| 基于累积分布函数匹配的多源遥感土壤水分数据连续融合算法 |
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| 引用本文： | 姚晓磊,鱼京善,孙文超.基于累积分布函数匹配的多源遥感土壤水分数据连续融合算法[J].农业工程学报,2019,35(1):131-137. |
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| 作者姓名： | 姚晓磊 鱼京善 孙文超 |
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| 作者单位： | 1. 首都师范大学资源环境与旅游学院，北京 100048; 2. 北京师范大学水科学研究院/城市水循环与海绵城市技术北京市重点实验室，北京 100875;,2. 北京师范大学水科学研究院/城市水循环与海绵城市技术北京市重点实验室，北京 100875;,2. 北京师范大学水科学研究院/城市水循环与海绵城市技术北京市重点实验室，北京 100875; |
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| 基金项目： | 国家重点研发计划重点专项基金资助项目（2016YFC0401308）；国家自然科学基金资助项目（51779007）；中国博士后科学基金资助项目联合资助 |
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| 摘 要： | 数据融合是解决不同来源遥感数据无法直接对比分析这一瓶颈的有效方法。实时更新的SMOS土壤水分数据(soil moisture and ocean salinity)可开展实时干旱评价(2010年至今),但由于序列短无法开展频率及演变分析。CCI(climate change initiative)土壤水分数据是联合了多种主被动遥感数据合成的长序列数据产品(1979—2013年)。为提高不同来源遥感数据的融合精度,该研究基于累积分布匹配原理构建了多源遥感土壤水分连续融合算法,将SMOS和CCI融合成长序列、近实时的遥感土壤水分数据。经验证分析,累积概率曲线相关性中表征干旱的低值区纳什效率系数由0.52提高到0.99,且融合后土壤水分数据可以较准确地反映当地的干旱事件。该研究提出的多源遥感土壤水分连续融合算法显著提高了现有融合算法的融合精度。
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| 关 键 词： | 遥感 土壤水分 数据融合 多源连续算法 |
| 收稿时间： | 2018/4/17 0:00:00 |
| 修稿时间： | 2018/10/12 0:00:00 |
Continuous fusion algorithm analysis for multi-source remote sensing soil moisture data based on cumulative distribution fusion |
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| Yao Xiaolei,Yu Jingshan and Sun Wenchao.Continuous fusion algorithm analysis for multi-source remote sensing soil moisture data based on cumulative distribution fusion[J].Transactions of the Chinese Society of Agricultural Engineering,2019,35(1):131-137. |
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| Authors: | Yao Xiaolei Yu Jingshan and Sun Wenchao |
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| Institution: | 1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; 2. Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China,2. Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China and 2. Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China |
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| Abstract: | Abstract: As an important grain-producing region, Songnen Plain located in Northeast China has been significantly affected by drought in recent years. Remote sensing soil moisture is one of the important indices for monitoring agricultural drought in large-scale farmland area. The time series length and update speed of remote sensing data are 2 important factors affecting its application. In 2009, satellite called SMOS (soil moisture and ocean salinity) was launched. As the first satellite dedicated to monitoring soil moisture of earth, daily updated SMOS soil moisture data have been proven to be suitable for the application in real-time drought monitoring and evaluation in many researches. In the field of agricultural drought management, drought characteristics and frequency analysis are basic contents of these researches. However, it is impossible to analyze the drought frequency and characteristic evolution by SMOS data, due to their short time series. CCI (climate change initiative) soil moisture data, which have a long time series (1979-2013), was combined with a variety of C-band scattered data and multi-frequency radiometer data. As a kind of historical data, CCI soil moisture product can make up for SMOS data to analyze the agricultural drought characteristics. Because of the difference of the sensors and the inversion methods, remote sensing data from different sources cannot be directly compared and analyzed. Therefore, data fusion becomes a hotspot and key issue in the application research of remote sensing data nowadays. Based on cumulative distribution matching principle, the key of data fusion is to establish the correlation between cumulative probability curves of different data. The work amount of traditional piecewise linear fusion method is proportional to the fusion accuracy. This linear method is difficult to process a number of data in batches with high precision. Unary interpolation can establish this correlation between any quantile on different cumulative probability distribution curves. Therefore, a continuous fusion algorithm of multi-source remote sensing soil moisture was built in this study. Using this continuous fusion method, SMOS and CCI data were fused to real-time remote sensing soil moisture data product with long time series characteristics with the Songnen Plain as the case. This study compared the fusion accuracy between this continuous fusion and piecewise linear fusion method. And the time series of original SMOS data and fused SMOS data was also analyzed. The analysis results indicate that this unary interpolation continuous fusion method can improve the fusion accuracy of multi-source remote sensing soil moisture significantly. Data segment of the cumulative probability distribution curve with low water content can characterize agricultural drought. By the piecewise linear fusion method, data segment of the cumulative probability distribution curve with low water content yet has some errors, which will lead to the inaccuracy of drought evaluation. By this new continuous fusion method, fused SMOS data and CCI data are completely coincident at each quantile in the low-value region of the curve. Through the accurate evaluation of drought events, the fused SMOS data can reflect local drought conditions. Through time series analysis, the range of fused SMOS data is closer to the CCI data, and the relative change pattern of original SMOS data still remains. This remote sensing fusion data combining the advantages of CCI and SMOS data can provide reliable data support for the next study of agricultural drought evaluation. |
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| Keywords: | remote sensing soil moisture data fusion multi-source continuous algorithm |
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