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| 青藏高原地表水热状况的被动微波卫星反演算法研究 | |
| 其他题名 | Development of passive microwave retrieval algorithm for estimation of soil hydrothermal state on the Tibetan Plateau |
| 韩孟磊 | |
| 出版年 | 2016 |
| 学位类型 | 博士 |
| 导师 | 阳坤 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 土壤水热状况是陆地水循环、能量循环和碳循环的重要控制因子，监测区域地表水热状况在水文、气象和农业等领域具有广泛的需求。被动微波信号对云层的穿透性高，受天气的影响小，对土壤水热状况更敏感，为监测地表水热状况变化提供了可行的技术手段。然而星载传感器接收到的微波亮温受到植被、地表粗糙度、土壤质地、土壤温湿度等诸多因子的影响，使得利用被动微波估计高精度的地表水热状态的研究进展缓慢。青藏高原具有大气稀薄、水汽含量小、生物量低等特点，利用被动微波反演该区域土壤水热状况具有很大优势。因此，论文利用AMSR-E被动微波数据，发展了青藏高原地区地表状况 (土壤表层温度、土壤水分、地表冻融) 的反演算法。主要研究成果包括： (1) 发展了基于AMSR-E多波段亮温的表层土壤温度反演算法。论文利用高原那曲观测网的表层土壤温度数据与AMSR-E五个低频波段的亮温数据进行回归分析，建立了新的被动微波表层土壤温度反演算法。该算法显著提高了青藏高原地区土壤表层温度的估算精度，同时，在全球不同气候、植被覆盖区观测网的评估结果也表明新算法可以用于全球土壤表层温度的监测，从而弥补了热红外遥感土壤表层温度反演过程中易受云雾影响的不足。(2) 在微波反演土壤水分模型中引入了陆面数据同化系统估计的地表粗糙度和植被光学厚度参数，作为模型的率定参数值，显著改善了青藏高原土壤水分的估计精度。地表粗糙度和植被光学厚度参数输入的准确与否是影响土壤水分反演准确度的重要因素之一。本文使用的陆面数据同化系统 (LDAS) 利用卫星数据估算出单位像元的地表粗糙度，有效解决了土壤水分反演过程中对这些参数全球使用同一定值的问题。率定后的土壤水分反演算法在青藏高原的那曲、玛曲观测网及蒙古高原的Mandal Govi观测网进行了评估，结果表明该算法具有较高的反演精度。利用同化系统率定土壤水分反演算法的参数，降低了对辅助数据的依赖性，有效地提高了土壤水分反演算法的实用性。同时本文比较了反演的微波发射温度与实测表层土壤温度，发现夜间微波发射温度受植被影响，在植被覆盖区域总是低于土壤表层温度。(3) 基于近些年在青藏高原地区建立的土壤温湿度观测网，建立了监测青藏高原地表冻融状态的“双指标算法”。两个判别指数中，标准偏差指数 (SDI) 用来指示土壤冻融过程中表层水分含量的变化过程; 36.5 GHz的垂直极化亮温用来指示表层土壤冻融过程中热状况的变化情况。这两个指数的阈值范围都是基于AMSR-E亮温数据和实测的青藏高原地表温度数据得到的。验证结果表明，不管是白天还是晚上，新的算法在青藏高原半湿润半干旱区的判别准确率较高 (>90%)，但在干旱区微波信号来自于深层土壤，36.5 GHz的垂直极化亮温可以更好指示表层土壤冻融状况。 |
| 英文摘要 | Soil hydrothermal condition is an important controlling factor of land water cycle, energy cycle and carbon cycle. Monitoring of regional surface hydrothermal condition has great demand in the research fields of hydrology, meteorology, agriculture and so on. Passive microwave remote sensing is one of the most effective methods to monitor the surface hydrothermal condition. Passive microwave satellites have the advantage of high revisit frequency that facilitates near-real-time monitoring. Moreover, low-frequency microwave signals can penetrate clouds and thus can work under all-sky conditions. However, the brightness temperature is influenced by both land state (soil moisture, soil temperature and surface soil freeze-thaw state) and land parameters (vegetation cover condition, surface roughness and soil texture). These complexities lead to unsatisfactory accuracy of current hydrothermal condition estimations, although several passive microwave satellites were launched during last decade. The Tibetan Plateau is characterized by small air mass, water vapor content, biomass, so it has a great advantage to retrieve the soil hydrothermal condition in this area using passive microwave data. This study aims at the development of new algorithms to retrieve the soil hydrothermal conditions (soil temperature, soil moisture and surface soil freeze-thaw state) on the Tibetan Plateau based on passive microwave data of AMSR-E and three soil moisture and temperature measuring networks. Major research achievements are summarized below: (1) A statistical algorithm was established to retrieve the surface soil temperature based on brightness temperature from different frequencies. As the brightness temperature at vertical polarization of AMSR-E is more sensitive to land surface temperature, the soil temperature retrieval algorithm was established by a regression analysis of observed soil temperature and the vertically-polarized brightness temperatures on a Naqu network in the central Tibetan Plateau (CTP-Naqu). The new algorithm significantly improves the accuracy of the soil temperature estimation on the Tibetan Plateau, and also can be used to monitor the global surface temperature. The new algorithm compensates the deficiency of thermal infrared-based algorithms that are vulnerable to clouds contamination in terms of land surface temperature retrieval. (2) In order to improve the accuracy of soil moisture retrieved from passive microwave data, soil roughness and vegetation optical parameters estimated by land data assimilation system (LDAS) were introduced as the input parameters of a Q-h algorithm for soil moisture retrieval. The accuracy of the surface roughness and the vegetation optical parameters is crucial in the soil moisture retrieval. Instead of using an identical value for these parameters at global scale, an LDAS was used to estimate their values in each satellite pixel by assimilating microwave data. With the gridded parameter values, the soil moisture retrieval algorithm was evaluated in two soil moisture networks on the Tibetan Plateau and one soil moisture network on the Mongolian Plateau, respectively. The results showed that the new estimates had much higher accuracy than current major products. Moreover, although most of current schemes assume that the microwave emission temperature at night can be approximated equal to surface soil temperature, we found their differences were significant in vegetated areas; that was, the estimated microwave emission temperature was usually lower than the observed surface soil temperature.(3) A dual-index microwave algorithm with AMSR-E data was developed for the detection of soil surface freeze/thaw state on the Tibetan Plateau. One index is the standard deviation index (SDI) of brightness temperature, which is defined as the standard deviation of horizontally-polarized brightness temperatures at 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz. It is the major index and is used to reflect the reduction of liquid water content after soils get frozen. The other index is the 36.5 GHz vertically polarized brightness temperature , which is approximately linearly correlated with soil temperature. The threshold values of the two indices were determined within one grid of the CTP-Naqu network located in a semi-arid climate of the Tibetan Plateau, and the algorithm was validated with other grids from the same network. Further validations are conducted based on other two networks located in different climate regimes (semi-humid and arid, respectively). Results showed that the classification accuracy using this algorithm was more than 90% for the semi-humid and semi-arid regions. Nevertheless, the algorithm had limited capability in identifying the soil surface freeze/thaw state in arid regions because microwave signals can penetrate deep dry soils and thus embody the bulk information beneath the surface layer. |
| 中文关键词 | 青藏高原 ; 土壤冻融 ; 地表温度 ; 土壤水分 ; 被动微波 |
| 英文关键词 | Tibetan Plateau Freeze-thaw Soil surface temperature Soil moisture Passive microwave. |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 大气物理学与大气环境 |
| 来源机构 | 中国科学院青藏高原研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287739 |
| 推荐引用方式 GB/T 7714 | 韩孟磊. 青藏高原地表水热状况的被动微波卫星反演算法研究[D]. 中国科学院大学,2016. |
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