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| 1 | A Method for Surface Roughness Parameter Estimation in Passive Microwave Remote Sensing显示文摘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. | ZHENG Xingming ZHAO Kai | 2010 | Chinese Geographical Science2010，20，4： | 4 |
| 2 | An AMSR-E Data Unmixing Method for Monitoring Flood and Waterlogging Disaster显示文摘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. | GU Lingjia ZHAO Kai ZHANG Shuang ZHENG Xingming | 2011 | Chinese Geographical Science2011，21，6： | 2 |
| 3 | Effects of Snow Cover on Ground Thermal Regime: A Case Study in Heilongjiang Province of China显示文摘The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial(the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive(warmer) effect of snow cover on the ground temperature(GT) on the daily basis, the highest difference between GT and daily mean air temperature(DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm. | LI Xiaofeng ZHENG Xingming WU Lili ZHAO Kai JIANG Tao GU Lingjia | 2016 | Chinese Geographical Science2016，26，4： | 2 |
| 4 | Dynamic b_p in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval显示文摘The parameter b_p in the tuo-omega(τ–ω)model is important for retrieving soil moisture data from passive microwave brightness temperatures.Theoretically,b_p depends on the observation mode(polarization,frequency,and incidence angle)and vegetation properties and varies with vegetation growth.For simplicity,previous studies have taken b_p to be a constant.However,to reduce the uncertainty of soil moisture retrieval further,the present study is of the dynamics of b_p based on the SMAPVEX12 experimental dataset by combining a genetic algorithm and the L-MEB microwave radiative transfer model of vegetated soil.The results show the following.First,b_p decreases nonlinearly with vegetation water content(VWC),decreasing critically when VWC becomes less than 2 kg/m^2.Second,there is a power law between b_p and VWC for both horizontal and vertical polarizations(R^2=0.919 and 0.872,respectively).Third,the effectiveness of this relationship is verified by comparing its soil-moisture inversion accuracy with the previous constant-b_p method based on the HiWATER dataset.Doing so reveals that the dynamic b_p method reduces the root-mean-square error of the retrieved soil moisture by approximately 0.06 cm^3/cm^3,and similar improvement is obtained for the calibrated SMAPVEX12 dataset.Our results indicate that the dynamic b_p method is reasonable for different vegetation growth stages and could improve the accuracy of soil moisture retrieval. | JIANG Tao ZHAO Kai ZHENG Xingming CHEN Si WAN Xiangkun | 2019 | Chinese Geographical Science2019，29，2： | 1 |
| 5 | A newly-designed self-powered electrochromic window显示文摘By converting incident light into electric power,self-powered electrochromic window(SP-ECW)can achieve color change in electrochromic layer with no need for external voltage.In this work,a newly-de signed SP-ECW is proposed for altering its color between deep blue and colorless state according to on/off state of incident light.The device consists of a working electrode with planar integration of photovoltaic(PV)and electrochromic(EC)elements on one electrode,a platinum counter electrode and a redox electrolyte comprising Br^-/Br_3^-couple.A high transmittance modulation of 41%at 582 nm is obtained.Electrical energy converted from light is not only sufficient to drive the device,but also can be outputted to the external circuit. | Xingming Wu Jianming Zheng Chunye Xu | 2017 | Science China Chemistry2017，60，1： | 1 |
| 6 | An investigation on microwave transmissivity at frequencies of 18.7 and 36.5 GHz for diverse forest types during snow season显示文摘Forests have invariably been considered as an obstacle in retrieving land surface parameters from spaceborne passive microwave brightness temperature(T_(B))observations.For quantifying the effect of forests on microwave signals,several models have been developed.However,these models rarely reveal the dependence of microwave radiation on forest types,which can hardly meet the needs of high-accuracy retrieval of terrestrial parameters in forested regions.A ground-based microwave radiometric observation experiment was designed to investigate the dependence of microwave radiation on frequency,polarization,and forest type.Downward TB at 18.7-and 36.5-GHz for horizontal-and vertical-polarization from the forest canopy was measured at 14 sample plots in Northeast China,along with snowpack and forest structural parameters.By providing fits to experimental data,new empirical transmissivity models for three forest types were developed,as a function of woody stem volume and depending on the frequency/polarization.The proposed models give diverse asymptotic transmissivity saturation levels and the corresponding saturation point of woody stem volume for different forest types.Root-mean-square error results between T_(B) simulations and Advanced Microwave Scanning Radiometer-2 observations are approximately 3-6 K.This study provides an experimental and theoretical reference for further development of inversion models for snow parameters in forested areas. | Wang Guangrui Li Xiaofeng Chen Xiuxue Jiang Tao Zheng Xingming Wei Yanlin Wan Xiangkun Wang Jian | 2021 | International Journal of Digital Earth2021，14，10： | 1 |
| 7 | A novel fine-resolution snow depth retrieval model to revealdetailed spatiotemporal patterns of snow cover in NortheastChina显示文摘Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018. | Yanlin Wei Xiaofeng Li Lingjia Gu Xingming Zheng Tao Jiang | 2023 | International Journal of Digital Earth2023，16，1： | 1 |
| 8 | Spatiotemporal Changes of Snow Depth in Western Jilin,China from 1987 to 2018显示文摘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. | WEI Yanlin LI Xiaofeng GU Lingjia ZHENG Zhaojun ZHENG Xingming JIANG Tao | 2024 | Chinese Geographical Science2024，34，2： | 0 |
| 9 | Rapid Diagnosis with FISH for Chromosomal Abnormality of Fetal Pyelectasia显示文摘因为 pyelectasis 是正常的,在 situ 杂交(鱼) 的荧光被用来调查胎儿的染色体是否出生前地诊断了。羊膜的液体从其胎儿被出生前的检查与 pyelecta-sia 检测的怀孕女人被拿。没有文化的羊膜的液体房间的染色体带着鱼被检验。与传统的羊膜的液体房间文化相比,钓鱼的结果表演有更多的快速的、更高的敏感和特性的优点,并且更早是 10-12 天完成比传统的方法诊断。胎儿在每个组检测了 chromosomal 反常当有正常染色体的那些胎儿继续怀孕时,在中间、迟了的三个月期间被导致,当 pyelectasia 的严厉增加了, pyelectasia 的自发的消失的率减少了。鱼能由于它的快在临床的出生前的诊断满足迫切需要决定有 pyelectasia 的胎儿是否伴有 chromosomal。 | HUANG Fenghua ZHENG Xingming ZHANG Yuanzheng XIAOLiping LIN Li | 2008 | Wuhan University Journal of Natural Sciences2008，13，2： | 0 |
| 10 | A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries显示文摘Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping. | GUO Tianhao ZHENG Jia WANG Chunmei TAO Zui ZHENG Xingming WANG Qi LI Lei FENG Zhuangzhuang WANG Xigang LI Xinbiao KE Liwei | 2023 | Chinese Geographical Science2023，33，4： | 0 |
| 11 | Classification and Spatial Pattern of Township Development in Liaoning Province, China显示文摘The classification of township development types is an urgent problem that requires solution to enable the township to choose an appropriate development path.Using a township development classification method,we determine the township development types and their spatial patterns in Liaoning Province,China.The results showed that the patterns of township development types based on their general advantages had significant spatial differentiations.The planting,and livestock and poultry breeding township development types based on general advantages were mainly distributed across the central plain of Liaoning Province,China,and also concentrated in Dandong City−Dalian City along Yellow Sea coast,and in the northwest of Chaoyang City.The business and tourism,industry and mining,and residence township development types based on general advantages were distributed mainly along the Shenyang–Dalian Economic Belt in the central and southern Liaoning Province.The ecology township development type based on general advantages was mainly distributed in the eastern and western Liaoning Province to maintain regional ecological security.Township development types based on non-advantages were sporadically distributed in the middle and western Liaoning Province.Based on the classification and spatial patterns,the differences between the distribution of twonship development types and the plan for the major functional areas of Liaoning Province were proposed which could provide the basis for the optimization of the major functional areas. | LIU Xiaohui ZHENG Xingming LIU Wenxin CHEN Xinyu | 2023 | Chinese Geographical Science2023，33，4： | 0 |
| 12 | An Evaluation on the Effects of the Policy of the Great Campaign of Western Development of China in the First 10 Years Based on the Kuznets Regional Inverted-U Theory显示文摘 | Huan Zheng Xingming Fang | 2013 | Chinese Business Review2013，12，10： | 0 |
| 13 | Predicting Surface Roughness and Moisture of Bare Soils Using Multi- band Spectral Reflectance Under Field Conditions显示文摘Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery. | CHEN Si ZHAO Kai JIANG Tao LI Xiaofeng ZHENG Xingming WAN Xiangkun ZHAO Xiaowei | 2018 | Chinese Geographical Science2018，28，6： | 0 |
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