Knowledge Resource Center for Ecological Environment in Arid Area
| 利用ASAR与Hyperion数据联合反演植被覆盖地区土壤水分 | |
| 其他题名 | Estimation of Surface Soil Moisture in Vegetated area from ASAR Dual-Polarized and Hyperion Hyperspectral Data |
| 马建威 | |
| 出版年 | 2013 |
| 学位类型 | 硕士 |
| 导师 | 宋小宁 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 土壤水分是水分平衡的重要参量，是联系地表水、地下水和生物地球循环的核心和纽带，在水文、气候和生态等研究领域都具有十分重要的作用。光学和微波遥感反演土壤水分各有优势和不足，联合两种数据源，对提高土壤水分反演精度有重要意义。本论文依托中国科学院西部行动计划项目（三期）—“黑河流域生态-水文遥感产品生产算法研究与应用试验”（KZCX2-XB3-15），利用ASAR和Hyperion数据，开展了联合雷达和高光谱数据反演植被覆盖地区土壤水分的研究，主要从以下几方面开展了研究： 基于AIEM模型，建立了利用ASAR双极化雷达数据反演裸土区土壤水分的模型。首先利用AIEM模型模拟裸露地表C波段SAR信号的后向散射特征，通过非线性回归的方法建立四种极化下的裸露地表后向散射模型；然后将不同极化组合、不同角度ASAR-AP数据分别输入所建立的后向散射模型，通过模型的联立计算，消除了模型中的粗糙度参数，从而拟合得到土壤水分反演模型；并通过对比分析，选出了反演土壤水分的最佳极化组合方式和最佳角度；最后，将建立的反演模型应用到黑河流域中游临泽草地站试验区，实现了区域尺度土壤水分的定量反演，并利用实测的土壤水分数据进行检验，结果表明模型具有较好可靠性和适用性。 基于PROSAIL模型，建立了利用Hyperion数据反演植被冠层含水量的模型。PROSAIL模型模拟植被冠层反射特征表明，970nm水吸收带右侧曲线（980nm-1070nm）一阶导数D980-1070与冠层含水量关系密切，决定系数达0.96。基于此，利用中心波长为983nm、993nm、1003nm、1013nm、1023nm、1033nm、1043nm、1053nm和1063nm的Hyperion的9个波段数据计算D980-1070，并利用所建模型反演植被冠层含水量。最后，利用黑河流域盈科绿洲的实测数据对反演结果进行了验证，其平均相对误差为12.5%，均方根误差在0.1 kg?m-2内，结果表明该模型可靠。 联合ASAR双极化数据和Hyperion高光谱数据，建立了一个半经验的植被覆盖区土壤水分反演模型。该模型是基于水云模型发展而来，增加了水云模型忽略的土壤-冠层的二次散射项，并利用MIMICS模型对所建反演模型进行了参数的率定。然后联合双极化ASAR和Hyperion数据实现了盈科绿洲地区土壤水分的反演。初步验证表明，该模型可靠，适用于获取区域植被覆盖地区土壤水分。 |
| 英文摘要 | Soil moisture is a key factor in water balance, which provides a vital link among surface water, ground water and biogeochemical cycle, and plays an important role in study of hydrology ,climatic and ecology. Optical and microwave remote sensing have their respective advantages in inversion of soil moisture content. It will be very important in retrieval of soil moisture if we can efficiently use the two data source. This paper focuses on quantitative remote sensing for joint retrieval of soil moisture content using both SAR and hyperspectral remote sensing .Following is the main content of this work: Based on the AIEM model, a novel model for quantitative estimation of soil moisture in bare surface using ASAR dual-polarized data was developed. Firstly, synthetic aperture radar (SAR) backscattering characteristic of bare surface at C band was simulated by using advanced integrated equation model (AIEM), and four bare surface backscattering models with different polarization were established. Secondly, with simultaneous equations of the former four formulas, the surface roughness was eliminated, and models used to estimate soil moisture on bare surface were derived from simulated multipolarization and multiangle ASAR-AP data. Based on these, the best combination of polarization and incident angle was determined. Finally, soil moisture in the middle stream of the Heihe River Basin was estimated. The field measured data demonstrated that the proposed method was capable of retrieving surface soil moisture for both sparse grassland and homogeneous farmland area. Based on the PROSAIL model, a novel model for quantitative inversion of vegetation canopy water content using Hyperion hyperspectral data was explored. Firstly, characteristics of vegetation canopy reflection were investigated with the PROSAIL radiative transfer model, and it showed that the first derivative at the right slope (980-1070nm) of the 970 nm water absorption feature (D980-1070) was closely related to VCWC, and determination coefficient was up to 0.96. Then, 9 bands centered at wavelengths of about 983 nm, 993 nm,1003 nm,1013 nm,1023 nm,1033nm , 1043 nm,1053 nm and 1063 nm of Hyperion data were selected to calculate D980-1070, and VCWC was estimated using the proposed method. Finally, the retrieval result was verified using field measured data in Yingke oasis of the Heihe basin. It indicated that the mean relative error was 12.5%, RMSE was within 0.1kg?m-2 and the proposed model was practical and reliable. A new semi-empirical soil moisture inversion model for vegetated area was developed using ASAR and Hyperion data collaboratively. The algorithm was based on extension of Water-Cloud model by adding the ground-crown term which was neglected in Water-Cloud model, and the parameters in the inversion model were calibrated using MIMICS model. Then, soil moisture in Yinke was estimated using ASAR and Hyperion data. Preliminary results demonstrated the feasibility of this method, which was suitable for inversion of soil moisture in the region with vegetation coverage. |
| 中文关键词 | 土壤水分 ; ASAR ; Hyperion ; AIEM ; PROSAIL ; 水云模型 ; MIMIC |
| 英文关键词 | soil moisture ASAR Hyperion AIEM PROSAIL Water-Cloud Model MIMICS |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 环境工程 |
| 来源机构 | 中国科学院大学 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287201 |
| 推荐引用方式 GB/T 7714 | 马建威. 利用ASAR与Hyperion数据联合反演植被覆盖地区土壤水分[D]. 中国科学院大学,2013. |
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