基于地基微波辐射观测的土壤水分反演算法评估

doi:10.11873/j.issn.1004-0323.2020.1.0074

遥感技术与应用

2020, Vol. 35

Issue (1): 74-84 DOI: 10.11873/j.issn.1004-0323.2020.1.0074

土壤水分专栏

基于地基微波辐射观测的土壤水分反演算法评估

胡路^1,²(

),赵天杰¹(

),施建成¹,李尚楠³,樊东²,王平凯⁴,耿德源^1,²,肖青¹,崔倩⁵,陈德清⁵

^1.中国科学院遥感与数字地球研究所遥感科学国家重点实验室, 北京 100101
^2.中国科学院大学，北京 100049
^3.93920部队，陕西西安 710061
^4.上海航天电子通讯设备研究所，上海 201109
^5.水利部信息中心，北京 100053

Evaluation of Soil Moisture Retrieval Algorithms based on Ground-based Microwave Radiation Observation

Lu Hu^1,²(

),Tianjie Zhao¹(

),Jiancheng Shi¹,Shannan Li³,Dong Fang²,Pingkai Wang⁴,Deyuan Geng^1,²,Qing Xiao¹,Qing Cui⁵,Deqing Chen⁵

^1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
^2.University of Chinese Academy of Sciences, Beijing 100049, China
^3.Unit 93920, Xi’an 710061, China
^4.Institute of Aerospace Electronic Communication Equipment, Shanghai 201109, China
^5.Ministry of Water Resources Information Center, Beijing 100053, China

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摘要：

当前常用的被动微波土壤水分反演算法有水平极化单通道算法、垂直极化单通道算法、双通道算法、微波极化差比值算法和扩展双通道算法，5种反演算法具有不同的差异，对这些反演算法进行系统的评估和分析将有助于反演算法的改进和星载高精度土壤水分产品的发布。为了避免直接采用卫星产品验证时的尺度匹配、空间异质性等问题，基于地基L波段微波辐射观测以及配套的土壤和植被参数测量数据，对这5种反演算法进行了实现、对比和分析，得出以下结论：①单通道算法具有最佳的反演性能，水平极化单通道算法反演结果具有最高的相关性(相关性系数R=0.83)，垂直极化单通道算法反演结果具有最小的反演误差(均方根误差RMSE=0.028 m³/m³,偏差BIAS= -0.011 m³/m³),但单通道算法需要精确的植被含水量输入；②其余3种算法能脱离植被辅助数据的使用，性能略差但也能满足星载微波传感器的探测指标要求(小于等于0.04 m³/m³)；其中，扩展双通道算法和微波极化差比值算法的土壤水分反演结果比双通道算法略差，但本例中扩展双通道算法在植被含水量反演方面更具优势。

关键词： 土壤水分; 微波辐射计; L波段; 反演算法

Abstract:

The commonly used passive microwave soil moisture inversion algorithms include Single Channel Algorithm at H polarization (SCA-H), Single Channel Algorithm at V polarization (SCA-V), Dual-Channel Algorithm (DCA), Microwave Polarization Ratio Algorithm (MPRA) and Extended Dual Channel Algorithm (E-DCA). The five retrieval algorithms have different performance, systematic evaluation and analysis of these inversion algorithms will contribute to the improvement of the retrieval algorithm and the release of satellite soil moisture products. Verification of satellite product could bring some problems, such as scale matching and spatial heterogeneity. In order to avoid these issues, the above five soil moisture inversion algorithms are implemented, compared and analyzed based on ground-based microwave radiometer observation and supporting soil and vegetation parameter measurement data. The results show: (1) SCA has the best inversion performance. SCA-H has the highest correlation (R=0.83), and SCA-V has the smallest inversion error (RMSE=0.028 m³/m³, BIAS=-0.011 m³/m³), but SCA needs the accurate vegetation water content as an input. (2) The other three algorithms can get rid of the use of vegetation-aided data, with slightly poor performance but also meet the satellite detection requirements (less than or equal to 0.04 m³/m³). Among them, E-DCA and MPRA are slightly worse than the DCA. However, E-DCA is more advantageous in the vegetation water content inversion in our study.

Key words: Soil moisture Microwave radiometer L band Retrieval algorithm

收稿日期: 2019-02-16 出版日期: 2020-04-01

ZTFLH:

TP79

基金资助: 国家重点研发计划(2016YFE0117300);国家重大科学研究计划(2015CB953701);“十三五”民用航天预先研究项目“陆地水资源卫星系统技术”(Y7D0070038);中国科学院青年创新促进会项目(2016061)

通讯作者: 赵天杰 E-mail: hulu@radi.ac.cn;zhaotj@radi.ac.cn

作者简介: 胡路（1994-），男,湖北潜江人，硕士研究生,主要从事微波遥感反演土壤水分研究。E?mail:hulu@radi.ac.cn。

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	胡路
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	崔倩
	陈德清

引用本文:

胡路,赵天杰,施建成,李尚楠,樊东,王平凯,耿德源,肖青,崔倩,陈德清. 基于地基微波辐射观测的土壤水分反演算法评估[J]. 遥感技术与应用, 2020, 35(1): 74-84.

Lu Hu,Tianjie Zhao,Jiancheng Shi,Shannan Li,Dong Fang,Pingkai Wang,Deyuan Geng,Qing Xiao,Qing Cui,Deqing Chen. Evaluation of Soil Moisture Retrieval Algorithms based on Ground-based Microwave Radiation Observation. Remote Sensing Technology and Application, 2020, 35(1): 74-84.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0074 或 http://www.rsta.ac.cn/CN/Y2020/V35/I1/74

图1 L波段观测亮温组成部分(忽略了大气影响)

图2 单通道算法流程图

图3 车载辐射计观测视场图

图4 玉米地观测亮温、土壤温湿度和降雨量

图5 不同ω和b参数下的单通道算法模拟结果

观测模式

RMSE最小

R最大

Dayscan

$ω = 0.02$

$b = 0.06$

$ω = 0.02$

$b = 0.05$

Nightscan

$ω = 0.01$

$b = 0.05$

$ω = 0.08$

$b = 0.07$

表1 不同观测模式下的最佳ω和b参数值

表2 不同反演算法精度验证

图6 5种方法反演结果对比

图7 植被含水量反演结果与实测对比

表3 植被含水量反演方法精度验证

图8 不同深度的土壤湿度与观测亮温散点图

图9 L波段理论穿透深度变化

图10 40°入射角MPDI变化图

图11 DCA和MPRA反演精度随初值的变化

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