收稿日期: 2006-10-11
修回日期: 2006-10-11
网络出版日期: 2006-12-15
基金资助
National Natural Science Foundation of China(90302008); Program for Changjiang Scholars and Innovative Research Team in University(PCSLRT).
Development of Snow Depth and Snow Water Equivalent Algorithm in Western China Using Passive Microwave Remote Sensing Data
Received date: 2006-10-11
Revised date: 2006-10-11
Online published: 2006-12-15
雪深、雪水当量是积雪研究中重要参数，其在流域水量平衡和融雪径流预报以及雪灾监测与评价中起着重要作用。Chang等(1987)以辐射传输理论和米氏散射为理论基础，假定积雪密度和颗粒大小为常数，利用实测雪深数据和SMMR的亮温数据，通过统计回归方法，建立了雪深与18 GHz和37 GHz水平极化的亮温梯度之间的关系，发展了SMMR半经验的反演雪深的算法。后在此基础上又发展了针对SSM/I的半经验反演雪深算法。2002年发射的装载于Aqua卫星上的AMSR-E是新一代的被动微波辐射计，性能较以往星载被动微波辐射计有较大提高，采用了改进后的SSM/I的半经验算法作为其估算全球雪水当量的反演算法。
将AMSR-E的雪水当量产品与气象台站观测的雪水当量进行比较，发现在新疆地区和青藏高原地区雪水当量的RMSE分别达到31.8 mm和21 mm。本研究旨在建立基于AMSR-E亮温数据，适用于中国西部地区的雪深和雪水当量反演算法。首先收集整理了2003年新疆地区的雪深、雪水当量数据和AMSR-E亮温数据，去除错误样本，利用统计回归的方法，建立了新疆的反演雪深、雪水当量的半经验算法，算法中加入积雪覆盖度参数，较以往的算法有所改进，与气象台站观测数据比较，结果也表明新疆地区建立的经验算法较AMSR-E的雪水当量算法有较大改进，RMSE为15.7 mm。但青藏高原地区因海拔高，地形复杂，大部分地区积雪较浅,空间分布不均和冻土存在等诸多因素运用同样的方法建立反演算法，结果不甚理想，以后的研究将重点消除这些干扰因素。
孙之文 , 蒋玲梅 , 张立新 , 杨虎 , 施建成 . 被动微波遥感反演中国西部地区雪深、雪水当量算法初步研究[J]. 地球科学进展, 2006 , 21(12) : 1363 -1369 . DOI: 10.11867/j.issn.1001-8166.2006.12.1363
In order to evaluate the accuracy of snow water equivalent (SWE) inversion algorithm for passive microwave sensor Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) in Western china, we compared SWE obtained from AMSR-E daily SWE product with the ground measurements from 15 meteorological stations in Tibetan plateau in 2003 and 35 meteorological stations in Xinjiang in January 2004. The results show AMSR-E overestimate SWE both in these two regions, and RMSE is 21mm and 31.8 mm in Tibetan plateau and Xinjiang, respectively.
Through incorporating snow fraction factor, a new empirical algorithm estimate snow depth and SWE have been developed in Xinjiang. This new algorithm appeared higher accuracy than AMSR-E does in Xinjiang. Due to complex topography, shallow patchy snow and frozen grounds covered at the Tibetan Plateau, this technique didn't show good results. In future we will focus on how to evaluate and eliminate the effects of these factors quantitatively on SWE retrieval.
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