Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements
Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements
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DOI：
10.1016/j.rse.2004.10.007
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发表时间：
2005-01
影响因子：
13.5
通讯作者：
Xiaobing Zhou;H. Xie;J. Hendrickx
中科院分区：
文献类型：
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作者：
Xiaobing Zhou;H. Xie;J. Hendrickx
以流量和积雪遥测（SNOTEL）数据为约束条件，以上里约热内卢格兰德河流域为试验点，对中分辨率成像光谱仪（MODIS）每日和8天积雪产品进行了评价。利用为本研究开发的基于自动地理信息系统（GIS）的算法，从MODIS卫星h09v05中检索了2000年2月至2004年6月期间的上里约热内卢大盆地积雪面积（SAE）时间序列。Otowi （NM）站的流量与两种MODIS积雪产品反演的SAE之间的统计分析表明，两种产品的流量与SAE之间存在显著的统计学相关性。这种关系可能会被春末的暴雨扰乱，尤其是在5月。相关分析表明，MODIS 8天产品与河流流量的相关性较好（r= - 0.404），冬季假融雪事件的百分比低于MODIS日产品（r= - 0.300）。以SNOTEL数据集为地面真值对两者进行对比，结果表明：(1)MODIS 8天产品对雪和陆地的分类精度都较高；(2)两种产品将雪误分类为陆地的遗漏误差相似，均较低；(3) MODIS 8天产品将土地误分类为雪的委托误差略高于MODIS日产品；(4) MODIS日产品将雪和陆地误分类为云的遗漏误差较高。云是降低MODIS日常产品整体精度的主要原因。从这个比较研究中可以明显看出8天产品在抑制云方面的改善。牺牲的是时间分辨率，从1天减少到8天。研究结果的意义在于，考虑到8天产品的时间分辨率比日产品低，从长期角度来看，8天产品更有利于评价径流对积雪覆盖范围变化的响应。对于晴天，MODIS日算法的效果相当好，甚至优于MODIS 8天算法。
Using streamflow and Snowpack Telemetry (SNOTEL) measurements as constraints, the evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily and 8-day snow-cover products is carried out using the Upper Rio Grande River Basin as a test site. A time series of the snow areal extent (SAE) of the Upper Rio Grande Basin is retrieved from the MODIS tile h09v05 covering the time period from February 2000 to June 2004 using an automatic Geographic Information System (GIS)-based algorithm developed for this study. Statistical analysis between the streamflow at Otowi (NM) station and the SAE retrieved from the two MODIS snow-cover products shows that there is a statistically significant correlation between the streamflow and SAE for both products. This relationship can be disturbed by heavy rainstorms in the later springtime, especially in May. Correlation analyses show that the MODIS 8-day product has a better correlation (r=−0.404) with streamflow and has less percentage of spurious snowmelt events in wintertime than the MODIS daily product (r=−0.300). Intercomparison of these two products, with the SNOTEL data sets as the ground truth, shows that (1) the MODIS 8-day product has higher classification accuracy for both snow and land; (2) the omission error of misclassifying snow as land is similar for both products, both are low; (3) the MODIS 8-day product has a slightly higher commission error of misclassifying land as snow than the MODIS daily product; and (4) the MODIS daily product has higher omission errors of misclassifying both snow and land as clouds. Clouds are the major cause for reduction of the overall accuracy of the MODIS daily product. Improvement in suppressing clouds in the 8-day product is obvious from this comparison study. The sacrifice is the temporal resolution that is reduced from 1 to 8 days. The significance of the results is that the 8-day product will be more useful in evaluating the streamflow response to the snow-cover extent changes, especially from the long-term point of view considering its lower temporal resolution than the daily product. For clear days, the MODIS daily algorithm works quite well or even better than the MODIS 8-day algorithm.