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

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.

Roughness correction method for salinity remote sensing using combined active/passive observations

Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means,which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements.The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application.The combined active/passive observations of normalized radar cross-sections(NRCSs)and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission,and the auxiliary wind directions collocated from the National Centers for Environmental Prediction(NCEP)dataset are used for model development.The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction.Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs,which can be better than 0.3 K.However,for crosswind directions and larger NRCSs,the model accuracy is relatively low.A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones.For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data,the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources.Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.

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.

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

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.

Active-Layer Soil Moisture Content Regional Variations in Alaska and Russia by Ground-Based and Satellite-Based Methods, 2002 through 2014

Soil moisture is a vital physical parameter of the active-layer in permafrost environments, and associated biological and geophysical processes operative at the microscopic to hemispheric spatial scales and at hourly to multi-decadal time scales. While?in-situ?measurements can give the highest quality of information on a site-specific basis, the vast permafrost terrains of North America and Eurasia require space-based techniques for assessments of cause and effect and long-term changes and impacts from the changes of permafrost and the active-layer. Satellite-based 6.925 and 10.65 GHz sensor algorithmic retrievals of soil moisture by Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard NASA-Aqua and follow-on AMSR2 onboard JAXA-Global Change Observation Mission—Water-1 are ongoing since July 2002. Accurate land-surface temperature and vegetation parameters are critical to the success of passive microwave algorithmic retrieval schemes. Strategically located soil moisture measurements are needed for spatial and temporal co-location evaluation and validation of the space-based algorithmic estimates. We compare on a daily basis ground-based (subsurface-probe) 50- and 70-MHz radio-frequency soil moisture measurements with NASA- and JAXA-algorithmic retrieval passive microwave retrievals. We find improvements in performance of the JAXA-algorithm (AMSR-E reprocessed and AMSR2 ongoing) relative to the earlier NASA-algorithm version. In the boreal forest regions, accurate land-surface temperatures and vegetation parameters are still needed for algorithmic retrieval success. Over the period of AMSR-E retrievals, we find evidence of at the high northern latitudes of growing terrestrial radio-frequency interference in the 10.65 GHz channel soil moisture content. This is an important error source for satellite-based active and passive microwave remote sensing soil moisture retrievals in Arctic regions that must be addressed.

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

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.

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.

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.

Monitoring the frozen duration of Qinghai Lake using satellite passive microwave remote sensing low frequency data

The Qinghai Lake is the largest inland lake in China.The significant difference of dielectric properties between water and ice suggests that a simple method of monitoring the Qinghai lake freeze-up and break-up dates using satellite passive microwave remote sensing data could be used.The freeze-up and break-up dates from the Qinghai Lake hydrological station and the MODIS L1B reflectance data were used to validate the passive microwave remote sensing results.The validation shows that passive microwave remote sensing data can accurately monitor the lake ice.Some uncertainty comes mainly from the revisit frequency of satellite overpass.The data from 1978 to 2006 show that lake ice duration is reduced by about 14―15 days.The freeze-up dates are about 4 days later and break-up dates about 10 days earlier.The regression analyses show that,at the 0.05 significance level,the correlations are 0.83,0.66 and 0.89 between monthly mean air temperature(MMAT) and lake ice duration days,freeze-up dates,break-up dates,respectively.Therefore,inter-annual variations of the Qinghai Lake ice duration days can significantly reflect the regional climate variation.

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.

基于MODIS积雪覆盖度数据的青藏高原两套被动微波雪深产品降尺度对比研究

积雪深度(雪深)是流域水量平衡、融雪径流模拟等模型的重要输入参数,被动微波雪深遥感产品被广泛用于雪深监测。然而,由于山区积雪时空异质性强,这些空间分辨率较粗的雪深产品受到极大限制。本研究基于MODIS积雪覆盖度数据,根据经验融合规则以及积雪衰退曲线对“中国雪深长时间序列数据集”的两套雪深产品(由SMMR、SSMI和SSMI/S反演的称为Che_SSMI/S产品;由AMSR-2反演称为Che_AMSR2产品)进行空间降尺度,最终获得青藏高原500 m降尺度雪深数据(Che_SSMI/S_NSD和Che_AMSR2_NSD)。利用6景Landsat-8影像对两套降尺度雪深数据进行对比分析,结果发现两套降尺度数据与Landsat-8影像积雪空间分布吻合度均较高。与29个气象站点雪深数据相比,Che_AMSR2_NSD与实测雪深更为接近,相关系数(R)达到0.72,均方根误差(RMSE)为3.21 cm;而Che_SSMI/S_NSD精度较低(R=0.67,RMSE=4.44 cm),可能是由于采用不同传感器亮温数据的两套原始雪深产品精度不同所致。除此之外,实验表明被动微波雪深产品降尺度精度还受积雪深度、积雪期等因素的影响。当积雪深度小于10 cm且在积雪稳定期时,两套雪深产品降尺度精度均最高;当积雪深度大于30 cm且在积雪消融期时,两套雪深产品降尺度精度均最低。通过对比两套降尺度雪深产品,有助于全面地了解青藏高原雪深时空分布变化及其应用提供数据支持。

综合孔径微波辐射成像技术发展现状与趋势

从综合孔径微波辐射成像技术实际需求和技术特点出发,首先简要回顾了综合孔径微波辐射成像技术的整个发展历程;然后,从地球被动微波遥感和目标被动探测两个应用领域较为全面地介绍了综合孔径微波辐射成像技术的发展现状,包括综合孔径微波辐射成像系统研制和相关重要研究进展等;最后,从高空间分辨率和多手段联合等方面总结了综合孔径微波辐射成像技术的发展趋势。随着综合孔径微波辐射成像技术的发展,其在地球被动微波遥感和目标探测领域将会得到更广泛的应用。

基于微波遥感的土壤水分反演估算研究进展

土壤水分是地表和大气水热过程交换的重要纽带,对于农业生产、生态规划、水资源管理等具有十分重要的意义。微波遥感具有基本不受天气条件影响,具有较好探测植被覆盖下的土壤信息和土壤水分变化趋势等优势,成为目前遥感精确反演土壤水分的热点。本文整理了现有全球尺度的基于微波遥感的土壤水分产品;分析比较了土壤水分反演中主动微波遥感、被动微波遥感、主被动微波协同技术的原理、特点、适用范围和关键技术进展:主动微波遥感和被动微波遥感的优势分别在于高空间分辨率和高时间分辨率,高空间分辨率可以很好捕捉地表细微的空间信息特征,但囿于土壤水分与后向散射系数之间的复杂关系,特别是植被、地表粗糙度等对雷达后向散射系数的干扰,使得反演土壤水分的精度不高,因而根据现实情况选取不同散射模型以及利用多源数据协同是目前改善精度的研究热点。而高时间分辨率可以实现全球及大尺度下的土壤水分监测,但是很难满足小尺度或者小区域范围的实际研究需求,为了能使实测数据在空间上得以较好匹配,提出多种降尺度方法。结合以上两种微波遥感方式的优劣,依托更为丰富的数据源、相对成熟的观测技术来对两者进行融合以提取更多的水分信息,以提升反演精度或者获得长时间序列数据。在目前的方法中,土壤水分反演在小尺度下表现出良好的性能,但在全球尺度上会出现数据缺失、适用性不强、反演精度不高以及反演过程过于复杂等诸多问题,可以借助多种观测方式(多极化、多角度、多波段)、多时相重复观测、在原有模型上引入新的算法以及数据同化等方面着手进行改进,同时全球卫星导航系统(Global Navigation Satellite System,GNSS)中长期稳定、高时空分辨率的L波段微波信号在陆面遥感领域的快速发展也为我国北斗卫星导航系统(BeiDou Navigation Satellite System,BDS)的发展提供了借鉴,展现出在土壤水分反演方面的巨大潜力。

Reconstruction and analysis of temporal and spatial variations in surface soil moisture in China using remote sensing

An ensemble method was used to combine three surface soil moisture products,retrieved from passive microwave remote sensing data,to reconstruct a monthly soil moisture data set for China between 2003 and 2010.Using the ensemble data set,the temporal and spatial variations of surface soil moisture were analyzed.The major findings were:(1) The ensemble data set was able to provide more realistic soil moisture information than individual remote sensing products;(2) during the study period,the soil moisture increased in semiarid regions and decreased in arid regions with anoverall drying trend for the whole country;(3) the soil moisture variation trends derived from the three retrieval products and the ensemble data differ from each other but all data sets show the dominant drying trend for the summer,and that most of the drying regions were in major agricultural areas;(4) compared with the precipitation trends derived from Global Precipitation Climatology Project data,it is speculated that climate change is a possible cause for the drying trend in semiarid regions and the wetting trend in arid regions;and (5) combining soil moisture trends with land surface temperature trends derived from Moderate Resolution Imaging Spectroradiomete,the study domain was divided into four categories.Regions with drying and warming trends cover 33.2%,the regions with drying and cooling trends cover 27.4%,the regions with wetting and warming trends cover 21.1% and the regions with wetting and cooling trends cover 18.1%.The first two categories primarily cover the major grain producing areas,while the third category primarily covers nonarable areas such as Northwest China and Tibet.This implies that the moisture and heat variation trends in China are unfavorable to sustainable development and ecology conservation.

复合负反馈技术在宽带有源微波冷噪声源中的应用

针对C波段宽带有源微波冷噪声源设计中输出低温噪声性能与工作带宽的矛盾,文中选用Avago公司的ATF38143型GaAs基赝掺杂高电子迁移率晶体管(pHEMT)设计了一款C波段宽带有源微波冷噪声源。采用串、并联负反馈电路拓扑结构,在保证低温噪声输出性能的同时提升了工作带宽,其中心频点为6.95 GHz,工作带宽为1.3 GHz,相对带宽为18.7%,常温条件下带内输出最低噪声温度为141.62 K,工作频段可覆盖C波段微波辐射计中心频点为6.60 GHz、6.93 GHz、7.30 GHz的三个通道。