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遥感技术与应用  2019, Vol. 34 Issue (6): 1227-1234    DOI: 10.11873/j.issn.1004-0323.2019.6.1227
模型与反演     
基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合
王树果(),刘伟,梁亮
江苏师范大学 地理测绘与城乡规划学院,江苏 徐州 221116
Uncertainty Analysis and Data Fusion of Microwave Remote Sensing Soil Moisture Products based on Triple-Collocation Method
Shuguo Wang(),Wei Liu,Liang Liang
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
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摘要:

微波遥感可以获取大范围的地表土壤水分信息,以及由此得到全球尺度的土壤水分产品。但由于传感器观测配置和反演方法等诸多因素的影响,使得不同的土壤水分产品在精度和可靠性方面存在差异。基于Triple-Collocation(TC)方法,在青藏高原那曲地区的0.25°×0.25°和1.0°×1.0°两个空间尺度上对AMSR2、SMAP和SMOS 3种土壤水分遥感产品进行不确定性分析,开展基于随机误差的数据融合算法研究。研究结果表明:不同遥感产品间的随机误差在空间分布上存在显著的不一致性,使得应用传统的算术平均方法进行数据融合不具有普适性。基于此不确定性,对3种产品配赋相应的权重进行融合,相比于3种土壤水分原始数据集,融合产品不仅具有更丰富的数据量,也会对数据精度有所改善。当遥感产品间的随机误差接近时,等权重和优化权重的融合结果非常接近;当遥感产品间的随机误差差异较大时,基于不确定性的数据融合方法相比等权重方法可以明显的提高融合数据的精度。

关键词: 微波遥感土壤水分产品Triple-Collocation不确定性数据融合    
Abstract:

Microwave remote sensing can provide large scale soil moisture information, and even further derive soil moisture products at global scale. Due to impacts of observation configuration and retrieval method etc., different soil moisture products feature different accuracies and reliabilities. Based on Triple-Collocation method, this study analyzes the uncertainties among AMSR2, SMAP and SMOS soil moisture products at two spatial scales in Naqu study area, i.e., 0.25°× 0.25° and 1.0°×1.0°, and further performs data fusion based on analyzed random errors to obtain more reliable soil moisture product. The uncertainty analysis indicates that the three products have distinct random error distribution in spatial. In this case, the traditional arithmetic mean method may not be appropriate. Hence, data fusion is performed by proposed optimizing weighting method based on the analyzed uncertainties. In comparison with the three original soil moisture products, the fusion result shows a more effective data size and improved accuracy. When different original products present similar errors, the fusion products of equal weighting and optimized weighting methods show the similar performance. Oppositely, the uncertainties analysis based fusion method is superior to equal weighting method in terms of effective data size and accuracy.

Key words: Microwave remote sensing    Soil moisture product    Triple-Collocation    Uncertainty    Data fusion
收稿日期: 2019-01-15 出版日期: 2020-03-23
ZTFLH:  TP79  
基金资助: 江苏省自然科学基金项目(BK20171165);国家自然科学基金项目(41971305)
作者简介: 王树果(1980-),男,甘肃兰州人,博士,讲师,主要从事定量遥感研究。E?mail:swang@jsnu.edu.cn
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引用本文:

王树果,刘伟,梁亮. 基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合[J]. 遥感技术与应用, 2019, 34(6): 1227-1234.

Shuguo Wang,Wei Liu,Liang Liang. Uncertainty Analysis and Data Fusion of Microwave Remote Sensing Soil Moisture Products based on Triple-Collocation Method. Remote Sensing Technology and Application, 2019, 34(6): 1227-1234.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1227        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1227

图1  研究区示意图[25]
图2  3种土壤水分遥感产品的不确定性比较
图3  3种土壤水分遥感产品的权重系数分配
图4  0.25°×0.25°尺度的融合结果比较
图5  1.0°×1.0°尺度的融合结果比较
优化权重融合等权重融合AMSR2_ LPRMSMAPSMOS
RMSE(cm3/cm3
0.25°0.0570.0590.0960.0580.071
1.00°0.0490.0620.1230.0480.078
相关系数
0.25°0.8840.8810.7730.8900.826
1.00°0.8680.8540.7740.8640.694
表1  数据融合结果验证
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