combined use of Tversky’s fuzzy similarity and Mamdani-type fuzzy inference, specifically designed to handle transition zones—areas characterized by gradual shifts in land cover, such as from vegetation to suburban environments. The second approach is based on the Random Forest algorithm. After performing the ranking of spectral, textural, and geometric features using the fuzzy approach, a fuzzy system based on Tversky’s fuzzy similarity was developed. This system enables a more adaptive and nuanced classification of different land cover classes, including water bodies, forests, and cultivated areas. The results indicate that the proposed fuzzy approach slightly outperforms the Random Forest method in handling mixed land cover regions and reducing classification uncertainties, achieving overall accuracies of 98.5% for Sentinel-2 and 96.7% for Landsat 8.

HGAN: monocular 3D object depth completion method via hierarchical geometric-aware network

Abstract Accurate and dense scene depth perception is critical for applications such as autonomous driving and robotic navigation. However, due to the limited geometric cues provided by inherently sparse depth data acquired from sensors, significant challenges remain in completing depth information by integrating monocular RGB images to reconstruct object depth in a coherent 3D space. Traditional data augmentation strategies lack geometric awareness, often causing depth discontinuities at the foreground-background boundaries, leading to edge blurring and artifacts that distort geometric relationships in mixed regions. Additionally, mainstream depth estimation frameworks focus too much on global features, making it difficult to model the complex spatial relationships between foreground objects and the background, resulting in the loss of foreground details and ambiguity in background depth. To address these challenges, we propose a hierarchical geometric-aware depth completion network (HGAN) that consists of two key modules: the geometric consistency-aware enhancement module (GCAM) and the geometric relationship decomposition modeling module (GRDM). Specifically, the GCAM constructs a geometric consistency map between the foreground and background regions based on depth similarity and employs adaptive weights to guide foreground-background feature fusion. This enhances the boundary modeling capabilities, significantly improving the structural clarity and continuity of the depth map. The GRDM introduces a geometric relationship decomposition mechanism that explicitly separates depth feature mapping into two orthogonal subspaces: Range Space and Null Space. The Range Space models global scene consistency constraints, ensuring the structural coherence of depth estimation, whereas the Null Space focuses on reconstructing local residual details, effectively enhancing the perception of foreground object edges and fine details. The experimental results show that our method outperforms previous approaches in terms of both efficiency and accuracy on the KITTI and NYU-Depth V2 datasets, with HGAN reducing RMSE by 7.5% on KITTI and 2.3% on NYUv2 compared to CompletionFormer.

Segment-Weighting Similarity-Based Fragment-Learning Model for Single-Cell Raman Spectral Analysis.

Raman spectroscopy provides intrinsic biochemical profiles of all cellular biomolecules in a segmented manner, promising nondestructive and label-free phenotyping at the single-cell level. However, current analytical methods rarely utilize spectral biological characteristics and their fusion with data characteristics, limiting the application of these methods to biological Raman spectroscopy. Herein, a segment-weighting similarity-based fragment-learning (SWS-FL) model, integrating SWS-based feature extraction and fusion learning, is proposed to fuse biological and data characteristics for single-cell spectral analysis, which segments spectra into fragments and differentiates their biological characteristics for fusing feature matrices. The SWS-based feature extraction fabricates a group of low-dimensional feature vectors at multiple N values, providing a more distinguishable feature space compared to conventional KNN. The weights of five fragments, including the fingerprint region, protein I region, mixed region, protein II region, and genetic material region, are assigned as 0.282, 0.302, 0.273, 0.276, and 0.239, respectively, which highlights the spectral biological characteristics. The fusion learning process synthesizes characteristics from all spectral fragments using an ANN, achieving accuracy with only 0.5% variation across N values from 1 to 30, greatly enhancing the robustness of the model. In the five-classification task of breast cancer cells and their subtypes, the accuracy and kappa coefficient of SWS-FL can reach 94.9% and 0.943%, respectively, which are 5% and 7% higher than those of ANN. The generalization capability is also validated on the data set of lung cancer cells and their subtypes. This model provides a new path for the fusion of biological and data characteristics in spectral analysis and promises to be a powerful analytical framework in more spectroscopic areas.

Modelling the evolution of Richtmyer–Meshkov mixing width during shock compression phases

Turbulent mixing driven by the reshocked Richtmyer–Meshkov (RM) instability plays a critical role in numerous natural phenomena and engineering applications. As the most fundamental physical quantity characterizing the mixing process, the mixing width transitions from linear to power-law growth following the initial shock. However, there is a notable absence of quantitative models for predicting the pronounced compression of initial interface perturbations or mixing regions at the moment of shock impact. This gap has restricted the development of integrated algebraic models to only the pre- and post-shock evolution stages. To address this limitation, the present study develops a predictive model for the compression of the mixing width induced by shocks. Based on the general principle of growth rate decomposition proposed by Li et al. (Phy. Rev. E, vol. 103, issue 5, 2021, 053109), two distinct types of shock-induced compression processes are identified, differentiated by the dominant mechanism governing their evolution: light–heavy and heavy–light shock-induced compression. For light–heavy interactions, both stretching (compression) and penetration mechanisms are influential, whereas heavy–light interactions are governed predominantly by the stretching (compression) mechanism. To characterize these mechanisms, the average velocity difference between the extremities of the mixing zone is quantified, and a physical model of RM mixing is utilized. A quantitative theoretical model is subsequently formulated through the independent algebraic modelling of these two mechanisms. The proposed model demonstrates excellent agreement with numerical simulations of reshocked RM mixing, offering valuable insights for the development of integrated algebraic models for mixing width evolution.

Multi-view semi-supervised attention network for 3D cardiac image segmentation

In recent years, semi-supervised methods have been rapidly developed for three-dimensional (3D) medical image analysis. However, previous semi-supervised methods for three-dimensional medical images usually focused on single-view information and required a large number of annotated datasets. In this paper, we innovatively propose a multi-view (coronal and transverse) attention network for semi-supervised 3D cardiac image segmentation. In this way, the proposed method obtained more complementary segmentation information, which improved the segmentation performance. Simultaneously, we integrated the CBAM module and adaptive channel attention block into the 3D VNet (CBAP - VNet) to enhance the focus on the segmentation regions and edge portions. We first introduced the CutMix data augmentation mechanism to enhance 3D cardiac medical image segmentation. In this way, the proposed method made full use of the mixed regions in the images and expanded the training dataset. Our method was tested on two publicly available cardiac datasets and achieved good segmentation results. Our code and models are available at https://github.com/HuaidongLi-NEFU/TPSSAN.

Global attention focused on difficult land and ship regions for ship detection in SAR images

ABSTRACT Detecting rotating ship targets in synthetic aperture radar (SAR) images is a challenging task; not only do the complex nearshore backgrounds and noise hinder the accuracy of deep learning-based detection methods, but the additional introduction of angular information further increases the complexity of the detection task. In this paper, we propose a global attention mechanism for difficult regions that aims to improve the accuracy when detecting rotating ship targets in SAR images. The core of the approach is two innovative components: the Sea Background Coarse Segmentation Module (SBCSM) and the Land Ship Attention Module (L-SAM). The SBCSM effectively isolates the broad, homogeneous sea surface from the more complex land-ship target regions, while the L-SAM utilizes the Transformer’s global information processing capabilities to further identify targets within these mixed regions. This approach ensures that computational resources are focused on complex regions, allowing L-SAM to be applied to detailed, larger-scale features for more accurate detection. The superiority of our approach is demonstrated by experimental evaluations on three publicly available datasets. The state-of-the-art A P 50 (average precision at intersection over union equal to 0.5) of 0.920, 0.873 and 0.952 is achieved on RSDD-SAR, HRSID, and SSDD, respectively.

Study on interaction mechanism between natural convection and forced convection during storage and temperature rise of waxy crude oil tank

As the primary facility for crude oil storage, storage tanks play a critical role in achieving high operational efficiency and low energy consumption through comprehensive understanding of complex thermal convection mechanisms. This study establishes a theoretical model for the coil-agitator synergistic heating process in crude oil storage tanks, characterizing the coupled heat transfer between natural and forced convection. Dimensionless parameters including Reynolds number, Grashof number, and Richardson number are employed to quantitatively delineate the agitator-induced forced convection zone, crude oil natural convection zone, and mixed convection region. Based on the spatial distribution characteristics of multi-scale turbulent vortex structures within these zones, the interaction mechanisms between natural and forced convection are qualitatively analyzed, while the Richardson number is used to quantitatively characterize the primary influencing factors of vortex flow in each convection region. The results indicate that the tank's heat transfer is dominated by coil-induced natural convection with supplementary agitator-driven forced convection. A 2.45 m² forced convection zone forms near the agitator. Reduced agitator rotation angle generates high-speed vortex flow along tank walls, forming large-scale vortex structures with enhanced intensity. This expands the forced convection zone by 55% and mixed convection zone by 73%, prolonging forced convection trajectories while improving natural convection heat exchange, albeit causing localized non-uniformity. Multi-scale analysis reveals that in the natural convection zone, large vortices (≥34.08 m) govern energy transport via macroscopic convection, while small-scale vortices (≤11.99 m) facilitate energy conversion through viscous dissipation. In forced convection regions, 30° agitator rotation optimally develops large vortices (≥8.52 m), enhancing vortex intensity while reducing energy dissipation. Furthermore, Richardson number analysis shows that large vortices (Ri ∈ [0, 10]) in the forced convection zone primarily enhance convective heat transfer, whereas small-scale turbulent vortices (Ri ∈ [0, 1]) contribute to mixing and heat transfer through localized energy dissipation.

One-dimensional mixing model of ablative Rayleigh–Taylor instability in direct-drive implosion

Abstract A one-dimensional (1D) mixing model, incorporating the effects of laser ablation and initial perturbations, is developed to study the influence of ablative Rayleigh–Taylor instability on compression dynamics. The length of the mixing region is determined with the buoyancy-drag model (2024 arXiv:2411.12392v2). The mixing effect on laser ablation is mainly described with an additional heat source which depends on turbulent kinetic energy and initial perturbation level through a free multiplier. The model is integrated into a 1D radiation hydrodynamics code and validated against two-dimensional planar simulations. The relative errors of the model for quantifying compression remain below 10%. The further application of our model to spherical implosion simulations reveals that the model can give reasonable predictions of implosion degradation due to mixing, such as lowered shell compression, reduced stagnation pressure, and decreased areal density, etc. It is found that the time interval between the convergence of the main shock and stagnation may offer an estimate of mixing level in single-shot experiments.

Heterogeneous Mixing Processes Observed in the Dotson Ice Shelf Outflow, Antarctica

Abstract We present the first observations of ocean turbulent mixing rate in front of the Dotson Ice Shelf, where meltwater‐enriched water leaves the cavity. The observations showed elevated turbulent kinetic energy dissipation rates (; ∼10−7 W kg−1) and turbulent diapycnal diffusivities (; ∼10−2 m2 s−1) near the seabed and in middepth layers, which are three orders of magnitude above background values away from the outflow. Elevated diapycnal fluxes of heat and salt were observed in regions of high mixing, moving vertically on average O ∼ 10 W m−2 and O ∼ 10−6 kg m−2 s−1, respectively, toward shallow depths. At middepth layers, the overturning instabilities are characterized by shear‐driven symmetric and centrifugal instabilities. Our observations provide an understanding of mixing in front of fast‐melting ice shelves and are key to developing better parameterizations and representations of mixing in climate models.

Hard-seeded annual pasture legume phases are a profitable and low risk option in mixed farming regions with low to medium rainfall

Hard-seeded annual pasture legume phases are a profitable and low risk option in mixed farming regions with low to medium rainfall

Seasonably Variable Estuarine Exchange Through Interconnected Channels in the Salish Sea

Abstract The Salish Sea is a semi‐enclosed estuary whose largest basin (Strait of Georgia (SoG)) is connected to the north‐eastern Pacific Ocean through regions with tight constructions and sills that cause intense tidal mixing. The estuarine circulation is complicated through the tidally mixed region around the San Juan and Gulf Islands, which consists of three different straits: Haro Strait, Rosario Strait, and San Juan Channel (SJC). Haro Strait, as the largest and deepest of the channels, is the dominant pathway; however, we determine that Rosario Strait also has an important influence in this region. To examine the differences in water transport through the different channels, Lagrangian particle tracking experiments were performed for a 4‐year hindcast (from 2018 to 2022) using the 3‐dimensional numerical model SalishSeaCast. While Haro Strait has southward surface flow and northward deep flow, the flux making it through Rosario Strait and SJC is primarily southward. The proportion of the total southward flux through these two channels is higher from May to October and this increase is attributed to the influence of both the Fraser River and the river discharge from Puget Sound. Rosario Strait is the dominant pathway of southward exchange from the SoG to Puget Sound, while the majority of deep northward flux to the SoG is through Haro Strait.

Regional Characteristics in Ultradeep MXene Slit Nanopores: Insights from Molecular Dynamics Simulation.

The presence of slit nanopores in MXene materials inevitably influences the electrochemical performance of supercapacitor electrodes. However, most studies focus on experimental approaches, lacking microscopic-scale analysis. Here, we performed molecular dynamics (MD) simulations to thoroughly analyze and predict the ion transport pathways and energy storage mechanisms of MXene/ionic liquid (IL) supercapacitors with ultradeep slit nanopores. The simulation results indicate that during the charging process, counterions migrate from the bulk region into the electrode pores, forming a counterion layer near the electrode surface, while some co-ions gradually exit the pores. When charging is completed, a distinct ion layering structure emerges. As the interlayer spacing varies, the ion distribution in the electrode pores exhibits regional characteristics: in the ordered region near the bulk region, a stable electrical double-layer (EDL) structure is maintained, whereas in the deeper mixed region, persistent co-ion presence and significant disorder are observed. Dominated by the mixed region, the total energy variation of the electrode decreases as the interlayer spacing decreases, with energy changes of 8526.52, 7443.52, and 6640.99 kJ·mol-1 at interlayer spacings of 1.2, 1.0, and 0.8 nm, respectively, representing reductions of approximately 12.7 and 22.1% compared to 1.2 nm. In the mixed region, after compensating for the interaction between counterions, the contribution of the interaction between counterions and electrode increases with decreasing interlayer spacing, reaching 123, 153, and 176% at 1.2, 1.0, and 0.8 nm, respectively. An increasing amount of energy is offset by interactions related to the co-ions, ultimately leading to the observed energy differences. These findings offer new insights into the impact of nanopore structure on supercapacitor performance and provide theoretical guidance for optimizing electrode design.

Microcellular Foam from Ethylene Vinyl Acetate/Shear-Stiffening Gel for Advancing Footwear Midsole Development.

Ethylene-vinyl acetate (EVA) is widely used in shoe soles due to its foamability, affordability, and resilience. However, rising consumer demands for greater durability and energy rebound drive the need for performance enhancements. This study presents a novel approach by introducing a silicone-based masterbatch (MB), developed by incorporating a silicone-based shear-stiffening gel (SSG) (polyborosiloxane) with EVA through a reactive extrusion process. Two types of SSG/EVA are produced, i.e., crosslinked SSG/EVA (abbreviated as SSG/EVA-X) and non-crosslinked SSG/EVA (abbreviated as SSG/EVA-NX). The SSG/EVA-X reveals interconnected phases, including crosslinked and uncrosslinked SSG and EVA, and mixed regions. The developed SSG/EVA are further utilized as MB to modify and enhance EVA foam properties. Incorporating SSG/EVA MB into EVA foam significantly enhances its physico-mechanical properties. These improvements are pronounced in EVA foams incorporating with the SSG/EVA-X MB (abbreviated as EVA/MB-X), which outperforms the SSG/EVA-NX MB, (abbreviated as EVA/MB-NX) due to superior material integration. Dynamic impact energy return of EVA foam increases by over 10%, while abrasion resistance shows an improvement of more than 50% at the optimal SSG/EVA MB content. These findings suggest that crosslinking silicone MB with EVA presents a promising strategy for EVA foam modification, offering a pathway to enhance its performance.

Controls on Exchange through a Tidal Mixing Hotspot at an Estuary Constriction

Abstract Deep estuaries are often separated from the open ocean by sills and constrictions. These constrictions are areas of intense mixing often dominating the total estuarine mixing. Our example is the Salish Sea on the West Coast of North America with strong mixing through the Southern Gulf and San Juan Islands. The amount and depth of the estuarine exchange, and the tracers it carries, through these constrictions depend sensitively on the mixing and the densities of the waters on the two sides of the mixing region. Predicting the future of estuarine exchange in a given region requires a full understanding of the dynamics. Here, we use a Lagrangian tracking method that allows us to directly separate the exchange flow from the recirculating flows (the reflux and efflux). Separating the components of the estuarine flow simplifies the dynamics; 95% of the variance in the exchange flow through the tidal mixing region can be explained by the density difference across the region combined with a Richardson number based on the tidal velocities. The Lagrangian tracking is done on output from SalishSeaCast, a three-dimensional ocean model of the region. Using a 4-yr hindcast from the model, we determine the amount, depth, and position of the outflow and inflow. The direct separation of the estuarine components shows their four-dimensionality. Significance Statement The purpose of this study is to better understand how much water is exchanged between the open ocean and the coastal ocean. This exchange is important because the contents of the water from the open ocean impact the ecosystem of the coastal ocean. Our results show what controls this exchange, highlighting the importance of the density difference between the coastal and open ocean as well as the turbulence of the waters connecting them.

Decoupling of N2O Production and Emissions in the Northern Indian Ocean

Abstract The northern Indian Ocean is a hotspot of nitrous oxide (O) emission to the atmosphere. Yet, the direct link between production and emission of O in this region is still poorly constrained, in particular the relative contributions of denitrification, nitrification and ocean transport to the O efflux. Here, we implemented a mechanistically based O cycling module into a regional ocean model of the Indian Ocean to examine how the biological production and transport of O control the spatial variation of O emissions in the basin. The model captures the upper ocean physical and biogeochemical dynamics of the northern Indian Ocean, including vertical and horizontal O distribution observed in situ and regionally integrated O emissions of 286 152 Gg N (annual mean seasonal range) in the lower range of the observation‐based reconstruction (391 237 Gg N ). O emissions are primarily fueled by nitrification in or right below the surface mixed layer (57%, including 26% in the mixed layer and 31% right below), followed by denitrification in the oxygen minimum zones (30%) and O produced elsewhere and transported into the region (13%). Overall, 74% of the emitted O is produced in subsurface and transported to the surface in regions of coastal upwelling, winter convection or turbulent mixing. This spatial decoupling between O production and emissions underscores the need to consider not only changes in environmental factors critical to O production (oxygen, primary productivity etc.) but also shifts in ocean circulation that control emissions when evaluating future changes in global oceanic O emissions.

Super-resolution algorithms for imaging FCS enhancement: A comparative study.

Super-resolution algorithms for imaging FCS enhancement: A comparative study.

Spatial Distribution Pattern of Forests in Yunnan Province in 2022: Analysis Based on Multi-Source Remote Sensing Data and Machine Learning

Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making.