【作者】 徐军；

【导师】 蒋建军；

【作者基本信息】 南京师范大学 ， 遥感技术与应用， 2008， 硕士

【摘要】 土壤含水量是水文学、气象学以及农业科学研究领域的一个重要参数,而区域性土壤含水量监测是农田水资源管理以及农作物旱情监测的一项重要内容,也是陆面过程研究必不可少的一个参量。传统的方法难以大范围的揭示土壤含水量的空间分异特征。遥感技术的出现为实现大范围地区土壤水分含量的实时或准实时动态监测提供了可能性。目前,利用热红外遥感监测土壤水分的方法都是基于温度与土壤含水量之间关系建立统计模型,而对发射率与土壤含水量的关系的研究还非常少。本论文通过仿自然态土样的制备,发射率光谱以及土壤水分含量的定期监测,对获得的不同水分含量的土壤热红外发射率光谱数据比较分析,阐述了不同土壤水分含量的热红外发射率光谱特征并通过水分诊断指数建立相应估算模型;模拟ASTER热红外通道数据,阐述其光谱特征并利用模拟的ASTER通道数据建立了土壤水分含量估算模型。研究结果表明:1、室内实测土壤发射率光谱特征为:8～9.51μm随土壤含水量的增加,土壤热红外发射率也有不同程度的增加,光谱曲线之间相对平行;土壤水分含量的增加使得发射率光谱曲线在8～9.51μm变得平直,reststrahlen吸收特征相对减弱。在9.5～11μm土壤热红外发射率大致呈单调递增趋势,但与土壤含水量的变化无明显相对应的关系。在11～14μm随土壤含水量的增加,土壤热红外发射率有不同程度的减小,而且在12.7μm附近存在一个吸收谷,其吸收的深度大致随土壤水分的增加而增加。该吸收谷有可能是土壤水分的吸收而产生。2、通过对发射率光谱数据的微分、差分以及标准化比值处理变换,运用统计单相关分析方法确定了诊断土壤水分含量的敏感波段为8.237μm,对敏感范围8.194～8.279μm输出均值进行标准化比值处理作为自变量,提出水分诊断指数的概念,建立了土壤水分含量和水分诊断指数的对数统计模型。3、模拟ASTER热红外通道B10～B14的数据,模拟结果表明B10、B11和B12表现为谷-峰.谷的特征,B10和B12分别对应了reststrahlen不对称双吸收谷,其特征较明显。4、通过各个波段数据与土壤水分含量的相关性分析,结果表明ASTER的B10～B12波段与土壤水分含量均存在正相关关系,就单个波段而言其中B10波段与土壤水分含量最为敏感;而ASTER的B14波段与土壤水分含量存在负相关关系。水分诊断指数B10/B14减弱了背景信息、土壤粗糙度、传感器与光源几何位置等乘性因子的影响,与土壤水分含量更为敏感。通过统计建模,建立了土壤水分含量和土壤水分诊断指数B10/B14的对数统计模型。更多还原

【Abstract】 Soil moisture content is a vital parameter in the study field of hydrology, meteorology and agriculture science. It is indispensable factor for indicating soil drought and development of plants, conducting the management of water resource, studying the processes of land surface. It is difficult to indicate spatial variability of soil moisture content at large area by conventional methods. But it is possible to monitor soil moisture content in timing and dynamic at large area as the development of remote sensing technology. At present, most methods to monitor soil moisture content by thermal infrared remote sensing are based on the relationship of land surface temperature and soil moisture content. The search to the relationship between emissivity and soil moisture content is considered less.This paper takes paddy soil in Kunshan as study object. Through imitating nature soil samples, the spectrum of emissivity and soil moisture are measured in order to expound the characteristic of emissivity in the thermal infrared part of the spectrum with different soil moisture content. A model of estimating the moisture content in soil is attempted to make based on Moisture Diagnostic Index (MDI). The bands in the thermal infrared part of ASTER satellite are imitated by the data of emissivity which we measured. Through analyzing the characteristic of spectrum which is imitated, we also try to construct a model to estimate soil moisture content using the stimulant bands in the thermal infrared part of ASTER satellite. The dissertation concentrates on the following aspects:1. In general, the spectral characteristic of soil emissivity in laboratory includes the following aspects. First, in the region of 8～9.5μm, along with the increase of soil moisture content, the emissivity of soil has varying degree increases. The spectral curves are parallel relatively and have a tendency to become horizontal and the absorbed characteristic of reststrahlen is also weakened relatively with the increase of soil moisture in this region. Secondly, in the region of 9.5～11μm, the emissivity of soil has a tendency of increasing, but there is no obvious relationship with the increase of soil moisture. And thirdly, in the region of 11～14μm, along with the increase of soil moisture content, the emissivity of soil has varying degree decreases. There is an absorption vale near about 12.7μm. As along with the soil moisture content increases, the depth of absorption also increases. This phenomenon may be coursed by soil moisture absorption.2. Methods as derivative, difference and standardized ratio transformation may weaken the background noise effectively to the spectrum data. Especially using the ratio of the emissivity to the average of 8～14μm may obviously enhance the correlation between soil moisture and soil emissivity. According to the result of the correlation analysis, the 8.237μm is regarded as the best detecting band for soil moisture content. Moreover, based on the Moisture Diagnostic Index (MDI) in the 8.194～ 8.279μm the logarithmic model of estimating soil moisture is made.3. Bands in the thermal infrared part of ASTER satellite are imitated. The simulation results show that B10, B11 and B12 have the characteristic of vale-peak-vale. B10 and B12 correspond to one of absorption reststrahlen vales respectively. And it is obvious.4. According to the result of the correlation analysis between each band and soil moisture, the results suggest that B10, B11 and B12 have positive correlation with soil moisture content, while B14 has negative correlation with soil moisture content. As far as each band is concerned, B10 is most sensitive to soil moisture content. Moisture Diagnostic Index B10 / B14 weaken the background noise effectively to the spectrum data, is more sensitive than B40. At last, the logarithmic model of estimating soil moisture is made based on Moisture Diagnostic Index B10 / B14.更多还原