干旱区生态环境知识资源中心(Arid): SSM/I监测地表冻融状态的决策树算法

Arid

	SSM/I监测地表冻融状态的决策树算法
其他题名	A decision tree algorithm for surface freeze/thaw classification using SSM/I
	晋锐; 李新; 车涛
ISSN	1007-4619
出版年	2009
卷号	13 期号:1 页码:152-161
中文摘要	基于样本统计分析及冻结和融化地表的辐射/散射特性建立了判别地表冻融状态的决策树,首次联合使用散射指数、37GHz垂直极化亮温及19GHz极化差3个关键指标识别出地表或植被冠层的冻融状态,同时剔除了沙漠和降水的影响。利用国际协同加强观测期(CEOP)在青藏高原地区的土壤温度和湿度观测系统获取的4cm地温数据代表浅层土壤真实冻融状态验证分类结果,其准确性达87%。经分析,约40%和73%的误分分别发生在浅层土壤温度为-0.50.5℃和-2.02.0℃之间,即冻结点附近;且多发生在冷暖季节过渡时期,即45月和910月,分别占误分的33%和38%。基于该决策树获得的2002年10月2003年9月中国全境地表冻结日数图,以中国冻土区划及类型图为参考进行精度评价,其总体分类精度为91.66%,Kappa系数为80.5%,且冻融界线与季节冻土分布南界具有较好的一致性。
英文摘要	A decision tree algorithm was developed to classify the freeze/thaw status of the surface soil based on the cluster analysis of samples such as frozen soil,thawed soil,desert and snow,along with microwave emission and scattering characteristics of the frozen/thawed soil.The algorithm included five SSM/I channels(19V,19H,22V,37V,85V)and three crucial indices including scattering index,37GHz vertical polarization brightness temperature and 19GHz polarization difference,and took into consideration the scattering effect of desert and precipitation.The pureness of samples is essential to the analysis of the microwave brightness temperature characteristics,which is prior to deciding the thresholds of each node of the decision tree.We have selected four types of samples,including frozen soil,thawed soil,desert and snow.The frozen soil has some special microwave emission and scattering characteristics different from the thawed soil:① lower thermodynamic temperature and brightness temperature;② higher emissivity;③ stronger volume scattering,and the brightness temperature decreased with increasing frequency.The threshold of each node of the decision tree can be determined by using cluster analysis of three vital indices,and calculating the average and standard differences of each type and each index.The 4cm-depth soil temperature on the Qinghai-Tibetan Plateau observed by Soil Moisture and Temperature Measuring System of GEWEX-Coordinated Enhanced Observing Period,were used to validate the classification results.The total accuracy can reach about 87%.A majority of misclassification occurred near the freezing point of soil,about 40% and 73% of the misclassified cases appeared when the surface soil temperature is between-0.50.5℃ and-2.02.0℃,respectively.Furthermore,the misclassification mainly occurred during the transition period between warm and cold seasons,namely April-May and September-October.Based on this decision tree,a map of the number of frozen days during Oct.2002 to Sep.2003 in China was produced by composing 5 days classification results due to the swath coverage of SSM/I.The accuracy assessment for pixels with more than 15 frozen days(less than 15 meaning the short time frozen soil)was carried out with the regions of permafrost and seasonally frozen ground in map of geocryological regionalization and classification in China as reference data(Zhou et al.,2000),and the total classification accuracy was 91.66%,while the Kappa coefficient was 80.5%.The boundary between frozen and thawed soil was well consistent with the southern limit of seasonally frozen ground.A long time series surface frozen/thawed dataset can be produced using this decision tree,which may provide indicating information for regional climate change studies,regional and global scale carbon cycle models,hydrologic model and land surface model so on.
中文关键词	亮温 ; 地表冻融 ; 决策树
英文关键词	SSM/I SSM/I brightness temperature surface frozen/thawed decision tree
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:3490311
来源机构	中国科学院西北生态环境资源研究院
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/224402
作者单位	中国科学院寒区旱区环境与工程研究所, 兰州, 甘肃 730000, 中国
推荐引用方式 GB/T 7714	晋锐,李新,车涛. SSM/I监测地表冻融状态的决策树算法[J]. 中国科学院西北生态环境资源研究院,2009,13(1):152-161.
APA	晋锐,李新,&车涛.(2009).SSM/I监测地表冻融状态的决策树算法.,13(1),152-161.
MLA	晋锐,et al."SSM/I监测地表冻融状态的决策树算法".13.1(2009):152-161.