Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models
Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models
复制标题
基于正演和反演模型相结合的遥感雪反照率重建以提高质量
DOI：
10.1109/tgrs.2018.2846681
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发表时间：
2018-07
影响因子：
8.2
通讯作者：
Xiaohua Hao
中科院分区：
文献类型：
--
作者：
Donghang Shao;Wenbo Xu;Hongyi Li;Jian Wang;Xiaohua Hao
雪在全球气候系统中起着重要的作用。由于遥感技术的限制，目前的遥感雪景产品存在明显的数据缺失和误差不确定性。由于气象因素的不确定性和各种正演模式模拟方法的差异，雪量正演模拟也存在着相当大的不确定性。本文提出了一种利用前向辐射传输模型和遥感反演模型，结合多波段遥感数据和气象数据重建积雪的长时间序列。本文的关键是利用物理机制明确的积雪正演模型来估计缺乏数据的地区的积雪信息。估计的雪信息可以作为可靠的数据，积雪重建。结果表明，雪量反演模型与正演模拟模型耦合得到的雪量长时间序列具有较高的精度。观测和重建的雪场的平均绝对误差、均方根误差、皮尔逊相关系数（R）和Nash-Sutcliffe效率系数分别为0.11、0.14、0.79和0.69。重建的雪水数据被低估，只有11%，相对于在原地雪表面的雪水测量。在高寒山区，该方法的模拟精度比MOD 10A 1 SAD高6%。本文提供了一种有效的重建解决方案，提高了雪覆盖估计的准确性，并填补了数据中的空白。
Snow albedo plays an important role in the global climate system. There are notable missing data and error uncertainties in the current remote sensing snow albedo products that are attributed to the limits of remote-sensing technology. Due to the uncertainties of meteorological factors and the differences in various forward model simulation methods, snow albedo forward simulations also have considerable uncertainties. This paper suggests a long-time-series reconstruction of snow albedo utilizing a forward radiation-transferring model and a remote-sensing retrieval model together with multisource remotely sensed data and meteorological data. The key to this paper is to estimate snow information for areas lacking data utilizing a forward model for snow albedo with clear physical mechanisms. The estimated snow information can be used as reliable data for snow albedo reconstructions. The results indicate that the long time series of snow albedo data obtained by coupling the snow albedo retrieval model and forward simulation model is highly accurate. The mean absolute error, root mean square error, Pearson’s correlation coefficient (R), and Nash–Sutcliffe efficiency coefficient of the observed and reconstructed snow albedos are 0.11, 0.14, 0.79, and 0.69, respectively. The reconstructed snow albedo data are underestimated by only 11% relative to the in situ snow surface albedo measurements. In the alpine mountain regions, the proposed method has a simulation accuracy that is 6% greater than that of the MOD10A1 SAD. This paper provides an effective reconstruction solution that improves the accuracy of estimations of snow albedo and fills gaps in the data.
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影响因子：
4.9
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
10.1109/jstars.2017.2716388
发表时间：
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影响因子：
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作者：
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