| 阿尔山地区积雪深度微波遥感反演算法的改进与验证 |
| |
| 引用本文： | 毕永恒,何文英,龙菲,陈洪滨,李长富.阿尔山地区积雪深度微波遥感反演算法的改进与验证[J].气候与环境研究,2020,25(4):410-418. |
| |
| 作者姓名： | 毕永恒 何文英 龙菲 陈洪滨 李长富 |
| |
| 作者单位： | 1.中国科学院大气物理研究所中层大气与全球环境探测实验室，北京 1000292.中国科学院大学，北京 1000493.成都锦江电子系统工程有限公司，成都 6102254.阿尔山市气象局，内蒙古自治区阿尔山 137805 |
| |
| 基金项目： | 国家自然科学基金项目41575033、41575065，国家重点研发计划项目2017YFC1501700 |
| |
| 摘 要： | 利用阿尔山地区多年实测雪深数据评估3种微波遥感雪深数据，即星载微波成像仪AMSR-E（Advanced Microwave Scanning Radiometer for EOS）和AMSR-2（Advanced Microwave Scanning Radiometer 2）的积雪产品、国内学者建立的中国雪深数据集，在该地区的适用状况，并建立新的雪深反演算法。1981～2014年的中国雪深数据集和阿尔山站点实测雪深统计的积雪日数和最大积雪深度具有较好的一致性，尤其是在2000年以后。AMSR-E和AMSR2雪深数据年变化与实测雪深变化趋势一致，与实测雪深数据相关系数超过0.60，不过具体雪深数据变化幅度远高于实测数据，致使两者之间的均方根误差高达13.0 cm。中国雪深数据集在阿尔山地区与实测雪深相关系数超过0.65，两者之间均方根误差为6.3 cm。结合星载微波观测亮温与实测雪深建立适合阿尔山地区的雪深反演算法，验证分析显示反演结果与实测雪深相关系数为0.77，两者的均方根误差减小为4.7 cm，优于本文评估的3种微波遥感雪深数据。
|
| 关 键 词： | 雪深 微波遥感 反演 评估 |
| 收稿时间： | 2019/3/18 0:00:00 |
Improvement of the Snow-Depth Retrieval Algorithm Using Passive Microwave Remote Sensing over the Arxan Region Inner Mongolia |
| |
| BI Yongheng,HE Wenying,LONG Fei,CHEN Hongbin,LI Changfu.Improvement of the Snow-Depth Retrieval Algorithm Using Passive Microwave Remote Sensing over the Arxan Region Inner Mongolia[J].Climatic and Environmental Research,2020,25(4):410-418. |
| |
| Authors: | BI Yongheng HE Wenying LONG Fei CHEN Hongbin LI Changfu |
| |
| Institution: | 1.Key Laboratory of Middle Atomosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000292.University of Chinese Academy of Sciences, Beijing 1000493.Chengdu Jinjiang Electronic System Engineering Co. Ltd., Chengdu 6102254.Arxan Meteorological Bureau, Arxan, Inner Mongolia Autonomous Region 137805 |
| |
| Abstract: | Long-term snow-depth observations at Arxan station are used to evaluate the application of snow-depth products retrieved from the AMSR-E and AMSR-2 microwave data and Chinese snow-depth dataset developed by Chinese researchers and to establish a new snow-depth retrieval algorithm. The statistical snow-cover days and maximum snow-depth records derived from the 35-year Chinese snow-depth dataset and station observations are consistent, particularly after 2000. The snow-depth variation trend estimated from the snow-depth products retrieved from the AMSR-E and AMSR-2 microwave data is consistent with that retrieved from the station observations, with the correlation coefficient greater than 0.6. However, the variation range of the snow-depth products is wider than that of the station observations. Thus, the root mean square error (RMSE) of both snow-depth datasets is high (i.e., approximately 13 cm). The Chinese snow-depth dataset at Arxan station shows a higher correlation coefficient of 0.65 and a lower RMSE of 6.3 cm than the station observations. To better estimate snow depth in the Arxan region, a new snow-depth retrieval method is developed using both space-borne passive microwave brightness temperature and observed snow-depth data at Arxan station. The validation shows that the snow-depth data retrieved using the new method has a higher correlation with the observations (i.e., approximately 0.77) and a lower RMSE (i.e., approximately 4.68 cm) than the snow-depth products retrieved from the AMSR-E and AMSR-2 microwave data and Chinese snow-depth dataset used in this study. |
| |
| Keywords: | Snow-depth Microwave remote sensing Retrieval Evaluation |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《气候与环境研究》浏览原始摘要信息 |
| 点击此处可从《气候与环境研究》下载免费的PDF全文 |
