Knowledge Resource Center for Ecological Environment in Arid Area
| 多源遥感数据联合提高土壤水分反演精度——以HiWATER PLMR 数据为例 | |
| 其他题名 | Reduce the Uncertainty of Soil Moisture Retrieval by Using Multi-angles and Multi-sources Remote Sensing Data—the Example of HiWATER PLMR Data |
| 李大治 | |
| 出版年 | 2014 |
| 学位类型 | 硕士 |
| 导师 | 晋锐 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 土壤水分是气候、水文学研究中的重要变量；微波遥感，尤其是被动微波遥感，是现今获取区域地表土壤水分的重要手段，而L波段更是微波反演土壤水分的最优波段。 本文利用全局敏感性分析方法和集合反演方法定量研究了L波段被动微波反演土壤水分过程中会出现的各种不确定性；并针对航空PLMR（Polarimetric L-band Multi-beam Radiometer）微波辐射计的微波亮温观测开展了多种反演策略的比较研究。 首先分别对植被覆盖和裸土的微波辐射传输模型L-MEB（L-band Microwave Emission of the Biosphere）进行参数敏感性分析得到影响微波辐射传输模型输出的主要参数及其量化的贡献比例；通过对不同极化、不同角度和不同参数值域下的敏感性分析，得到各参数的一阶和总敏感性指数，以及参数耦合项的大小，比较参数在各个角度和极化的不同表现。 接着通过集合反演来研究土壤水分反演过程中由观测误差、模型参数误差以及不同的反演策略所引起的不确定性： （1）通过模拟的多角度微波亮温和不同量级的观测噪声生成亮温集合，进行单参数集合反演实验，得到不同角度和极化下采用L-MEB模型反演土壤水分时，受到亮温观测误差的不同影响，即观测误差引起的不确定性； （2）对主要敏感参数根据其误差噪声进行随机扰动生成参数集合，通过土壤水分单参数集合反演来定量研究敏感参数的误差在不同的观测角度和极化方式下对土壤水分反演不确定性的影响，即参数误差引起的不确定性； （3）另外通过对模拟亮温进行敏感参数的多参数集合反演，可以对多参数反演过程中可能产生的不确定性进行分析，即反演策略的不确定性；从而提炼出最优的反演方法和反演策略，为实际的土壤水分反演算法提供参考。 通过对模型参数的敏感性分析和土壤水分反演中不确定性来源的分析，发现单通道单参数反演（尤其是大入射角V极化通道）最容易受到观测误差和参数误差的影响，其在不同入射角和极化下的表现与参数的敏感性密切相关；观测通道数量的增加可以显著降低观测误差和参数误差的影响，以及反演的不确定性；在参数反演时提供较精确的初猜值同样可以减少反演不确定性。 最后，在L-MEB模型敏感性分析和反演不确定性理论分析的基础上，依托“黑河流域生态-水文过程综合遥感观测联合试验”（Watershed Allied Telemetry Experimental Research：HiWATER）黑河中游绿洲试验区的机载PLMR微波辐射计亮温数据及地面同步观测，并利用MODIS地表温度产品（MOD11A1）和叶面积指数产品（MYD15A2）作为模型及反演中的先验辅助信息，采用微波辐射传输模型L-MEB模型和LM（Levenberg-Marquardt）优化算法，针对土壤水分（SM）、植被含水量（VWC）和地表粗糙度（Hr）这3个敏感性参数，分别进行了土壤水分单参数反演、双参数反演以及三个参数同时反演，并利用地面生态水文无线传感器网络（WSN）观测数据对土壤水分反演结果进行验证。 通过不同反演策略的比较可以得出结论，多源辅助数据及PLMR双极化、多角度观测信息的综合应用可以显著降低土壤水分定量反演的不确定性，提高反演精度；同时也证明在合理的模型参数和反演策略约束下，L-MEB模型和土壤水分反演算法可以达到0.04cm3/cm3的反演精度，另外说明无线传感器网络可以在遥感产品真实性检验中起到重要作用。 |
| 英文摘要 | Soil moisture is one of the important variables in the climate and hydrology research. Remote sensing can map the soil moisture distribution at regional or global scale. Microwave remote sensing now has been the main method to retrieve the soil moisture information, especially using satellite-based passive microwave radiometer. L-band is most suitable for the microwave remote sensing of soil moisture due to its longer wavelength. This dissertation will use a global sensitivity analysis method, simulated data and real observed PLMR data to study the various kinds of uncertainties during the soil moisture retrieval using L-band microwave brightness temperatures. First, we get the main parameters which dominate the major output of L band microwave radioactive transfer model and their quantitative contributions by sensitivity analysis. And by changing the incidence angle, polarization and the range of parameter, we can get the first-order and total sensitivity index of each parameter at different situation. Then, the soil moisture retrieval uncertainties caused by the observation error, parameter error and retrieval strategy are studied based on the ensemble retrieval. (1) Different levels of Gaussian-distributed noise, representing the observation errors, are randomly added to the modeled brightness temperatures to produce an ensemble dataset. The single parameter retrieval of soil moisture is conducted based on this ensemble dataset to obtain the RMSE (root mean square error) of the retrieved soil moisture. (2) To depict the model parameter uncertainty, the parameter ensembles, including parameter errors, are produced by adding different levels of Gaussian noise to each sensitive parameter. Then, the ensemble retrieval of soil moisture is performed to estimate the response of soil moisture retrieval RMSE on the parameter error. (3) Using the simulated PLMR multi-angle brightness temperatures, we designed an ensemble retrieval experiment to test the utility of multi-angle remote sensing observations and pre-estimation information of the model parameters for reducing the inversion error of the multi-parameter retrieval. The results of parameter sensitivity and uncertainty analysis show significant differences at different incidence angles and polarizations. The soil moisture retrieval using single observation channel is more likely impacted by the observation error and parameter uncertainty, especially at large incidence angle and under V-polarization; and it can be explained by sensitivity analysis. Increasing observation channels can obviously reduce the uncertainties caused by observation, parameter and inverse method. Giving reasonable pre-estimation of the parameters which will be retrieved can also reduce the uncertainty during inverse process. The multi-parameters (two or three) retrieval method combined with suitable pre-estimations of parameters is proposed in this paper. At last, we use airborne PLMR radiometer data combined with MODIS LST (MOD11A1) and LAI (MOD15A2) products to retrieve surface soil moisture at the artificial oasis experimental area of HiWATER by L-MEB radiative transfer model and LM (Levenberg-Marquardt) optimization algorithm. The three retrieving strategies are tested, including single parameter, two and three parameters selected from soil moisture, vegetation water content and surface roughness. The comparison analysis shows the multi-angle and dual-polarization PLMR brightness temperatures combined with prior information from operational remote sensing products can obviously reduce the uncertainty of retrieval process and improve the retrieval accuracy. This paper proves that with reasonable model parameters and retrieval method, the L-MEB model can achieve 0.04cm3/cm3 accuracy requirement for soil moisture remote sensing product. This paper also reveals the importance of using wireless sensor network in the verification of remote sensing products. |
| 中文关键词 | PLMR微波辐射计 ; 土壤水分反演 ; 微波辐射传输模型 ; 参数敏感性分析 ; HiWATER |
| 英文关键词 | PLMR radiometer soil moisture retrieval radiative transfer model sensitivity analysis HiWATER |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287394 |
| 推荐引用方式 GB/T 7714 | 李大治. 多源遥感数据联合提高土壤水分反演精度——以HiWATER PLMR 数据为例[D]. 中国科学院大学,2014. |
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