Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.

A Method for Surface Roughness Parameter Estimation in Passive Microwave Remote Sensing

Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.

STUDY AND APPLICATION OF THE AERIAL PASSIVE MICROWAVE REMOTE SENSING

Ⅰ. Introduction Over the past two decades, microwave remote sensing has evolved into a focal point in the remote sensing area. This is due to the fact that in microwave band, we can acquire physical parameters about ocean, terrain and atmosphere on all weather condition. Research and application work about the aerial passive micro wave remote sensors has been done at Changchun Institute of Geography since 1973, under the unitary planning of Academia Sinica. Microwave radiometers of six freqency bands have been developed. Numerous remote sensing experiments were carried out, and large amount of scientific data were accumulated. Recently, theoretical models have

Review on retrieval of lunar regolith thickness by active and passive microwave measurements

It is one of the important methods to retrieve lunar regolith thickness using active and passive microwave techniques.The retrieval of lunar regolith thickness is based on microwave radiation transfer process simulation in the regolith media.The lunar regolith model is first introduced,and the features of the involved physical parameters are indicated thereafter,such as dielectric constants,surface roughness,particle size and thermal grads of the lunar regolith.The time delay and the migration of the radar echoes from the different interfaces is the key problem for active microwave measurement.And the simulation of the microwave radiative transfer in the regolith media is the important technique for the passive microwave measurement.The important parameters and the physical mechanism for the two measurements are also presented.

Effects of spatial distribution of soil parameters on soil moisture retrieval from passive microwave remote sensing

In this paper,we studied the effect of spatial distribution of soil parameters on passive soil moisture retrieval at pixel scale.First,we evaluated the forward microwave emission model and soil moisture retrieval algorithm accuracy through the observa-tion of field experiments.Then,we used soil parameters in different spatial distribution patterns,including random,normal,and uniform distribution,to determine the different levels of heterogeneity on soil moisture retrieval,in order to seek the rela-tionship between heterogeneity and soil moisture retrieval error.Finally,we conducted a controlled heterogeneity effect ex-periment measurements using a Truck-mounted Multi-frequency Radiometer(TMMR) to validate our simulation results.This work has proved that the soil moisture retrieval algorithm had a high accuracy(RMSE=0.049 cm3 cm 3) and can satisfy the need of this research.The simulation brightness temperatures match well with observations,with RMSE=9.89 K.At passive microwave remote sensing pixel scale,soil parameters with different spatial distribution patterns could have different levels of error on soil moisture estimation.Overall,we found that soil moisture with a random distribution in a satellite pixel scale can cause the largest error,with a normal distribution being the second,and a uniform distribution the least due to the smallest het-erogeneity.

A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing

Lake ice phenology(LIP)is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts.Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe,North America,and the Tibetan Plateau,but there is a lack of data for inner Eurasia.In this work,enhanced-resolution passive microwave satellite data(PMW)were used to investigate the Northern Hemisphere Lake Ice Phenology(PMW LIP).The Freeze Onset(FO),Complete Ice Cover(CIC),Melt Onset(MO),and Complete Ice Free(CIF)dates were derived for 753 lakes,including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020.Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF,respectively,and the corresponding values of the RMSE were 11.84 and 10.07 days.The lake ice phenology in this dataset was significantly correlated(P<0.001)with that obtained from Moderate Resolution Imaging Spectroradiometer(MODIS)data-the average correlation coefficient was 0.90 and the average RMSE was 7.87 days.The minimum RMSE was 4.39 days for CIF.The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations.The PMW LIP dataset pro-vides the basic freeze-thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere.The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.

Spatial and Temporal Variability of Snow Depth Derived from Passive Microwave Remote Sensing Data in Kazakhstan

Snow cover plays an important role in the hydrological cycle and water management in Kazakhstan. However, traditional observations do not meet current needs. In this study, a snow depth retrieval equation was developed based on passive microwave remote sensing data. The average snow depth in winter （ASDW）, snow cover duration （SCD）, monthly maximum snow depth （MMSD）, and annual average snow depth （AASD） were derived for each year to monitor the spatial and temporal snow distributions. The SCD exhibited significant spatial variations from 30 to 250 days. The longest SCD was found in the mountainous area in eastern Kazakhstan, reaching values between 200 and 250 days in 2005. The AASD increased from the south to the north and maintained latitudinal zonality. The MMSD in most areas ranged from 20 to 30 cm. The ASDW values ranged regularity of latitudinal zonality from 15 to 20 cm in the eastern region and were characterized by spatial The ASDW in the mountainous area often exceeded 20 cm.

Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia,and the lagging updates of datasets do not meet the operational requirements of the meteorological service department.This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work.These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3(FY-3)satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites,which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration.Firstly,inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors.A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established,overcoming the large uncertainties induced by varying snow characteristics.This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth.The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ.Against in situ snow depth,the overall bias and root mean square error are−2.04 and 6.49 cm,respectively,facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product,which adopts the static algorithm.Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths,demonstrating a noticeable reduction since around 2000.The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

Advances in Research on Soil Moisture by Microwave Remote Sensing in China

Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active re- mote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.

Classification of ice and water in the Arctic using radar altimeter and microwave radiometer data from HY-2B satellite

Several Chinese marine satellites have been launched in recent years.Monitoring sea ice and the ocean in the Arctic is of great importance for climate research.Sea ice in the Arctic has changed rapidly during the past few decades with respect to the extent and thickness.In this study,we applied combined passive and active microwave data from the Chinese HaiYang-2B(HY-2B)satellite to classify ice and sea water in the Arctic.We use data from a radar altimeter(RA)and a calibration microwave radiometer(CMR)to discriminate between ice and water by applying several approaches(1)the single parameter threshold criteria,(2)the multi-parameters linear segmentations and(3)the K-means clustering.The results yielded by these methods were in good agreement(classification accuracy>95%)with the Satellite Application Facility on Ocean and Sea Ice products between November and April.For other months(May–October),however,the agreement was less good(lowest classification accuracy approximate 85%in summer).A hybrid approach combined with graphical ice edges detection and microwave radar waveform analysis is therefore developed.A visual comparison with SAR images suggested the hybrid approach results greatly improved the ice and water discrimination in summer.This study demonstrated that multi-sensors(RA and CMR)configurations from HY satellites can offer comparable polar earth observation to the European Space Agency and NOAA satellite products.

Spatiotemporal Changes of Snow Depth in Western Jilin,China from 1987 to 2018

Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to glob-al warming conditions.However,the spatiotemporal snow cover patterns are challenging in western Jilin,China due to natural condi-tions and sparse observation.Hence,this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive mi-crowave(PMW)snow depth(SD)data from 1987 to 2018,and revealed the potential influence of climate factors on SD variations.The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend(P>0.05)at a rate of 0.009 cm/yr.In snowmelt periods,the snow cover contributed to an increase in volumetric soil water,and the change in SD was significantly affected by air temperature.The correlation between SD and air temperature was negative,while the correlation between SD and precipitation was positive during December and March.In March,the correlation coefficient exceeded 0.5 in Zhenlai,Da’an,Qianan,and Qianguo counties.However,the SD and precipitation were neg-atively correlated over western Jilin in October,and several subregions presented a negative correlation between SD and precipitation in November and April.

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 exceed0.98.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.

Brightness temperature simulation of subarctic lake-ice evolution by HIGHTSI and SMRT model

Microwave brightness temperature(TB)can be used to retrieve lake ice thickness in the Arctic and subarctic regions.However,the accuracy of the retrieval is affected by the physical properties of lake ice.To improve the understanding of how lake ice affects TB,numerical modeling was applied.This study combined a physical thermodynamic ice model HIGHTSI with a microwave radiation transfer model SMRT to simulate the TB and lake ice evolution in 2002-2011 in Hulun Lake,China.The reanalyzed meteorological data were used as atmospheric forcing.The ice season was divided into the growth period,the slow growth period,and the ablation period.The simulations revealed that TB was highly sensitive to ice thickness during the ice season,especially vertical polarization measurement at 18.7 GHz.The quadratic polynomial fit for ice thickness to TB outperformed the linear fit,regardless of whether lake ice contained bubbles or not.A comparison of the simulated TB with space-borne TB showed that the simulated TB had the best accuracy during the slow growth period,with a minimum RMSE of 4.6 K.The results were influenced by the bubble radius and salinity.These findings enhance comprehension of the interaction between lake ice properties(including ice thickness,bubbles,and salinity)and TB during ice seasons,offering insights to sea ice in the Arctic and subarctic freshwater observations.

An AMSR-E Data Unmixing Method for Monitoring Flood and Waterlogging Disaster

Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spatial resolution,they are often interfered by clouds,haze and rain.As a result,it is very difficult to retrieve ground information from spectral remote sensing data under those conditions.Compared with spectral remote sensing tech-nique,passive microwave remote sensing technique has obvious superiority in most weather conditions.However,the main drawback of passive microwave remote sensing is the extreme low spatial resolution.Considering the wide ap-plication of the Advanced Microwave Scanning Radiometer-Earth Observing System(AMSR-E) data,an AMSR-E data unmixing method was proposed in this paper based on Bellerby's algorithm.By utilizing the surface type classifi-cation results with high spatial resolution,the proposed unmixing method can obtain the component brightness tem-perature and corresponding spatial position distribution,which effectively improve the spatial resolution of passive microwave remote sensing data.Through researching the AMSR-E unmixed data of Yongji County,Jilin Provinc,Northeast China after the worst flood and waterlogging disaster occurred on July 28,2010,the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.

A Neural Network Method for Monitoring Snowstorm: A Case Study in Southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions.With advancement of remote sensing and better understanding of frozen soil dynamics,discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change.However,as an important data source of frozen soil processes,remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes.Although great progress has been made in remote sensing and frozen soil physics,yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies.In the present study,a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed.In order to reduce the uncertainty of the simulation,the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation.The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau.The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%.These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study.The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory.The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil.The average accuracy increased by about 5%after integrating remotely sensed information on the surface soil.The simulation accuracy was significantly improved,especially in transition periods between freezing and thawing of the surface soil.

Variation of Antarctic marginal ice zone extent (1989-2019)

The Antarctic marginal ice zone(MIZ)is the transition region between open water and consolidated pack ice,which is defined as an area with 15%-80%sea ice concentration.The MIZ represents the outer circle of Antarctic sea ice and the biological activity circle of Antarctic organisms,which provides a direct indication of the extent of Antarctic sea ice.In this study,the joint total variation and nonnegative constrained least square algorithm are applied to retrieve the Antarctic MIZ extent based on passive microwave data sets from 1989 to 2019.The spatial and temporal variations of the Antarctic MIZ extent and five regions are analyzed.The results show that the Antarctic MIZ extent follows a strong monthly variation pattern,decreasing from November to February and increasing from March to October.The annual MIZ extent is largest in the Weddell Sea and smallest in the Western Pacific Ocean.The edge of the sea ice begins to form a closed ring in May,which eventually closes near the Antarctic Peninsula.The ring width variation is large in summer,but generally stabilizes between 350 and 370 km in winter.The average latitude of the Antarctic MIZ is relatively stable in summer,but changes substantially in winter with a difference of approximately 3°.In October,the lowest mean latitude of the MIZ can reach 64.35°S.The sea surface pressure,2-m temperature,and 10-m wind speed are negatively correlated with the MIZ extent variation,among which the second-order partial correlation coefficient of the sea surface pressure and MIZ extent is−0.8773 in the Western Pacific Ocean.

Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data

The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice.By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth.This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers.Based on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice.The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S.National Snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.

CPW Circulators with Barium Ferrite Thin Films

A miniaturized circulator using barium ferrite films with a coplanar waveguide （CPW） structure is designed and optimized by high frequency electromagnetic field simulations based on finite element methods. The best circulation performance of the film circulator based on 10 pm thick barium ferrite thin films is obtained with an insertion loss of 0.13 dB and an isolation of 22.89 dB around 36.9 GHz. The microwave characteristics of film circulators with CPW and CPW with ground （CPWG） structures have been compared. The influences of the gap between the ground and the signal line, and the ferromagnetic resonance line width on the microwave properties are also studied.

Optimal Gridding Process for GMI Brightness Temperature Using the Backus−Gilbert Method

Satellite microwave instruments have different field of views(FOVs)in different channels.A direct average technique(“direct method”)is frequently used to generate gridded datasets in the earth science community.A large FOV will measure radiance from outside the area of a designated grid cell.Thus,the direct method will lead to errors in a measurement over a grid cell because some pixels covering areas outside of the cell are involved in the averaging process.The Backus−Gilbert method(BG method)is proposed and demonstrated to minimize those uncertainties.Three sampling resolutions(6.5 km×6.0 km,11.5 km×6.0 km,13.0 km×6.0 km)are analyzed based on the scanning characteristics of the Global Precipitation Measurement(GPM)Microwave Imager(GMI)18.9-GHz channel.Brightness temperatures(TBs)at 0.5 km×0.5 km resolution over eastern China are used to obtain synthetic 18.9-GHz TBs at the three sampling resolutions.The direct and BG methods are both applied to create a 25 km×25 km gridded dataset and their related uncertainties are analyzed.Results indicate the error variances with the direct method are 3.00,3.68 and 4.99 K2 at the three sampling resolutions,respectively.By contrast,the BG method leads to a much smaller error variance than the direct method,especially over areas with a large TB gradient.Two GMI orbital measurements are applied to verify the BG method for gridding process is reliable.The BG method could be utilized for general purpose of creating a gridded dataset.