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引用本文:

DOI:

10.11834/jrs.20209293

收稿日期:

2019-08-09

修改日期:

2020-02-05

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青藏高原地区近地表冻融状态判别算法研究
张子谦1, 赵天杰2, 施建成2, 李玉霖1, 冉有华1, 陈莹莹3, 赵少杰4, 王健5, 宁志英1, 杨红玲1, 韩丹1
1.中国科学院西北生态环境资源研究院;2.中国科学院 空天信息研究院 遥感科学国家重点实验室;3.中国科学院 青藏高原研究所 青藏高原环境变化与地表过程重点实验室;4.北京师范大学 地理科学学部 地表过程与资源生态国家重点实验室;5.北京师范大学 地理科学学部 遥感科学国家重点实验室
摘要:

青藏高原地区以其独特的气候水文特征被称为“亚洲水塔”, 这一地区广泛分布的冻土及其冻融过程对地表非绝热加热与水文过程具有重要影响。然而, 恶劣和复杂的地理环境为这一区域的地表冻融过程本地观测和遥感监测均带来极大挑战。本文利用AMSR-2传感器遥感数据开展青藏高原地区的近地表冻融判别算法研究, 包括判别式算法和季节性阈值算法, 并使用4个青藏高原典型地区的土壤温湿度密集观测网数据对算法进行区域适应性优化。研究特别针对季节性阈值算法进行了两点改进: 首先考虑到地表发射率的变化对于冻融相态的转变指示更为直接,故采用6.9 GHz水平(H)极化的准发射率替换季节性阈值算法中的原有冻结因子; 其次使用一种新的数据归一化方法:标准差归一化方法, 用以替代原有的离差归一化方法, 并通过阈值设定对判别精度的影响分析改进后的优势。结果证明, 冻融判别式算法在升轨时期的整体精度最具优势, 其优势在于能够减少夏季地表发射率复杂变化导致的误判, 基于标准差归一化方法的季节性阈值算法在降轨时期的整体精度具有优势。通过对不同典型区域的冻融土辐射特征和判别精度的分析, 发现地表发射率的变幅(初始液态含水量)大小是影响所有冻融判别算法精度的最关键因素。

Near-surface freeze/thaw state mapping over the Tibetan Plateau
Abstract:

The Tibetan Plateau area is known as the “Water Tower of Asia” due to its significant climatic and hydrological characteristics. However, it’s difficult to detect land surface freeze/thaw transition in TP area because of its harsh and complex geographical environment. This study intends to establish an algorithm to identify near-surface freeze/thaw state by using AMSR-2 satellite data, including discrimination function and seasonal threshold for the identification. With the higher sensitivity to near-surface freeze/thaw cycle, 6.925 GHz horizontal polarization Quasi-emissivity is suggested to replace FFrel (Relative Frost Factor). In order to minimalize the impact of small-scale threshold selection, Min-Max normalization can be replaced by a new normalization method named standard deviation normalization method. The parameterization of discrimination function algorithm to Tibetan Plateau area are proposed to improve algorithm accuracy by using 4 soil moisture and temperature dense observation networks data. Results show that the classification accuracy of discriminant function algorithm has the most advantage at ascend period. It also reduces misclassification points caused by complex changes of surface emissivity in summer. The seasonal threshold algorithm based on the standard deviation normalization method show the best performance at descend period. Furthermore, the amplitude of surface emissivity (Initial liquid water content) has a significant impact on the algorithm accuracy.

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