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Monitoring of rice crop using ENVISAT ASAR data

science china Earth sciences 2006年第7期49卷 755-763页

作者： dong yanfang1, 2, sun guoqing1,3 & pang {4. state key laboratory of remote Sensing science, institute of remote Sensing applications, chinese academy of sciences,{14., china 2. Graduate University of chinese academy of sciences, beijing 100039, china 3. Department of Geography, University of Maryland, College Park, MD 2074., USA 4. Forest remote Sensing Lab, Research institute of Forest Resource and Information Technology, chinese academy of For-estry, beijing 100091, china 1. state key laboratory of remote Sensing {4. Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China 2. Graduate University of chinese academy of sciences Beijing 100039 China 3. Department of Geography University of Maryland College Park MD 20742 USA 4. Forest remote Sensing Lab Research Institute of Forest Resource and Information Technology Chinese Academy of Forestry Beijing 100091 China

Because of the cloudy conditions during the rice growth period, rice is one of the agri-cultural crops most suited to monitoring with the SAR instruments. Backscatter response measured by SAR is correlated with rice conditions, including height, density, biomass and structure, which are variable at different growing stages. In this paper, multi-date ENVISAT ASAR Alternating Polarization Mode (APMode) imageries were acquired during the rice crop growing cycle. At the same time, the rice parameters were measured in field. A continuous canopy model was used to compute the back-scattering from rice fields during the growth cycle, and the relationship between rice parameters and radar backscattering coefficients from both ASAR and modeling was analyzed. The effects of polari-zation, incidence angle and polarization on radar backscattering coefficients were analyzed. It was found that simulated radar backscatter has similar trends as ASAR data. This will be meaningful for the further research of rice parameters estimation from ASAR data. Different features show signifi-cantly different signatures in ASAR images and they follow some certain laws, so rice area can be accurately mapped by using multi-temporal SAR images, then rice yield can be estimated.

关键词： rice, ASAR, radar backscattering, model.

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Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

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science china Earth sciences 2010年第S1期53卷 16-25页

作者： GUO ZhiFeng1,CHI Hong1,2 & sun guoqing1,3 1state key laboratory of {4. Sensing science,Jointly Sponsored by the institute of {4. Sensing applications of chinese academy of sciences and beijing Normal University,institute of {4. Sensing applications,{14. academy of sciences,beijing 100101,china 2Graduate University of chinese academy of sciences,beijing 10004.,china 3Department of Geography University of Maryland,College Park,MD 2074.,USA 1. state key laboratory of remote Sensing {4. Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China2. Graduate University of Chinese Academy of Sciences Beijing 100049 China3. Department of Geography University of Maryland College Park MD 20742 USA

The ecosystem in northeastern china and the Russian Far East is a hotspot of scientific research into the global carbon *** aboveground biomass(AGB) is an important component in the land surface carbon *** this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern china by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory *** resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf *** models were used to estimate biomass for all GLAS footprints of forest located in the study *** GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 *** data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74.t *** pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this *** was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estim

关键词： Disaster and Environment Monitoring and Forecast Satellite ICESat GLAS waveform boreal forest forest aboveground biomass multiple linear regression

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Improvement of a 3D radar backscattering model using matrix-doubling method

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science china Earth sciences 2010年第7期53卷 1029-1035页

作者： NI WenJian1,2, GUO ZhiFeng1 & sun guoqing3 1 state key {4. of remote Sensing science, jointly sponsored by the institute of remote Sensing applications of chinese academy of sciences and beijing Normal University, beijing 100101, china 2 Graduate University of chinese academy of sciences, beijing 10004., china 3 Department of Geography, University of Maryland, College Park, MD 2704., USA 1. state key laboratory of remote Sensing {4. jointly sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University Beijing 100101 China2. Graduate University of Chinese Academy of Sciences Beijing 100049 China3. Department of Geography University of Maryland College Park MD 27042 USA

Radiative transfer models have been widely used to simulate the radar backscattering from forested areas. A three-dimensional radar backscatter model of forest canopy developed in previous studies takes full account of spatial position of trees in a forest stand, and the interactions among crown, trunk and ground surface. The model predicted well for the co-polarized backscatter measurements, but underestimated the backscattering for cross-polarization, primarily because only the first-order scattering within tree crowns was considered in the model. The backscattering at cross-polarization depends strongly on multiple scatter- ing within tree crowns. To produce good estimations for cross-polarized component, the matrix-doubling method is employed here to compute multiple-scattering within the crown. The modified model is compared with the original model, and the field forest measurements and AIRSAR data are used for validation of the modified model. The cross-polarization backscattering is improved in different degrees for different crown structures and at different bands.

关键词： remote sensing forestry electromagnetic propagation radar backscattering model matrix-doubling

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Long-term(2013-2022)mapping of winter wheat in the North china Plain using Landsat data:classification with optimal zoning strategy

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Big Earth Data 2024年第3期8卷 494-521页

作者： Yifei Liu Xuehong Chen Jin Chen Yunze Zang Jingyi Wang Miao Lu Liang sun Qi dong Bingwen Qiu Xiufang Zhu state key laboratory of remote Sensing {4. Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina beijing Engineering Research Center for Global Land remote Sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina state key laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)BeijingChina state key laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern china Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina key laboratory of Spatial Data Mining&Information Sharing of Ministry of Education Academy of Digital China(Fujian)Fuzhou UniversityFuzhouChina

security and formulating agricultural *** data are the only available source for long-term winter wheat mapping in the North china Plain due to the fragmented landscape in this *** various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse *** learning is an effective method for crop classification using Landsat ***,applying machine learn-ing for winter wheat mapping in the North china Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this *** address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop ***,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification *** each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI *** sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different *** derived winter wheat area also has a good correlation(R2=0.94.)with census area at the provincial *** results underscore the reliability of the produced annual winter wheat *** experiments demonstrate that

关键词： Winter wheat mapping Landsat machine learning North china Plain optimal zoning

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Assessment of Slope-Adaptive Metrics of GEDI Waveforms for Estimations of Forest Aboveground Biomass over Mountainous Areas

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Journal of remote Sensing 2021年第1期2021卷 180-196页

作者： Wenjian Ni Zhiyu Zhang guoqing sun state key laboratory of remote Sensing {4. Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101China University of chinese academy of sciences Beijing 100049China Department of Geographical sciences University of MarylandCollege ParkMD 20742USA

Waveform broadening effects of large-footprint lidar caused by terrain slopes are still a great challenge limiting the estimation accuracy of forest aboveground biomass(AGB)over mountainous ***-adaptive metrics of waveforms were proposed in our previous ***,its validation was limited by the unavailability of enough reference *** study made full validation of slope-adaptive metrics using data acquired by the Global Ecosystem Dynamics Investigation(GEDI)mission,meanwhile exploring GEDI waveforms on estimations of forest *** types of waveform metrics were employed,including slope-adaptive metrics(RHT),typical height metrics relative to ground peaks(RH),and waveform parameters(WP).In addition to terrain slopes,two other factors were also explored including the geolocation issue and signal start and ending points of *** showed that footprint geolocations in the first version GEDI data products were shifted to the left forward of nominal geolocations with a distance of about 24.m~30 m and were substantially corrected in the second version;the fourth and fifth groups of signal start and ending points of waveforms had worse performance than the rest of the four groups because they used the maximum and minimum signal thresholds,*** airborne laser scanner(ALS)data as reference,the root mean square error(RMSE)of terrain slopes extracted from the digital elevation model of the shuttle radar topography mission(SRTM DEM)was about 3°.The coefficients of determination(R^(2))of estimation models of forest AGB based on RH metrics were improved from 0.4. to 0.68 with RMSE decreased from 19.7 Mg/ha to 15.4.Mg/ha by the second version *** RHT and WP metrics gave the best and the worst estimation accuracy,*** further improved R^(2) to 0.77 and decreased RMSE to 13.0 Mg/ha using terrain slopes extracted from SRTM DEM with a resolution of 1 arc second.R^(2) of estimation models based on RHT was finally

关键词： terrain estimation ground

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Characterization of ASTER GDEM elevation data over vegetated area compared with lidar data

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International Journal of Digital Earth 2015年第3期8卷 198-211页

作者： Wenjian Ni guoqing sun Kenneth J.Ranson state key laboratory of remote Sensing {4. Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina Department of Geographical sciences University of MarylandCollege ParkMDUSA Biospheric sciences Branch NASA’s Goddard Space Flight CenterGreenbeltMDUSA

Current researches based on areal or spaceborne stereo images with very high resolutions(<1 m)have demonstrated that it is possible to derive vegetation height from stereo *** second version of the Advanced Spaceborne... 详细信息

Current researches based on areal or spaceborne stereo images with very high resolutions(state-of-the-art global elevation data-set developed by stereo ***,the resolution of ASTER stereo images(15 m)is much coarser than areal stereo images,and the ASTER GDEM is compiled products from stereo images acquired over 10 *** forest disturbances as well as forest growth are inevitable in 10 years time *** this study,the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar *** factors possibly affecting the extraction of vegetation canopy height considered include(1)co-registration of DEMs;(2)spatial resolution of digital elevation models(DEMs);(3)spatial vegetation structure;and(4.terrain *** results show that the accurate coregistration between ASTER GDEM and national elevation dataset(NED)is necessary over mountainous *** correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.4. by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.

关键词： vegetation canopy height ASTER GDEM NED photogrammetry stereo-mapping

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Numerical analysis of landslide-generated debris flow on July 3, 2021 in Izu Mountain area, Shizuoka County, Japan

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Journal of Mountain science 2022年第6期19卷 1738-1747页

作者： sun Tong sun dong-ya WANG Xie-kang MA Qiang GOURBESVILLE Philippe NOHARA Daisuke state key laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China china institute of Water Resources and Hydropower Research Beijing 100038China Universite Cote d'Azur Nice 06903France Sustainable Society laboratory KAJIMA Technical Research institute Kyoto University 6110011Japan

With the increasing of extreme rainfall frequency, landslides accompanied by mudslides often lead to serious casualties and property damage. On 3rd July 2021, a debris flow occurred in Izu Mountain area, Shizuoka County, Japan, and then resulted in 26 deaths and 131 houses destroyed, where houses were mainly built along the banks of the creek. In order to analyse the landslide state and distribution, a two-dimensional debris flow dynamic model(Massflow) was selected to simulate the process of the landslide-generated debris flow. When the model results are considered together with remote sensing images, the volume distribution of the unstable landslide is also able to be determined. The results show that(1) the affected areas are mainly concentrated at the outfall of the gully and on both sides of the streets.(2) The pore pressure ratio is an important factor affecting the damage range of this debris flow.(3) The increase of the pore pressure ratio in the landslide make the movement distance of debris flow increase significantly.

关键词： Izu Mountain Landslide Debris flow Numerical simulation Pore pressure ratio

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Scale Effect of Climate and Soil Texture on Soil Organic Carbon in the Uplands of Northeast china

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Pedosphere 2010年第4期20卷 525-535页

作者： WANG Dan-Dan SHI Xue-Zheng WANG Hong-Jie D. C. WEINDORF YU dong-Sheng sun Wei-Xia REN Hong-Yan ZHAO yong-Cun state key laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of SciencesNanjing 210008 （China） College of remote Sensing Nanjing University of Information Science and Technology Nanjing 210044 （China） Louisiana state University AgCenter Baton Rouge Louisiana 70803 （USA） Graduate University of the chinese academy of sciences Beijing 100049 （China）

Understanding how spatial scale influences commonly-observed effects of climate and soil texture on soil organic carbon （SOC） storage is important for accurately estimating the SOC pool at different scales. The relationships among climate factors, soil texture and SOC density at the regional, provincial, city, and county scales were evaluated at both the soil surface （0-20 cm） and throughout the soil profile （0-100 cm） in the Northeast china uplands. We examined 1 022 profiles obtained from the Second National Soil Survey of china. The results indicated that the relationships between climate factors and SOC density generally weakened with decreasing spatial scale. The provincial scale was optimal to assess the relationship between climate factors and SOC density because regional differences among provinces were covered up at the regional scale. However, the relationship between soil texture and SOC density had no obvious trend with increasing scale and changed with temperature. There were great differences in the impacts of climate factors and soil texture on SOC density at different scales. Climate factors had a larger effect on SOC density than soil texture at the regional scale. Similar trends were seen in Heilongjiang and eastern Inner Mongolia at the provincial scale. But, soil texture had a greater effect on SOC density compared with climate factors in Jilin and Liaoning. At the city and county scales, the influence of soil texture on SOC density was more important than climate factors.

关键词： factor precipitation spatial scale temperature

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ICESat-2植被冠层高度和地表高程数据产品用于森林高度提取的效果评价

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遥感学报 2021年第6期25卷 1294-1307页

作者：董佳臣倪文俭张志玉孙国清中国科学院空天信息创新研究院遥感科学国家重点实验室北京100094 中国科学院大学资源与环境学院北京100049 美国马里兰大学地理系马里兰州College Park 美国20742

由美国宇航局(NASA)研制的新一代冰、云和陆地高程卫星(ICESat-2)于2018-09-15发射成功,其搭载的先进地形激光测高系统(ATLAS)采用微脉冲多波束光子计数激光雷达技术,可用于全球高程线采样数据的获取。目前公开发布了9种数据产品,其中... 详细信息

由美国宇航局(NASA)研制的新一代冰、云和陆地高程卫星(ICESat-2)于2018-09-15发射成功,其搭载的先进地形激光测高系统(ATLAS)采用微脉冲多波束光子计数激光雷达技术,可用于全球高程线采样数据的获取。目前公开发布了9种数据产品,其中包括植被冠层高度和地表高程数据产品(ATL08),为全球森林结构参数的估算提供了新的契机。本文以美国宾夕法尼亚州斯奈德县和印度尼西亚西加里曼丹吉打邦为研究区,在温带森林和热带雨林两种不同的生态系统立地条件下,对ICESat-2的ATL08数据产品用于森林高度估算的效果进行评价。首先建立了地形高程数据产品(ATL03)与ATL08数据产品的关联规则,以获取分别记录在这两种产品中的光子空间分布信息和分类信息;进而以机载小光斑激光雷达数据为参考,对ATL08数据产品的光子分类可靠性及其用于森林高度估算的准确性进行了分析评价。结果表明:(1)在温带森林情况下,ATL08数据产品提供的平均冠层高度和最大冠层高度与参考数据的相关系数(R~2)分别为0.54.0.61,相对误差分别为16.78%和10.71%,表明ATL08数据产品的光子分类结果能够用于刻画森林冠层结构和林下地形;(2)在热带雨林情况下,到达地面的光子数量相较于温带森林明显减少,地面光子类型识别的可靠性低,ATL08数据产品提供的平均冠层高度和最大冠层高度与参考数据的相关系数(R~)分别为0.21和0.19;(3)森林覆盖度的增大会导致ATL08计算的冠层高度误差增大,热带雨林平均冠层高度的误差随着坡度增大有增大趋势,在坡度为0°—10°、10°—20°和20°—30°共3组情况下,误差分别为5.7 m、6.6 m和9.3 m。因此,在高森林覆盖度情况下,现有的ATL08数据产品难以直接用于森林高度的提取。

关键词： ICESat-2 ATLAS ATL08 森林结构参数

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基于深度学习的花岗伟晶岩型锂铍矿物识别研究

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地学前缘 2023年第5期30卷 185-196页

作者：蒋果周可法王金林白泳孙国庆汪玮中国科学院空间应用工程与技术中心北京100049 中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室新疆乌鲁木齐830011 新疆矿产资源与数字地质重点实验室新疆乌鲁木齐830011 中国科学院新疆矿产资源研究中心新疆乌鲁木齐830011 中国科学院大学北京100049 中国科学院空天信息创新研究院北京100190

虽然遥感技术在大宗型金属矿产资源勘查方面取得了非常卓越的成效,但将其应用于稀有金属矿物提取的成果较少,尤其是对硬岩型锂铍矿物识别,主要受光谱分辨率、含矿岩体与围岩物性差异小、锂铍矿物光谱区分差异小等因素限制。为此,本研究... 详细信息

虽然遥感技术在大宗型金属矿产资源勘查方面取得了非常卓越的成效,但将其应用于稀有金属矿物提取的成果较少,尤其是对硬岩型锂铍矿物识别,主要受光谱分辨率、含矿岩体与围岩物性差异小、锂铍矿物光谱区分差异小等因素限制。为此,本研究通过野外采集含锂铍矿物伟晶岩和围岩样品并测量其光谱数据,使用光谱增强技术凸显光谱特征,对比分析特征吸收参数相似度模型和深度神经网络模型对矿物识别精度的影响。结果表明:(1)结合包络线去除和混合高斯模型提取的光谱吸收特征参数更简洁且具有更强的地质内涵;(2)光谱增强技术可提高模型识别精度,对比原始光谱,基于对数一阶导数光谱构建的模型的总体精度提高了0.05;(3)从总体精度看,深度卷积神经网络(总体精度=0.78)比浅层网络模型(反向传播模型总体精度=0.55和极限学习机模型总体精度=0.73)能够取得更好的效果。因此,结合高光谱技术和深度学习能够有效快速地识别花岗伟晶岩型矿物,为航空-航天成像高光谱影像直接提取锂铍矿物提供科学依据。

关键词：锂、铍光谱变换 MICA 深度学习高光谱

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