High Spatial-temporal Resolution Research Articles

Comparative analysis of multiple observation data during two typhoons affecting Hainan Island

Based on different observation data, the wind and rain processes of two typhoons that continuously affected Hainan Island in October 2021 were compared and analyzed. The analysis shows that the two typhoons are similar in weather conditions, water vapor transport conditions, cold air intrusion, sea surface temperature providing energy, and so on. The spatial distribution of rainfall and wind field exhibits an evidently asymmetrical structure. Before the typhoon landed, the gale area was located mainly in the northeast of the typhoon center. Furthermore, different detection data have advantages and disadvantages, which should be verified and used each other. For example, there are also differences between radar echo reflectivity, wind profile radar data and MTCSWA data. By means of analysis and research, the ability to analyze and articulate the typhoon impact process with high spatial-temporal resolution data is further enhanced, thereby aiding in the forecast and service work during the typhoon period.

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Abstract. Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a, https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3, https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9, and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d).

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields.

Small-Scale Biophysical Interactions and Dinophysis Blooms: Case Study in a Strongly Stratified Chilean Fjord

Diarrhetic shellfish poisoning (DSP) toxins and pectenotoxins (PTXs) produced by endemic species of Dinophysis, mainly D. acuta and D. acuminata, threaten public health and negatively impact the shellfish industry worldwide. Despite their socioeconomic impact, research on the environmental drivers of DSP outbreaks in the Chilean fjords is scanty. From 22 to 24 March 2017, high spatial–temporal resolution measurements taken in Puyuhuapi Fjord (Northern Patagonia) illustrated the short-term (hours, days) response of the main phytoplankton functional groups (diatoms and dinoflagellates including toxic Dinophysis species) to changes in water column structure. Results presented here highlight the almost instantaneous coupling between time–depth variation in density gradients, vertical shifts of the subsurface chlorophyll maximum, and its evolution to a buoyancy-driven thin layer (TL) of diatoms just below the pycnocline the first day. A second shallower TL of dinoflagellates, including Dinophysis acuta, was formed on the second day in a low-turbulence lens in the upper part of the pycnocline, co-occurring with the TL of diatoms. Estimates of in situ division rates of Dinophysis showed a moderate growth maximum, which did not coincide with the cell density max. This suggests that increased cell numbers resulted from cell entrainment of off-fjord populations combined with in situ growth. Toxin profiles of the net tow analyses mirrored the dominance of D. acuminata/D. acuta at the beginning/end of the sampling period. This paper provides information about biophysical interactions of phytoplankton, with a focus on Dinophysis species in a strongly stratified Patagonian fjord. Understanding these interactions is crucial to improv predictive models and early warning systems for toxic HABs in stratified systems.

Imaging hot photocarrier transfer across a semiconductor heterojunction with ultrafast electron microscopy

Semiconductor heterojunctions have gained significant attention for efficient optoelectronic devices owing to their unique interfaces and synergistic effects. Interaction between charge carriers with the heterojunction plays a crucial role in determining device performance, while its spatial-temporal mapping remains lacking. In this study, we employ scanning ultrafast electron microscopy (SUEM), an emerging technique that combines high spatial-temporal resolution and surface sensitivity, to investigate photocarrier dynamics across a Si/Ge heterojunction. Charge dynamics are selectively examined across the junction and compared to far bulk areas, through which the impact of the built-in potential, band offsets, and surface effects is directly visualized. In particular, we find that the heterojunction drastically modifies the hot photocarrier diffusivities in both Si and Ge regions due to charge trapping. These findings are further elucidated with insights from the band structure and surface potential measured by complementary techniques. This work demonstrates the tremendous effect of heterointerfaces on hot photocarrier dynamics and showcases the potential of SUEM in characterizing realistic optoelectronic devices.

Imaging and proteomics toolkits for studying organelle contact sites.

Organelle contact sites are regions where two heterologous membranes are juxtaposed by molecular tethering complexes. These contact sites are important in inter-organelle communication and cellular functional integration. However, visualizing these minute foci and identifying contact site proteomes have been challenging. In recent years, fluorescence-based methods have been developed to visualize the dynamic physical interaction of organelles while proximity labeling approaches facilitate the profiling of proteomes at contact sites. In this review, we explain the design principle for these contact site reporters: a dual-organelle interaction mechanism based on how endogenous tethers and/or tethering complexes localize to contact sites. We classify the contact site reporters into three categories: (i) single-protein systems, (ii) two-component systems with activated reporter signal upon organelle proximity, and (iii) reporters for contact site proteomes. We also highlight advanced imaging analysis with high temporal-spatial resolution and the use of machine-learning algorithms for detecting contact sites.

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Small unmanned aerial vehicle (UAV)-based detection of seasonal micro-urban heat islands for diverse land uses

ABSTRACT Metropolitan areas have diverse land uses (LUs), which can also cause significant differences in land surface temperature (LST), leading to the formation of micro-urban heat islands (MUHIs). Measuring the MUHIs is significant for heat mitiga-tion and adaptation measures and requires high spatial-temporal resolution, which is not feasible through coarser satellite observations (CSOs). Thermal cameras onboard unmanned aerial vehicles (UAVs) can detect such MUHIs because of their high spatial and desired temporal resolution. This study used the Zenmuse H20T onboard a UAV providing LST at ∼8 cm resolution to evaluate MUHIs in an area with diverse and contiguous LUs including three urban built-up LUs: 1) residential high cost (RHC), 2) residential low cost (RLC), 3) industrial area (IA) and one natural area (i.e. park area (PA)). The LST and MUHI were estimated in two seasons: fall (October 2022) and summer (June-July 2023). In each season, six flights were conducted at similar times of day. The findings were compared with Landsat in each season to examine the loss of information between coarser and finer spatial resolution. Using UAV, a maximum MUHI of 25.54 ◦ C and 15.85 ◦ C was identified in the summer and fall seasons, respectively, between 15:30 and 16:20. The maximum LST was observed in RHC, and PA showed the minimum LST in both seasons. Notably, dark-coloured roofs with asphalt shingle coating reported up to 25.78 ◦ C and 27.37 ◦ C higher LST (UAV-estimated) than light-coloured roofs in the fall and summer, respectively. Landsat significantly underestimated MUHI hotspots in the summer and fall seasons. The on-ground validation of the UAV showed better results in the summer season. The study shows the pragmatic use of UAVs to detect localized MUHIs. The findings are useful to devise strategies to mitigate MUHIs utilizing UAVs in the face of climatic and environmental changes.

Remote sensing monitoring of irrigated area in the non-growth season and of water consumption analysis in a large-scale irrigation district

Globally, water scarcity threatens food security, especially in arid and semi-arid food-producing regions, such as the Hetao Irrigation District (HID), where the water consumption during the non-growth season (spring irrigation, autumn irrigation) accounts for more than 30 % of the total irrigation volume. Meanwhile, mapping the characteristics of spatial and temporal evolution of the area of cropland irrigated in the spring and autumn and the pattern of water consumption is crucial for strengthening the management of agricultural water use. Evaporation and infiltration of irrigation water are difficult to capture in time due to insufficient timeliness or accuracy of single remote sensing data. This study integrates seven remote sensing datasets (Landsat5, Landsat7, Landsat8, HJ-1A/B, GF-1, Sentinel-2 and MODIS) for the first time to build a high spatial-temporal resolution dataset, and combines the water index NDWI and MNDWI to extract the maximum water range formed after irrigation by manually adjusting the threshold method. In addition to the global land use/cover dataset (GLC FCS30–1985_2020), the cropland layer was superimposed to accurately identify the cropland spring irrigation and autumn irrigation range from 2000 to 2021 in the HID. In the past 20 years, spring-irrigated (SI) areas increased by 1012.60 km2 (+49.96 %), and the autumn-irrigated (AI) areas decreased by 512.63 km2 (-11.02 %). Combined with the quantity of water diversion and displacement, the characteristics of irrigation water consumption in the non-growth season were further analysed. The results showed that the water consumption of spring irrigation increased by 0.67×108 m3 (+22.26 %), and that of autumn irrigation increased by 4.34×108 m3 (+38.82 %). The decrease of autumn- irrigated area and the increase of water consumption were mainly related to the stability of autumn irrigation water, imperfect irrigation and drainage conditions and insufficient field water management. In 2020­2021, the area of non-growth “autumn irrigation - spring irrigation” repeated irrigation is about 1271.52 km2, which can be used as a key control area. This study provides a theoretical basis and direction for rational water use in non-growth seasons of irrigation districts.

Decoupling framework for large-scale energy systems simultaneously addressing carbon emissions and energy flow relationships through sector units: A case study on uncertainty in China's carbon emission targets

The energy system requires meticulous planning to achieve low-carbon development goals cost-effectively. However, optimizing large-scale energy systems with high spatial-temporal resolution and a rich variety of technologies has always been a challenge due to limited computational resources. Therefore, this study proposes a soft-linkage framework to deconstruct large-scale energy system optimization models based on sectors while ensuring the total carbon emission limit and the electricity supply-demand balance. Using China's energy system as a case study, the impact of uncertainty on emission reduction targets is analyzed. A long-term emission target curve is only described by the total carbon budget and its temporal distribution. Results show that different carbon budget time series can lead to total transition cost variations of up to nearly 100 trillion yuan. Moreover, although a lower carbon budget would increase the total cumulative transition cost quadratically, excessively high carbon budgets raise national natural gas demand, threatening energy security.

Surface plasmon resonance microscopy for optical EHL measurement systems

This study investigates the suitability and limitations of surface plasmon resonance (SPR) microscopy for optical elastohydrodynamic lubrication (EHL) measurement systems. SPR microscopy developed in this study can quantify the lubricant density distribution in contact region with high spatial-temporal resolution using simple total reflection optical systems. It means that, if either the temperature or pressure distribution can be estimated, the other can be calculated. To examine the practicalities, we determined specific optical setting conditions such as the incident angle and thickness of each film layer for surface plasmon excitations under EHL conditions based on numerical calculations and demonstration experiments.

Determination of the sensing properties of the fluorescence-based sensor for atmospheric NO2 gas

Air pollution in urban areas poses a serious threat to human health and therefore the studies about the development of low cost and sensitive sensors to monitor the air quality with high spatial and low temporal resolution continue to be an extensive area in literature. In this study, oxime modified poly(4-(1-pyrenyl) styrene) (P(PySt)-NOX) probes were synthesized to use as a sensor to detect NO2 gas in ambient air. The structural characterization results showed that the probe was successfully synthesized. The sensitivity, selectivity, repeatability, and aging tests were performed during the study, and it was observed that P(PySt)-NOX loaded sensor is sensitive to NO2 for concentrations below 100 ppb. The selectivity measurements were performed against O3 and SO2 which are common interfering gases in ambient air, and it was shown that the sensor is selective to NO2. Additionally, according to the aging tests performed in laboratory for 23 days, it was observed that the sensor is stable in this time interval. The studies showed the sensor synthesized and designed in this study is suitable for NO2 concentration measurements in ambient air where the concentration levels of NO2 is below 100 ppb.

Evaluation of the Forecasting Performance of Supercooled Clouds for the Weather Modification Model of the Cloud and Precipitation Explicit Forecasting System

Through the application of cloud top temperature data and the extraction of supercooled cloud information in cloud-type data from the next-generation Himawari-8 geostationary satellite with high spatial–temporal resolution, a quantitative evaluation of the forecasting performance of the weather modification model named the Cloud and Precipitation Explicit Forecasting System (CPEFS) was conducted. The evaluation, based on selected forecast cases from 8 days in September and October 2018 initialized at 00 and 12 UTC every day, focused especially on the forecasting performance in supercooled clouds (vertical integrated supercooled liquid water, VISL > 0), including the comprehensive spatial distribution of cloud top temperature (CTT) and 3 h precipitation over 0.1 mm (R3 > 0.1). The results indicated that the forecasting performance for VISL > 0 was relatively good, with the Threat Score (TS) ranging from 0.46 to 0.67. The forecasts initialized at 12 UTC slightly outperformed the forecasts initialized at 00 UTC. Additionally, the corresponding spatial Anomaly Correlation Coefficient (ACC) of CTT between forecasts and observations was 0.23, and the TS for R3 > 0.1 reached as high as 0.87. For a mix of cold and warm cloud systems, there was a correlation between the forecasting performance of VISL > 0 and CTT. The trends in the TS for VISL > 0 and the ACC of CTT aligned with the forecast lead-time.

A multi-satellite survey scheme for addressing open questions on the Earth’s outer radiation belt dynamics

The Earth’s outer radiation belt is highly dynamic, containing relativistic electron fluxes that can increase by several orders of magnitude during magnetospheric disturbances. This greatly increases the likelihood of spacecraft malfunction or failure and significantly influences the solar-terrestrial system’s energy and mass coupling, highlighting the importance of fully understanding the mechanisms governing these dynamics from both theoretical and practical perspectives. Although many theories have been proposed, further research is essential to quantify the specific contributions of different dynamic mechanisms for improving space weather forecasting. To address this, observations of the outer radiation belt with high spatial–temporal resolution to distinguish the spatial and temporal variations are essential. We introduce a 10-CubeSat constellation survey scheme in the geosynchronous transfer orbit (GTO) to achieve this required observation. Three baseline instruments are proposed to be employed: the high energy electron detector (HEED), the search coil wave detector (SCWD), and the magnetometer (MAG). Two groups of physical processes will be investigated: wave-particle interactions involving charged particles interacting with whistler-mode waves, electromagnetic ion cyclotron (EMIC) waves, and ultra-low frequency (ULF) waves; and radial transport encompassing shock-induced injections, substorm injections, storm convection and magnetopause shadowing. The performance parameters of instruments and platform of the constellation are presented. Additionally, aligned with the concept of constellation survey, we outline the COSPAR-coordinated space program, COnstellation of Radiation BElt Survey (CORBES), which will provide a crucial scientific contribution in the absence of the Van Allen Probes. The program’s excellent observational capability enables a comprehensive understanding of the underlying physical mechanisms governing the outer radiation belt dynamics and improved space weather forecasting.

Advanced remote sensing technologies for tracking landscape changes and environmental conditions

The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.

Mapping Crop Evapotranspiration by Combining the Unmixing and Weight Image Fusion Methods

The demand for freshwater is increasing with population growth and rapid socio-economic development. It is more and more important for refined irrigation water management to conduct research on crop evapotranspiration (ET) data with a high spatiotemporal resolution in agricultural regions. We propose the unmixing–weight ET image fusion model (UWET), which integrates the advantages of the unmixing method in spatial downscaling and the weight-based method in temporal prediction to produce daily ET maps with a high spatial resolution. The Landsat-ET and MODIS-ET datasets for the UWET fusion data are retrieved from Landsat and MODIS images based on the surface energy balance model. The UWET model considers the effects of crop phenology, precipitation, and land cover in the process of the ET image fusion. The precision evaluation is conducted on the UWET results, and the measured ET values are monitored by eddy covariance at the Luancheng station, with average MAE values of 0.57 mm/day. The image results of UWET show fine spatial details and capture the dynamic ET changes. The seasonal ET values of winter wheat from the ET map mainly range from 350 to 660 mm in 2019–2020 and from 300 to 620 mm in 2020–2021. The average seasonal ET in 2019–2020 is 499.89 mm, and in 2020–2021, it is 459.44 mm. The performance of UWET is compared with two other fusion models: the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Spatial and Temporal Reflectance Unmixing Model (STRUM). UWET performs better in the spatial details than the STARFM and is better in the temporal characteristics than the STRUM. The results indicate that UWET is suitable for generating ET products with a high spatial–temporal resolution in agricultural regions.

High-throughput volumetric mapping of synaptic transmission.

Volumetric imaging of synaptic transmission in vivo requires high spatial and high temporal resolution. Shaping the wavefront of two-photon fluorescence excitation light, we developed Bessel-droplet foci for high-contrast and high-resolution volumetric imaging of synapses. Applying our method to imaging glutamate release, we demonstrated high-throughput mapping of excitatory inputs at >1,000 synapses per volume and >500 dendritic spines per neuron in vivo and unveiled previously unseen features of functional synaptic organization in the mouse primary visual cortex.

Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model

The high temporal-spatial resolution estimation of XCO2 data is foundational for precision quantification of carbon dioxide sources and sinks at a regional scale. This study proposed an advanced XCO2 data estimation method, using spatial features deep fusion. Leveraging convolutional neural network (CNN) principles, SpatialFusionNet—a module was designed to amalgamate geographical features within a defined spatial range. This module captures and integrates the spatial characteristics of meteorological and surface environmental factors, enhancing its application to the XCO2 estimation model. Building upon machine learning methods including Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Deep Neural Network (DNN), combined with the SpatialFusionNet module, the spatial features deep fusion models were constructed utilizing relationships among OCO-2 satellite XCO2 trajectory monitoring data in China, Copernicus Atmospheric Monitoring Service (CAMS) XCO2 reanalysis data, and meteorological factors, surface vegetation, and meteorological factors. Model performance improvements were significant, with SVM, DNN, and XGBoost showing respective RMSE reductions of 1.297 ppm, 0.480 ppm, and 0.200 ppm in ten-fold cross-validation based on OCO-2 trajectory samples. In data validation with TCCON Hefei station, the correlation between inversion data and ground-based data reached 0.85, affirming the method's high accuracy. Employing the spatial feature extraction module combined with DNN, the 2015 XCO2 annual spatial distribution of China, analyzing temporal-spatial distribution characteristics in China was generated. The DNN model, combining the SpatialFusionNet module, significantly contributes to the estimation of high temporal-spatial resolution XCO2 datasets, facilitating fine-scale quantification of regional carbon cycling.

Future urban ecological land transition and its implications for high-heat exposure in China

Urban ecological land (UEL) is an important nature-based solution for mitigating human exposure to extreme heat (i.e., high-heat exposure, HHE), but the impacts of future UEL transition remain unclear. This study developed a high spatial-temporal resolution dataset (30 m) of urban land cover in China during the 21st century under the shared socioeconomic pathway and the representative concentration pathway (SSP-RCP) to quantify the contribution of UEL transition to HHE. Results showed that UEL transitions will influence 0.12–0.26 °C of the HHE from 2021 to 2100 and is the dominant driver in 10.2–22.8 % of cities (including 95.1 million to 181.2 million people in 2100), with substantial regional disparities. Moreover, UEL landscape pattern is a more important factor than area. Specifically, the UEL evolution pattern will dominate the exposure of 8.02 to 10.77 million people to extreme heat within the urban cores (i.e., predominant urban areas) in the SSP1-2.6 and SSP2-4.5 scenarios, especially in the eastern cities. Under the SSP3-7.0 and SSP5-8.5 scenarios, the UEL evolution pattern will dominate the exposure of 2.44 to 5.34 million people to extreme heat on the expansion surfaces (i.e., areas transitioning from rural to urban), especially in the western cities. The optimization of UEL landscape patterns should be a top priority as an urban heat adaptation strategy to maximize urban resilience to climate change.

High‐Resolution Carbon‐Based Tactile Sensor Array for Dynamic Pulse Imaging

AbstractWith the development of modern medicine, the importance of continuous and reliable pulse wave monitoring has increased significantly in physiological evaluation and disease diagnosis. Among them, the 3D reconstruction of the pulse wave is indispensable, and needs rely on ultra‐high resolution sensor arrays, that is, high spatial resolution, temporal resolution, and force resolution. Herein, a flexible high‐density 32 × 32 tactile sensor array based on pressure‐sensitive tunneling mechanism is develpoed. Conformal graphene nanowalls (GNWs) pattern arrays are deposited on micro‐pyramidal structural Si substrate via mask‐assisted plasma enhanced chemical vapor deposition (PECVD) method and are adopted as pressure‐sensitive electrode, exhibiting a spatial resolution of 64 dots/cm2, high sensitivity (222.36 kPa−1) and short response time (2 ms). More importantly, HfO2 tunneling layer can effectively suppress noise current, which made it sense weak pressure signals with 1/1000 force resolution and SNR of 36.32 dB. By leveraging its high‐resolution array, more holistic pulse signals are acquired and the 3D shape of the pulse wave are successfully replicated. This work shows high‐resolution sensors have significant promise for applications in remote intelligent diagnostics.