地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (1): 108-118.doi: 10.12082/dqxxkx.2018.170319

所属专题: 气候变化与地表过程

• 遥感科学与应用技术 • 上一篇    下一篇

地表组合粗糙度不确定性引起的SAR反演土壤水分的不确定性分析

陈鲁皖(), 韩玲*(), 王文娟, 秦小宝   

  1. 长安大学地质工程与测绘学院,西安 710064
  • 收稿日期:2017-07-12 修回日期:2017-11-06 出版日期:2018-01-20 发布日期:2018-01-20
  • 通讯作者: 韩玲 E-mail:368848532@qq.com;hanling@chd.edu.cn
  • 作者简介:

    作者简介:陈鲁皖(1980- ),男,博士生,讲师,主要从事微波土壤水分反演研究。E-mail: 368848532@qq.com

  • 基金资助:
    国家重大高分专项军事测绘专业处理与服务系统地理空间信息融合分系统(GFZX04040202-07);中央高校基本科研业务费专业资金项目(310826175031)

Uncertainty Analysis of SAR-retrieved Soil Moisture Induced by Uncertainty of Soil Surface Combined Roughness

CHEN Luwan(), HAN Ling*(), WANG Wenjuan, QIN Xiaobao   

  1. College of Geology Engineering and Geomatics, Chang’an University, Xi'an 710064, China
  • Received:2017-07-12 Revised:2017-11-06 Online:2018-01-20 Published:2018-01-20
  • Contact: HAN Ling E-mail:368848532@qq.com;hanling@chd.edu.cn
  • Supported by:
    China High-Resolution Earth Observation System, No.GFZX04040202-07;The Fundamental Research Funds for the Central Universities, No.310826175031.

摘要:

地表粗糙度的不确定性是引起SAR土壤水分反演结果不确定性的主要因素,现有研究大多着重于研究单个粗糙度参数(主要是相关长度)的不确定性,直接研究地表组合粗糙度不确定性的较少。本文使用偏度、峰度和四分位距3个指标来量化不确定性,通过在组合粗糙度中加入不同量级高斯噪声进行随机扰动的方法,研究组合粗糙度不确定性在反演过程中的传递,并对反演土壤水分的不确定性进行定量分析。进一步研究反演土壤水分的均方根误差对组合粗糙度不同比例误差范围的响应特征,得到满足反演精度要求的组合粗糙度误差控制范围。样区的实验分析结果表明:组合粗糙度高斯噪声标准差在0-0.045之间时,峰度取值从-0.1984到1.2501,偏度取值从0.0191到0.6791,四分位距取值从0.0018到0.0167,3个量化指标都随组合粗糙度高斯噪声量级的增大而增大,土壤水分反演值有集中在众数附近的趋势,土壤水分低估倾向比高估倾向更明显;本文提出的组合粗糙度误差控制范围可满足反演精度要求,误差控制范围与入射角负相关。

关键词: 不确定性量化, 组合粗糙度, 土壤水分, 峰度, 偏度, 四分位距, 误差控制范围

Abstract:

Soil moisture is a key factor in the energy and water balance of the earth's surface, and it also plays an important role in the ecological environment. Soil moisture inversions based on Synthetic Aperture Radar (SAR) have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms cannot quantify the uncertainty of soil moisture inversions. Uncertainty of surface roughness is the main factor that causes uncertainty of SAR-retrieved soil moisture. Most of the existing studies focused on the uncertainty of single roughness parameter (correlation length), and seldom directly studied the uncertainty of surface combined roughness. The uncertainty was usually estimated by probability distribution of model parameter values in existing studies. Then, the probability distribution was propagated through the inversion process. Finally, the probability distribution of soil moisture inversion was obtained. The uncertainty was quantified by using skewness, kurtosis, and interquartile range in this paper. First of all, the range and distribution of the measured soil moisture data and roughness data in sampling area were counted and analyzed. Input values and scope of the AIEM model parameters were obtained. Then, effective correlation length was calculated by using the LUT (look up tables) method based on the measured soil moisture data and backscattering coefficients, and the effective combined roughness was obtained. The nonlinear relationship between the effective combined roughness and backscattering coefficients was constructed. By adding different levels of Gaussian noise to surface combined roughness, the uncertainty propagating of surface combined roughness in the process of retrieved soil moisture was studied, and the uncertainty of soil moisture retrieval was quantitatively analyzed. For each Gauss noise level, 1000 effective combined roughness sampling values with noise were obtained. By using the nonlinear relationship between the effective combined roughness and the backscattering coefficient, the backscattering coefficients corresponding to the sampling value of each effective combined roughness were derived. The soil moisture was obtained by using the empirical equation of soil moisture inversion. The skewness, kurtosis and interquartile range of the effective combined roughness and the inversion results were calculated. By using the AIEM (Advanced Integrated Equation Model) model and the limited range of input parameters, a large number of simulated data were obtained. The effective combined roughness of the simulation was introduced into different proportion error according to the initial value, and the soil moisture was obtained by the empirical equation of soil moisture inversion. Furthermore, according to the response characteristics between RMSE (Root Mean Square Error) of retrieved soil moisture and the error range of combined roughness, the error control range that meets the inversion accuracy requirement was obtained. The experimental results of sample area show that kurtosis range is -0.1984 to 1.2501, the deviation range is 0.0191 to 0.6791, and interquartile range is 0.0018 to 0.0167 when gaussian noise standard deviation range of composite roughness is 0 to 0.045. Also, these three quantitative indexes increase with the increase of combined roughness gaussian noise. Soil moisture inversion values tend to be concentrated near mode, and the tendency to underestimate soil moisture is more obvious than the overestimation tendency. The error range of combined roughness should be controlled within a certain range of the initial value to meet the inversion accuracy requirement, and it is negatively related to the incident angle. The error control range is suitable for bare soil with low surface roughness and low sparse vegetation coverage area.

Key words: uncertainty quantification, surface combined roughness, soil moisture, skewness, kurtosis, quartile, error control range