Microwave Brightness Temperature Research Articles

Cross-calibration between MWRI and AMSR2 to improve consistency of snow depth products

The Microwave Radiation Imager (MWRI), boarded on the FY-3 series satellites: FY-3B, FY-3C, and FY-3D, is the first satellite-based microwave radiometer in China, commencing passive microwave brightness temperature data acquisition since 2010. The Advanced Microwave Scanning Radiometer 2 (AMSR2) boarded on the Global Change Observation Mission 1st-Water (GCOM-W1), has been operational since 2012. Despite the FY-3 series satellites are equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2, disparities in observation times and satellite platforms result in inconsistencies in the data obtained by different satellites, which further impacting the consistency of retrieved geophysical parameters. To improve the consistency of brightness temperatures from FY-3B, FY-3C, FY-3D/MWRI, and GCOM-W1/AMSR2, cross-calibrations were conducted among brightness temperatures at ten-channel from above four platforms. The consistency of derived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed. The results show that the correlation coefficients of brightness temperatures at all channels between sensors exceed 0.98. After cross-calibration, the RMSEs and biases of brightness temperatures at all frequencies and snow depth in China derived from them reduce to varying degrees. The consistencies in both brightness temperatures and snow depth of FY-3B/MWRI, FY-3D/MWRI, and AMSR2 are higher than those of FY-3C and others. These findings advocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI, FY-3D/MWRI, and AMSR2, which share similar satellite overpass time, to derived a long-term snow depth dataset.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

Assessment of various microwave brightness temperature products and methods for surface melt detection over Greenland ice sheet

Microwave brightness temperature have widely been used for the detection of the ice sheet's surface melt conditions and understanding their spatio-temporal variability. In this study, we investigated the sensitivity of microwave brightness temperature products from three different sensors, namely, Advanced Microwave Scanning Radiometer-2 (AMSR2), Special Sensor Microwave Imager/Sounder (SSMIS) and Indian scatterometer satellite (SCATSAT-1) for surface melt detection over the Greenland ice sheet (GrIS). In-situ air temperature measurements from GC-Net AWSs were used for sensitivity and inter-comparison analysis. Our findings show that brightness temperature (Tb) from SSMIS better correlates (Pcoef = 0.81 for 19 GHz) with air temperature measurements in comparison to AMSR2 (Pcoef = 0.7 for 18 GHz) and SCATSAT-1 (Pcoef = 0.67). However, interestingly, AMSR2 and SCATSAT-1 uniquely discriminated the surface conditions during pre- and post-melt period, due to their heterogeneity in Tb values during the two period. Error analysis with respect to AWS melt days shows that SSMIS 19 GHz Tb (Tb/SSMIS/19H) products with TED method giving the most promising observations. A wide variability in Tb values is observed during the melt season across the various AWS sites depending upon the elevation, location and frequency. During our study period, using Tb/SSMIS/19H for TED method, we observed the highest melt extent area for the extreme melt event in year 2019 (∼1.12 million km2), followed by another melt event year 2021 when it went as high as ∼1.02 million km2.

Hydrometeor Identification Using GMI Passive Microwave Brightness Temperatures

Abstract Using passive microwave brightness temperatures Tb from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb. Eight years of collocated Tb and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015–20 used for training and 2021–22 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tb.

Reciprocity-Based Modeling of Microwave Brightness Temperature Transfer in Near Field, From Calibration Target to Antenna

Reciprocity-Based Modeling of Microwave Brightness Temperature Transfer in Near Field, From Calibration Target to Antenna

Low-frequency variability and teleconnections during Northern Hemisphere winters captured by satellite microwave brightness temperature observations

Low-frequency variability and teleconnections during Northern Hemisphere winters captured by satellite microwave brightness temperature observations

Spatio-Temporal Variations of Surface Melt Over Antarctic Ice Shelves using SCATSAT-1 Data

Surface melting is a significant issue in Antarctica, affecting glacier movements and climate change. During summer, surface meltwaters from ponds circulate over ice shelves, causing mass loss. These melt water percolates down to shelf through crevasses and affects the iceshelf instability or break the ice shelf. Antarctica experiences a surface melting increase of around 3.5 million square kilometres for every one-degree rise in summer temperature. In this study we use remote-sensing data sets to assess the spatial and temporal distribution of surface melt over Antarctic ice shelves. We use microwave brightness temperature (Tb) to evaluate surface melting on ice shelves. Total four ice shelves from East and West Antarctica were selected for research due to their significant surface melting issues. The study estimated cumulative melt days over these ice shelves for year 2017 and 2018, and investigated melt variations over transect profiles. It was found that year 2018 showed increased amount in melt days in some regions of selected ice shelves.

Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to τ-ω model

Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm−³, and bias lowering from 0.127 to 0.060 cm³cm−³ in comparison with the SMAP measurements over forested regions.

An Innovative Approach: Sea Ice Types Classification Using Convolutional Neural Networks with DDDTDWT Filter

Studying sea ice and its interaction with climate change is crucial due to its significant impact on the environment, society, and global stability. The pressing need to address the underlying reasons for the rapid melting of Arctic and Antarctic sea ice is underscored by its adverse effects on the environment and society. In this proposed study, a Convolutional Neural Network (CNN) is utilized to predict ice types using data from the NSIDC DAAC Advanced Microwave Scanning Radiometer - Earth Observing System Sensor (AMSR-E) collection. This dataset contains parameters such as sea ice types and spans data products from June 2002, obtained from the NASA Data Centre. By employing hand-crafted features as input and a single layer of hidden nodes, the CNN used in this approach generates improved estimates of ice types, outperforming traditional image analysis methods. At each stage, ConvNets use diverse filter banks, feature extraction pooling layers, and fully connected layers with basic activation functions like Relu. This allows the network to build multifaceted hierarchies of features. The sea ice type estimates produced by the CNN are then compared with those obtained from passive microwave brightness temperature data using existing algorithms as well as a proposed CNN algorithm, resulting in an increased classification accuracy of 98.58%. This improvement is particularly notable in the reduction of the error rate, which has been effectively minimized from 3.01% without feature selection to 1.42% with infinite feature selection. When compared to existing algorithms, the CNN demonstrates superior performance. These findings underscore the impact of input patch size, CNN layer count, and input size on the model's performance.

Characteristics and mechanisms of near-surface negative atmospheric electric field anomalies preceding the 5 September 2022, Ms 6.8 Luding earthquake in China

Abstract. A magnitude 6.8 strike-slip earthquake (EQ) struck Luding, Sichuan Province, China, on 5 September 2022, resulting in significant damage to nearby Ganzi Prefecture and the city of Ya'an. In this research, the near-surface atmospheric electric field (AEF) recorded at four sites 15 d before the Luding EQ was analyzed and differentiated, and multisource auxiliary data including precipitation, cloud base height, and low cloud cover were used at the same time. Nine possible seismic AEF anomalies at four sites were obtained preliminarily. Accordingly, microwave brightness temperature (MBT) data, which are very sensitive to the surface dielectrics and are closely related to the air ionization, together with surface soil moisture, lithology, and a 3D-simulated crustal stress field, were jointly analyzed to confirm the seismic relations of the obtained negative AEF anomalies. The geophysical environment for crustal high-stress concentration, positive charge carrier transfer, and surface accumulation was demonstrated to exist and to meet the conditions necessary to generate local negative AEF anomalies. Furthermore, to deal with the spatial disparities in sites and regions with potential atmospheric ionization, near-surface wind field data were employed to scrutinize the reliability of the AEF anomalies by comprehensively analyzing the spatial relationships among surface charges accumulation areas, wind direction and speed, and the AEF sites. Finally, four negative AEF anomalies were deemed to be closely related to the Luding EQ, and the remaining five possible anomalies were ruled out. A possible mechanism of negative AEF anomalies before the Luding EQ is proposed: positive charge carriers were generated from the underground high-stress concentration areas and then transferred to and accumulated on the ground surface to ionize the surface air, thus disturbing the AEF above the ground. This study presents a method for identifying and analyzing seismic AEF anomalies and is also beneficial for the examination of the pre-earthquake coupling process between the coversphere and the atmosphere.

A Theory of Maximum Entropy Production and Its Application to Microwave Remote Sensing—Simultaneous Retrieval of Soil Moisture and Vegetation Water Content

AbstractA theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L‐band microwave brightness temperature (TB). One representation of the MEP principle states that a non‐equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof‐of‐concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g., VWC > 5 kg m−2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to microwave observations.

Identifying Seismic Anomalies via Wavelet Maxima Analysis of Satellite Microwave Brightness Temperature Observations

This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismogenic activity. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five surface categories of the epicenter area were defined contingent on position (oceanic/terrestrial) and ambient traits (soil hydration, vegetal covering). Continuous wavelet transform was applied to preprocess annualized MBT readings preceding and succeeding prototypical events of each grouping, utilizing optimized wavelet functions and orders tailored to individualized contexts. Wavelet maxima graphs visually portraying signal intensity variations facilitated the identification of aberrant phenomena, including pre-seismic accrual, co-seismic perturbation, and postseismic remission signatures. The casework found 10 GHz horizontal-polarized MBT optimally detected signals for aquatic and predominantly humid/vegetative settings, whereas 36 GHz horizontal-polarized performed best for arid, vegetated landmasses. Quantitative machine learning methods are warranted to statistically define selection standards and augment empirical forecasting leveraging lithospheric stress state inferences from sensitive MBT parametrization.

The Relationship Between Microwave Brightness Temperature, Salinity, and Thickness of Sea Ice Acquired With a Tank Experiment

The Relationship Between Microwave Brightness Temperature, Salinity, and Thickness of Sea Ice Acquired With a Tank Experiment

Improvement of Polar Snow Microwave Brightness Temperature Simulations for Dense Wind Slab and Large Grain

Improvement of Polar Snow Microwave Brightness Temperature Simulations for Dense Wind Slab and Large Grain

Estimation of Lightning Flash Rate in Precipitation Features by Applying Shallow AI Neural Network Models to the GMI Passive Microwave Brightness Temperatures

AbstractThe satellite‐constellation passive‐microwave Brightness Temperature (TB) observations, with global coverage, and more additions from upcoming CubeSats, have been mainly used in surface precipitation retrievals. However, these observations can also be used to indicate the intensity of convective systems. This study attempts to relate Global Precipitation Mission (GPM) Microwave Imager (GMI) TBs to Geostationary Lightning Mapper (GLM) lightning flashes by using 4 years (02/2018–04/2022) of GPM Precipitation Feature (PF) database. GMI TBs are collocated to the GLM lightning counts, and to the ERA5 reanalysis 2‐m air temperature in PFs. Three Artificial Intelligent Neural Network Models (AI‐NN) are trained to classify PFs producing lightning in a 20‐min window respectively for land, ocean, or coast. The flash rate of the determined Lightning producing PFs (LPFs) is then quantified by three other AI‐NNs, each trained for one of the three regions. Though the models clearly capture the global geographical distribution of LPFs with a Probability Of Detection over 90%, high False Alarm Rates are found, ranging from 49.9% over land to 91.5% over the ocean. The importance of TB at each passive microwave channel varies regionally, corresponding to the different microphysical properties in various types of precipitation systems. The global lightning distribution is derived by applying the AI models to global PFs and is well compared to the lightning climatology from Lightning Imaging Sensor and Optical Transient Detector. This suggests that the use of passive microwave TBs can help to fill the gaps in lightning monitoring thanks to their global coverage.

Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations.

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0-5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016-2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.

Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)

Abstract To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA’s Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED’s potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts.

Inversion of the Lunar Subsurface Rock Abundance Using CE-2 Microwave Brightness Temperature Data

The rock strongly affects the surface and subsurface temperature due to its different thermophysical properties compared to the lunar regolith. The brightness temperature (TB) data observed by Chang’E-1 (CE-1) and Chang’E-2 (CE-2) microwave radiometers (MRM) give us a chance to retrieve the lunar subsurface rock abundance (RA). In this paper, a thermal conductivity model with an undetermined parameter β of the mixture has been employed to estimate the physical temperature profile of the mixed layer (rock and regolith). Parameter β and the physical temperature profile of the mixed layer are constrained by the Diviner Channel 7 observations. Then, the subsurface RA on the 16 large (Diameter > 20 km) Copernican-age craters of the Moon is extracted from the average nighttime TB of the CE-2 37 GHz channel based on our previous rocky TB model. Two conclusions can be derived from the results: (1) the subsurface RA values are usually greater than the surface RA values retrieved from Diviner observations of the studied craters; (2) the spatial distribution of subsurface RA extracted from CE-2 MRM data is not necessarily consistent with the surface RA detected by Diviner data. For example, there are similar RA spatial distributions on both the surface and subsurface in Giordano Bruno, Necho, and Aristarchus craters. However, the distribution of subsurface RA is obviously different from that of surface RA for Copernicus, Ohm, Sharonov, and Tycho craters.

Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

Abstract Visible and infrared radiance products of geostationary orbiting platforms provide virtually continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave (PMW) radiances at any location much less frequently. Prior literature demonstrates the ability of a machine learning (ML) approach to build a link between these two complementary radiance spectra by predicting PMW observations using infrared and visible products collected from geostationary instruments, which could potentially deliver a highly desirable synthetic PMW product with nearly continuous spatiotemporal coverage. However, current ML models lack the ability to provide a measure of uncertainty of such a product, significantly limiting its applications. In this work, Bayesian deep learning is employed to generate synthetic Global Precipitation Measurement (GPM) Microwave Imager (GMI) data from Advanced Baseline Imager (ABI) observations with attached uncertainties over the ocean. The study first uses deterministic residual networks (ResNets) to generate synthetic GMI brightness temperatures with as little mean absolute error as 1.72 K at the ABI spatiotemporal resolution. Then, for the same task, we use three Bayesian ResNet models to produce a comparable amount of error while providing previously unavailable predictive variance (i.e., uncertainty) for each synthetic data point. We find that the Flipout configuration provides the most robust calibration between uncertainty and error across GMI frequencies, and then demonstrate how this additional information is useful for discarding high-error synthetic data points prior to use by downstream applications.

Pre-earthquake MBT anomalies in the Central and Eastern Qinghai-Tibet Plateau and their association to earthquakes

Thermal anomalies prior to large and hazardous earthquakes have been extensively detected by thermal infrared and microwave remote sensing techniques. However, few of the current research focuses on long-term tracking of the thermal anomalies for decades and their earthquake prediction potential in a seismically active area. Targeting at exploiting the association of thermal anomalies to earthquakes in terms of time, spatial patterns and magnitudes, comprehensive statistical analysis on the pre-earthquake microwave brightness temperature (MBT) anomalies of 103 destructive earthquakes with Ms. ≥ 5.0 in the Central and Eastern Qinghai-Tibet Plateau (90° E – 102° E, 28° N – 38° N) is conducted. To detect robust pre-earthquake thermal anomalies buried by the complex background and frequently variant meteorological noise, we propose a wavelet-based two-step difference (WTSD) approach which incorporates hierarchical clustering and wavelet decomposition, adopting it on the 17 years' microwave data collected by AMSR-E and AMSR-2 sensors. Interestingly, an NE trending MBT increase stripe has been repeatedly occurring prior to 60 of the 103 earthquakes (accounting for 58.25%). Then, we conduct comprehensive statistics on the occurring time, spatial patterns and intensities of the MBT anomalies, and find them closely associated to seismic events with Ms. ≥ 5.0 occurred in the Bayan Har and Qiangtang blocks. And, the MBT anomaly stripe is spatially consistent with the NE trending extensional faults and geothermal fields in this study area. Furthermore, the possible source mechanism of the MBT anomalies is explored with rock loading microwave observation experiments, and possible physical models suggested by predecessors are also discussed. On this basis, we discuss the relationship between the pre-earthquake MBT anomalies and extensional faults, and speculate the MBT anomalies are probably caused by intensified extrusion of the Indian plate to the Eurasian plate and the increased crustal stress, which indicates their significant potential as earthquake precursors in the Central and Eastern Qinghai-Tibet Plateau.