Look-up Table Approach Research Articles

Improving -inhomogeneity tolerance by resolving non-bijection in MP2RAGE R1 mapping: A 2D look-up table approach demonstrated at 3 T.

Regarding in vivo, robust longitudinal relaxation rate (R1) mapping, the goal of the present paper is two-fold. First, to verify that non-bijective mapping in magnetization-prepared 2 rapid gradient echo (MP2RAGE) imaging can be resolved through a two-dimensional look-up table approach. Second, that the expanded parameter space from this can be used to improve -inhomogeneity tolerance without other prerequisites. By deriving a second contrast from the magnitude images of the MP2RAGE acquisition, ambiguities in the original MP2RAGE image resulting from non-bijective transfer curves can be resolved. Such ambiguities may occur when protocols are optimized, such as for higher -inhomogeneity tolerance. A 2D look-up table approach combines the available information to resolve these ambiguities during mapping. At 3 T, we acquired MP2RAGE images with standard acquisition parameters and (non-bijective) parameters optimized for -inhomogeneity tolerance. From 3 subjects across multiple sessions, we assessed the -inhomogeneity tolerance through excitation-pulse amplitude scalings. The R1 maps resulting from the -optimized protocols showed greatly reduced effects across images but without additional scanner time. Meanwhile, these maps could only successfully be derived by a 2D look-up table approach. We show that it is possible to optimize for -inhomogeneity tolerance in MP2RAGE through sequence-parameter settings, while still successfully estimating the R1 map with a two-dimensional look-up table approach. This without the need for an additional map. The increased parameter space enabled by the two-dimensional look-up table approach may further be used to adjust MP2RAGE acquisitions for improved scan times, signal-to-noise ratio, and/or contrast-to-noise ratio.

Mapping and Analyzing Ecosystem Services Hotspots and Coldspots for Sustainable Spatial Planning in the Greater Asmara Area, Eritrea.

Rapid urbanization in African metropolises like the Greater Asmara Area, Eritrea, poses numerous environmental challenges, including soil sealing, loss of vegetation cover, threats to protected natural areas, and climate change, among others. Mapping and assessing ecosystem services, particularly analyzing their spatial and temporal distribution is crucial for sustainable spatial planning. This study aims at mapping and analyzing ecosystem services hotspots and coldspots dynamics in the Greater Asmara Area to identify recent trends and opportunities for enhancing ecosystem services supply. Utilizing remote sensing images, we produced land cover maps for 2009 and 2020 and mapped six ecosystem services through a lookup table approach. The study includes provisioning, regulating and maintenance, and cultural ecosystem services. We analyzed their spatio-temporal variations, identifying ecosystem services hotspots and coldspots and their changes over time. Results show that overall ecosystem services potential in the Greater Asmara Area remains low but stable, with some improvements. By 2020, areas with no ecosystem services potential decreased in southern regions like Gala Nefhi and Berik, and new hotspots and coldspots emerged in central Gala Nefhi. This pilot study demonstrates the feasibility and key challenges of the ecosystem services hotspots and coldspots approach for sustainable spatial planning in rapidly urbanizing African metropolitan regions. Despite limitations, the study offers valuable insights into ecosystem services potentials, and related hotspots and coldspots dynamics, raising awareness and paving the way for further research and application.

Junction Temperature Prediction and Lifetime Assessment in a PV Inverter Using a 10-year Mission profile

The expansion of great-scale photovoltaic (PV) power plants indicates the need for an accurate lifetime assessment of inverters to maintain energy supply availability. In this context, the study contributes in two ways. First, we use machine-learning (ML) models for junction temperature prediction. Second, we perform reliability assessments using a 10-year mission profile in three Brazilian cities. The thermal loadings are obtained through a look-up table approach. Although the ML models exhibit different performances in regression, other factors must be considered, such as easy-to-apply, interpretability, and generalization capability. The reliability assessment is typically based on an annual mission profile, assuming damage repeats until failure. However, only the historical series can confirm whether this choice was acceptable, pessimistic, or optimistic. For instance, in Campos do Jordão-SP, if the chosen mission profile is 2014, the expected failure of 10\% of inverter samples occurs three years earlier than suggested by the historical series. Regardless of the methodology used to estimate thermal loading or accumulated damage, the mission profile significantly influences photovoltaic inverter reliability, indicating that if more data is available, the chosen mission profile should align with the historical series.

Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Efficient Prediction of Turbulent Inflow and Leading-Edge Interaction Noise Using a Vortex Particle Method with Look-up Table Approach

This paper presents a new, efficient approach for predicting turbulent inflow, also known as leading-edge interaction noise. The method combines a low-fidelity Vortex Particle Method (VPM) with a look-up table approach. Its goal is to reconstruct realistic inflow turbulence and airfoil contours using stochastic control variables within a limited vortex window. To model far-field sound emission, Curle’s equation and a vortex sound database are employed. To increase confidence in the methodology, an analysis of inflow turbulence parameters and source characteristics was performed using systematic Large Eddy Simulations (LES). A generic NACA 0012 airfoil test case with different inflow turbulence grids was used for direct comparisons with semi-theoretical and semi-empirical predictions from the literature. The comparison is restricted to Amiet/Gershfeld predictions as the current model is only capable of dealing with homogeneous and isotropic turbulence. However, their usefulness is limited to narrower parameter ranges when compared to the more generally applicable new method. A satisfactory agreement of the results demonstrates the versatility of the proposed method.

Characterization of stratospheric particle size distribution uncertainties using SAGE II and SAGE III/ISS extinction spectra

Abstract. A new algorithm was developed to infer particle size distribution parameters from the Stratospheric Aerosol and Gas Experiment II (SAGE II) and SAGE III on the International Space Station (SAGE III/ISS) extinction spectra using a lookup table (LUT) approach. Here, the SAGE-based extinction ratios were matched to LUT values, and, using these matches, weighted statistics were calculated to infer the median particle size distribution values and higher-moment parameters as well as quantify the uncertainty in these estimates. This was carried out by solving for both single-mode and bimodal lognormal distributions. The work presented herein falls under two general headings: (1) a theoretical study was carried out to determine the accuracy of this methodology, and (2) the solution algorithm was applied to the SAGE II and SAGE III/ISS records with a brief case study analysis of the 2022 Hunga Tonga eruption. This methodology was demonstrated to be ≈ 25 % accurate for mode radius and has a minor dependence on particle composition. While bimodal solutions were obtained from this algorithm, we provide a conclusive demonstration of how and why these estimates are inherently unstable using SAGE III/ISS extinction spectra alone. Finally, we demonstrated how the Hunga Tonga aerosol plume evolved in regard to both size and transport over 18 months after the 2022 Hunga Tonga eruption. The particle size distribution (PSD) estimates, higher-moment parameters, and uncertainties are new products within the SAGE III/ISS Level 2 (L2) products, are currently available for download, and will be merged into the main SAGE III/ISS release products in a subsequent L2 release.

Dynamic wind farm flow control using free-vortex wake models

Abstract. A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.

Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.

The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption () and reduced scattering () coefficients at various depths. To achieve the stratified reconstruction of OPs and the separation between and , using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (, continuous-wave component, , mean time of flight). We compared their performance using a series of simulation and phantom validations. Compared to the LUT approach, utilizing full TR, and datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of and at different depths within 5mm.

Exploring the Optimized Leaf Area Index Retrieval Strategy Based on the Look-up Table Approach for Decametric-Resolution Images

Exploring the Optimized Leaf Area Index Retrieval Strategy Based on the Look-up Table Approach for Decametric-Resolution Images

Integration of Landsat 8 (OLI) and MODIS images to monitor suspended particles and evaluate the spatial pattern of air pollution

Air pollution has become one of the disturbances of daily life in urban areas due to its negative and repulsive effect in current conditions. On the other hand, it has a long-term negative impact on the behavior and quantity of climate change scenarios at the local, national and global levels. In this research, the integration of Landsat 8 (OLI) satellite images and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (to estimate the ozone permeability and the water vapor permeability coefficients) using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiation transfer model was used to determine Aerosol Optical Depth (AOD). For this purpose, instead of using the look-up table and approximating the AOD values, the components of the 6S radiative transfer model were estimated with a combined approach and the AOD values were retrieved without using the look-up table approach. One of the main problems of past research is to identify a suitable conversion model to connect the refractive index to the composition of aerosols. Therefore, the main innovation of this research is focused on a simplified, high resolution AOD retrieval method for Landsat 8 (OLI) images over mixed surfaces which does not require a LUT. Considering the relationship between AOD values and various indicators of air quality measurement, in this work, AOD estimation has been done with the aim of helping to predict these indicators. In this research, 24,569 square kilometers from a part of two states of Maryland and Virginia in the United States of America were investigated in order to estimate AOD. In order to evaluate the results of AOD retrieval, 5 Aerosol Robotic Network (AERONET) ground stations were used for comparison and their accuracy was evaluated. The results show that the proposed method has been able to extract the amount of aerosol with acceptable accuracy. According to the results obtained in this research, the root mean square errors (RMSE) is equal to 0.1036, the adjusted R2 coefficient (AdjR2) is equal to 0.8032 and the coefficient of determination (R2) is equal to 0.8079. It can be concluded that the integration of Landsat 8 (OLI) images with MODIS images has been able to provide more suitable results.

Implementation of Gate-All-Around Gate-Engineered Charge Plasma Nanowire FET-Based Common Source Amplifier.

This paper examines the performance of a Gate-Engineered Gate-All-Around Charge Plasma Nanowire Field Effect Transistor (GAA-DMG-GS-CP NW-FET) and the implementation of a common source (CS) amplifier circuit. The proposed GAA-DMG-GS-CP NW-FET incorporates dual-material gate (DMG) and gate stack (GS) as gate engineering techniques and its analog/RF performance parameters are compared to those of the Gate-All-Around Single-Material Gate Charge Plasma Nanowire Field Effect Transistor (GAA-SMG-CP NW-FET) device. Both Gate-All-Around (GAA) devices are designed using the Silvaco TCAD tool. GAA structures have demonstrated good gate control because the gate holds the channel, which is an inherent advantage for both devices discussed herein. The charge plasma dopingless technique is used, in which the source and drain regions are formed using metal contacts and necessary work functions rather than doping. This dopingless technique eliminates the need for doping, reducing fluctuations caused by random dopants and lowering the device's thermal budget. Gate engineering techniques such as DMG and GS significantly improved the current characteristics which played a crucial role in obtaining maximum gain for circuit designs. The lookup table (LUT) approach is used in the implementation of the CS amplifier circuit with the proposed device. The transient response of the circuit is analyzed with both the device structures where the gain achieved for the CS amplifier circuit using the proposed GAA-DMG-GS-CP NW-FET is 15.06 dB. The superior performance showcased by the proposed GAA-DMG-GS-CP NW-FET device with analog, RF and circuit analysis proves its strong candidature for future nanoscale and low-power applications.

Efficient modeling of wave generation and propagation in a semi-enclosed estuary

Accurate, and high-resolution wave statistics are critical for regional hazard mapping and planning. However, long-term simulations at high spatial resolution are often computationally prohibitive. Here, multiple rapid frameworks including fetch-limited, look-up-table (LUT), and linear propagation are combined and tested in a large estuary exposed to both remotely (swell) and locally generated waves. Predictions are compared with observations and a traditional SWAN implementation coupled to a regional hydrodynamic model. Fetch-limited and LUT approaches both perform well where local winds dominate with errors about 10%–20% larger than traditional SWAN predictions. Combinations of these rapid approaches with linear propagation methods where remotely generated energy is present also perform well with errors 0%–20% larger than traditional SWAN predictions. Model–model comparisons exhibit lower variance than comparisons to observations suggesting that, while model implementation impacts prediction skill, model boundary conditions (winds, offshore waves) may be a dominant source of error. Overall results suggest that with a relatively small loss in prediction accuracy, simulations computation cost can be significantly reduced (by 2–4 orders of magnitude) allowing for high resolution and long-term predictions to adequately define regional wave statistics.

Comparison of low-dropout voltage regulators designed with line and nanowire tunnel-FET experimental data including a simple process variability analysis

This work presents the comparison between Nanowire Tunnel Field-Effect Transistor (NW-TFET) and Line-TFET applied on the design of Low-Dropout Voltage Regulator (LDO). Both devices have a SiGe source composition in order to enhance the current drive. The transistors were modeled using lookup tables (LUTs) approach based on experimental data using Verilog-A language. The LDOs were designed for two conditions, considering different gm/ID, load currents and load capacitances. In order to compare the TFET LDOs with an established technology, a MOSFET LDO was designed with TSMC 0.18 µm process design kit. Both TFET LDOs reach stability without the presence of a compensation capacitor. For gm/ID = 10.5 V−1 the current consumption of NW-TFET LDO (1.5nA) is near two orders of magnitude lower than Line-TFET LDO (68nA) and three orders of magnitude lower than MOSFET LDO (9 µA). The Line-TFET LDO exhibits better results in almost all parameters despite of the gain-bandwidth product (GBW) that is in the same order of magnitude of the MOSFET LDO, 171 kHz compared to 250 kHz for gm/ID = 10.5 V−1. The comparison between TFET LDOs for gm/ID = 7 V−1 was also performed regarding the transient and process variability analysis. The transient response revealed that the Line-TFET LDO has a pronounced lower settling time, 71 µs compared to 5 ms for a load step, but with a damped oscillatory response, the NW-TFET LDO presented lower undershoot for the load step. The process variability analysis was performed for devices within the wafer and was observed that the Line-TFET LDO suffers a higher impact with a 20 dB variation on the loop gain in comparison to 10 dB in the NW-TFET LDO.

A comparison of process-based and data-driven techniques for downscaling offshore wave forecasts to the nearshore

Forecast downscaling for accurate nearshore wave conditions is important for a range of marine and coastal users. However, it is complex and high-resolution forecasting methods are computationally intensive, especially when being applied at scale. Identifying methods of downscaling to a high-resolution with accuracy and increased computational efficiency is desired. The prediction of wave parameters using machine learning techniques is becoming increasingly popular, potentially offering more computational efficiency. Other computationally efficient techniques also exist such as the Hybrid Downscale Technique and lookup table approaches. This paper compares three data-driven methods for downscaling offshore wave conditions to a typical computationally intensive process-based approach. A typical calibrated process-based wave model was used to create a catalogue of data from which to develop data-driven models. The comparison was also extended to include the use of wave parameter observations. Each of the data-driven methods showed comparable or improved numerical performance to the process driven technique particularly for the Hs parameter (with an up to 16% improvement in Root Mean Squared Error over the process based model). Both data-driven approaches were considerably more computationally efficient, between eleven and seven thousand times faster than the process-based model. However, limitations were identified that should be considered when applying data-driven approaches for predicting wave parameters, such as availability and consistency of data, and the ability of simple parameters to represent complex sea states.

Biases in Shortwave Three-Dimensional Radiative Transfer Calculations for High-Resolution Numerical Models

This study investigated biases of diffuse radiation in a look-up table approach, which pre-computed the sequential ray tracing to avoid heavy computation in full three-dimensional radiative transfer calculation. We introduced corrections that enhanced directionality of radiative propagation in the solar angle and horizontal direction. By comparing irradiance calculations with and without the corrections for cloudy field in an idealized atmospheric simulation, it was found that the corrections helped mitigate vertically localized false signals by diffuse irradiance. The results suggested that the two types of directionalities are important to accurately represent the three-dimensional transfer of diffuse radiation in an inhomogeneous atmosphere.

Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network

Aerosol single-scattering albedo (SSA) is one of the largest sources of uncertainty in the evaluation of the aerosol radiative forcing effect. The SSA signal, coupled with aerosol optical depth (AOD) and surface reflectance in satellite images, is difficult to retrieve by the look-up table approach. In this study, we proposed an artificial neural network- (ANN) based approach that retrieves SSA over land based on MODIS (moderate resolution imaging spectroradiometer) visible (red band) reflectance variations among nearby pixels that have different surface reflectivities. Using the training dataset generated by the radiative transfer model, the ANN model was trained to establish the relationship among SSA, surface reflectance, and top of atmosphere (TOA) reflectance. Then, based on the trained ANN model, SSA can be retrieved using the surface and apparent reflectance of several heterogeneous pixels. According to sensitivity analysis, this method works well on nonuniform land surfaces with high AODs. The root mean square error (RMSE) of retrieved and measured SSA (from 28 sites of AErosol RObotic NETwork, AERONET) was 0.042, of which the results with an error less than 0.03 accounted for 51%. In addition, the SSA retrieval method was applied to several thick aerosol layer events over different areas (South Asia, South America, and North China Plain) and compared with the ozone monitoring instrument near-UV aerosol data product (OMAERUV). The comparison results of the images show that the retrieval method of visible wavelength proposed in this study has similar outcomes to those from the ultraviolet wavelengths in these regions. The retrieval algorithm we propose provides an effective way to produce an SSA product in visible wavelength and might help to better estimate the aerosol radiative and optical properties over high heterogeneous areas, which is important for the aerosol radiative impact estimate at a regional scale.

Investigation on the Wilson Neuronal Model: Optimized Approximation and Digital Multiplierless Implementation.

Neuromorphic engineering is an essential science field which incorporates the basic aspects of issues together such as: physics, mathematics, electronics, etc. The primary block in the Central Nervous System (CNS) is neurons that have functional roles such as: receiving, processing, and transmitting data in the brain. This paper presents Wilson Multiplierless Neuron (WMN) model which is a modified version of the original model. This model uses power-2 based functions, Look-Up Table (LUT) approach and shifters to apply a multiplierless digital realization leads to overhead costs reduction and increases in the final system frequency. The proposed model specifically follows the original neuron model in case of spiking patterns and also dynamical pathways. To validate the proposed model in digital hardware implementation, the FPGA board (Xilinx Virtex II XC2VP30) can be used. Hardware results show the increasing in the system frequency compared with the original model and other similar papers. Numerical results demonstrate that the proposed system speed-up is 210 MHz that is higher than the original one, 85 MHz. Additionally, the overall saving in FPGA resources for the proposed model is 96.86 % that is more than the original model, 95.13 %. From case study viewpoint for CNS consideration, a network consisting of Wilson neurons, synapses, and astrocytes have been considered to test the controlling effects on LTP and LTD processes for investigating the neuronal diseases (medical approaches) such as Epilepsy.

Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs

Data compression is essential to reduce high storage and communication costs for a wide range of systems and applications. Canonical Huffman coding plays a pivotal role for several compression standards. This paper presents bit-parallel static and dynamic canonical Huffman decoder implementations using an optimized lookup table approach on a fine-grain many-core processor array and an Intel FPGA. The decoder implementation results are compared with an Intel i7-4850HQ and a massively parallel Nvidia GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 891× and 7× greater on average than the i7 and GT 750M respectively. Moreover, the many-core implementations result in a scaled energy efficiency (compressed bits decoded per energy) that is 149.5×, 3.9×, and 2.5× greater on average than the i7, GT 750M, and Intel FPGA respectively. In addition, the optimized lookup-table-based static canonical Huffman decoder on the Intel FPGA yields performance and energy efficiency improvements of 2.1× and 3.68× respectively on average compared to a dynamic canonical Huffman decoder at a 17% cost in compression ratio.

Modeling the start of frozen dates with leaf senescence over Tibetan Plateau

As the amplifier of global climate change, climate warming exerts an important impact on the freezing/thawing cycles of soil over the Tibetan Plateau, and it shapes the trend of permafrost degradation. Intensified frozen soil collapse causes severe effects on ecosystem water and energy balance as well as on carbon cycle. Previous studies have focused on the direct effects of climate change on permafrost degradation. However, there is also growing evidence showing vegetation growth can affect regional climate system, and consequently we hypothesize that vegetation autumn phenology (i.e., the end of the growing season, EOS) may influence the start date of frozen (SOF) through feedbacks to regional climates. Using satellite greenness data derived EOS and the microwave remote sensing generated SOFESDR (freeze–thaw Earth system data record) over 2001–2018, we showed a dominant-negative (13.1% vs. 0.9%) relationship between SOFESDR and EOS, suggesting an earlier SOFESDR with a delayed EOS. We found that biogeophysical indicators served as potential connections, including surface albedo, soil temperature, soil water content, and evapotranspiration, for the observed relationship. We therefore proposed a new site-level SOFf(EOS)×ESDR algorithm based on the EOS-SOF relationship. With ground SOFALT observed from the active layer thickness at 63 sites over Tibetan Plateau, the new model provided significantly improved estimates of SOF with Pearson's correlation coefficient (R) of 0.84 and root mean square error (RMSE) of 7.63 days, comapred with current remote sensing-based SOF product (R = 0.26, RMSE = 22.60 days). We further proposed a look-up table approach to map the SOF over TP and found an overall earlier SOF (24.0 ± 15.8) than current SOFESDR products. Therefore, our results identified a significant correlation between the autumn phenology and the SOF variability, highlighting the importance of feedbacks of autumn phenology on climate change.