裸露地表微波热采样深度统计模型

被动微波遥感反演土壤水分对应的土壤深度是土壤水分产品真实性检验和应用中必须确定的问题。本研究利用理论模型对影响土壤热采样深度的参数进行了分析。在此基础上,通过回归分析的方法发展了一个估算被动微波遥感土壤热采样深度的统计模型,并通过微波辐射测量实验对模型进行了验证。研究证明,理论模型模拟裸露地表发射率平均误差为0.032,基于理论模型发展的热采样深度统计模型的误差在0.5 cm左右。该统计模型可以通过土壤含水量、温度、质地和观测频率4个较容易获取的参数计算土壤微波辐射的热采样深度,为被动微波遥感土壤水分产品的真实性检验工作中地面土壤水分测量以及土壤水分遥感产品的应用提供参考。     

农田垄行结构对被动微波遥感土壤壤湿度反演的影响

根据离散方法建模垄行结构农田表面微波发射率,与地基多频率微波辐射计实测发射率比较发现：二者之间的平均绝对偏差小于0.01 ,证实了利用离散化方法建模农田表面微波发射率的可行性.在给定条件下不同观测方位角农田表面微波发射率与平坦表面的发射率差值在0.02 与0.05 之间,这说明农田结构微波辐射具有各向异性,行结构对发射率的影响在农田电磁波辐射建模过程中不可忽略.本文分析了不同土壤湿度条件下农田垄行结构可能引起的土壤湿度反演误差,结果表明,土壤湿度变化范围是0.02—0.5 cm³/cm³,垄行结构引起的土壤湿度反演误差为0—0.1 cm³/cm³, 此误差超过了土壤湿度反演的容限值,因此在进行农田参数的遥感提取过程中不可忽略周期性垄行结构对表面发射率的影响.     

Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass

The land surface temperature (LST) is an important parameter when studying the interface between the atmosphere and the Earth's surface. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing is better able to overcome atmospheric influences and to estimate the LST, especially in cloudy regions. However, methods for estimating PMW LSTs at the country and continental scales are still rare. The necessity of training such methods from a temporally dynamic perspective also needs further investigations. Here, a temporally land cover based look-up table (TL-LUT) method is proposed to estimate the LSTs from AMSR-E data over the Chinese landmass. In this method, the synergies between observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), which are onboard the same Aqua satellite, are explored. Validation with the synchronous MODIS LSTs demonstrates that the TL-LUT method has better performances in retrieving LSTs with AMSR-E data than the method that uses a single brightness temperature in 36.5 GHz vertical polarization channel. The accuracy of the TL-LUT method is better than 2.7 K for forest and 3.2 K for cropland. Its accuracy varies according to land cover type, time of day, and season. When compared with the in-situ measured LSTs at four sites without urban warming in the Tibet Plateau, the standard errors of estimation between the estimated AMSR-E LST and in-situ measured LST are from 5.1 K to 6.0 K in the daytime and 3.1 K to 4.5 K in the nighttime. Further comparison with the in-situ measured air temperatures at 24 meteorological stations confirms the good performance of the TL-LUT method. The feasibility of PMW remote sensing in estimating the LST for China can complement the TIR data and can, therefore, aid in the generation of daily LST maps for the entire country. Further study of the penetration of PMW radiation would benefit the LST estimations in barren and other sparsely vegetated environments.     

粗糙地表土壤含水量遥感模型研究

土壤粗糙度对地表二向反射率特征有重要影响而且极不稳定 ,是影响土壤含水量遥感精度的一个重要因素。该文分析土壤含水量影响反射率的物理过程 ,论证土壤含水量与土壤光谱的关系服从比尔定律 ;分析土块级土壤粗糙度影响土壤反射率的根本原因是微坡面的倾斜使二次和多次散射的比例增加。在此基础上建立粗糙地表土壤含水量遥感模型 ;实验证明模型具有较高的精度     

地基雷达的微波面散射模型对比与土壤水分反演

为了探究地基合成孔径雷达(cGBSAR)后向散射信号的时空变化规律和研究雷达土壤水分反演的影响因素,在内蒙古闪电河流域的昕元牧场站进行了地基雷达观测试验,本文结合以上观测试验的地基雷达数据进行波段、入射角度、极化通道3个雷达参数以及地表粗糙度参数对雷达的后向散射系数影响的分析,然后利用以上分析结果选择地表微波面散射模型...     

植被覆盖地表土壤水分遥感反演

以地域特色突出的新疆渭干河-库车河三角洲绿洲为研究区,联合使用雷达数据和光学遥感数据,对干旱区绿洲土壤和植被水分信息进行提取。在同期光学遥感影像数据提取植被归一化差分水分指数基础上,利用"水-云模型"从雷达数据总的后向散射中去除植被影响,建立土壤后向散射系数与土壤含水量的关系,相关系数为HH极化R2=0.5227,HV极化R2=0.3277。结果表明利用C波段HH极化雷达影像数据结合光学影像数据,进行干旱半干旱地区棉花、玉米等农作物种植区地表土壤水分反演时,在中等覆盖条件下去除植被影响有较好的效果。     

海表温度对SMOS盐度遥感反演精度的影响

利用星载微波辐射计对全球海表盐度的卫星遥感探测,其精度会受到多种环境因子的影响。采用广义加性模型GAM和偏最小二乘法PLS分析了水温对海表盐度遥感反演精度的影响,同时,利用ARGO观测数据对SMOS卫星反演的赤道太平洋和西北太平洋海表盐度进行精度检验。结果表明,水温对海表盐度反演精度具有显著影响,且Stokes矢量第一参数(总辐亮度)是海表盐度反演的最佳亮温参数。在平均水温约16℃时的均方误差约为0.9 psu,23℃水温下的均方误差约为0.7 psu,30℃水温下的均方误差约为0.4 psu,即高水温下盐度反演精度相对较高。     

山区地形效应对微波辐射特征与土壤水分反演的影响—以青藏高原地区为例

采用星载微波辐射计AMSR-E的低频C波段(6.925GHz),改进了山区微波辐射传输方程,以中国青藏高原地区为例,研究山区可能产生的多种地形效应对微波辐射特征以及土壤水分反演的影响。结果表明,地形效应使得垂直极化亮温最多衰减达到16K,水平极化的亮温最大增强了18K,土壤水分在地形的影响下将被高估超过最大允许误差4%。最后,利用地形效应模拟模型计算的山区地表有效发射率,为山区土壤水分的反演提出了可行的地形校正方法。     

一种极干旱区水热通量遥感模型反演

针对极干旱的土壤水热通量遥感估算研究少和缺乏模型验证分析的不足,该文选取新疆塔克拉码干沙漠东南缘米兰绿洲,借助2010年8月7日TM影像数据,结合地面观测数据和土地覆盖类型解译,利用SEBAL模型反演了研究区地表通量。结果表明:该方法在极干旱区的模拟精度较高,研究区地表能量平衡各分量具有明显的时空分布特征。地表通量参数的遥感反演估算对极干旱区的生态保护、灾害监测和地区水资源管理具有重要现实意义。     

降水云中液水含量的空基遥感

在建立不同降水云垂直结构模式的基础上，利用矢量微波辐射传扬模式进行空基云雨遥感的数值试验。结果表明：星载微波辐射计6．9GHz通道对降水云的冰层很不敏感，而对降水云校总液水含量(LWC)非常敏感；辐射亮温(Tb)随LWC呈单调变化关系；进一步考查这个频率下b的两个极化分量差△Tb(＝Tbv—Tbh)和LWC的关系发现，不同云结构(对流云和层状云)对△Tb-LWC关系影响较小。研究分析表明，6．9GHz的v,h两个通道有可能用于遥感降水云的液水总量。     

遥感数据时空尺度对地理要素时空变化分析的影响

地理要素的时空变化分析对于了解和掌握地表的规律性有着重要的作用。利用地面站点观测、实地调查等传统方式获取数据,对地理要素进行时空变化分析是最常用的方法。但该类方法往往表现的是"点尺度"观测,不能在大尺度情况下准确地反映地表的时空变化信息。遥感卫星能以一定的时间间隔获得空间连续的对地"面尺度"观测数据,然而其特定的空间和时间分辨率使其获取的地表信息仍十分有限。同时地表的空间异质性和个别地表短时间内的快速变化,使得利用遥感数据对地理要素进行时空变化分析时,时空变化分析结果会随观测尺度而发生改变。本文从空间尺度和时间尺度两方面综述遥感数据时空尺度对地理要素的时空变化分析产生的影响和原因,并针对这些问题总结了减小时空尺度对结果不确定性影响的现行方法。可以通过多源遥感协同观测和反演、尺度转换、空间建模等方法减小空间尺度引起的不确定性;通过联合多时相遥感数据的方法减小时间尺度引起的不确定性。在实际应用中,应根据所观测地理要素的实际情况,综合分析选择合适的方法。     

基于遥感和GIS的水土流失因子信息提取与分级定标研究--以攀枝花市为例

以攀枝花市为例,主要选用美国陆地卫星2003年的Landsat-7 ETM+遥感数据、1∶10万地形图,运用ERDAS/IMAG INE遥感图像处理软件及M apG IS软件提取对水土流失影响较大的植被覆盖度、地形坡度、沟谷密度、土地利用类型等因子;根据中华人民共和国行业标准《土壤侵蚀分类分级标准》(SL 190-96),建立攀枝花市水土流失强度面蚀分级指标,为进一步水土流失监测研究提供科学依据。     

An assessment of gully erosion along major armoured roads in south-eastern region of South Africa: a remote sensing and GIS approach

An assessment of gully erosion along road drainage-release sites is critical for understanding the contribution of roads to soil loss and for informed land management practices. Considering that road-related gully erosion has traditionally been measured using field methods that are expensive, tedious and limited spatially as well as temporally, it is important to identify affordable, timely and robust methods that can be used to effectively map and estimate the volume of gullies along the road networks. In this study, gullies along major roads were identified from remotely sensed data sets and their volumes were estimated in a Geographic Information Systems environment. Also, the biophysical and climatic factors such as vegetation cover, the road contributing surface area, the gradient of the discharge hillslope and rainfall were derived from remotely sensed data sets using Geographic Information Systems techniques to find out whether they could explain the morphology of gullies that existed in this area. The results of this study indicate that hillslope gradient (R²?=?0.69, α = 0.00) and road contributing surface area (R²?=?0.63, α = 0.00) have a strong influence on the volume of gullies along the major roads in the south-eastern region of South Africa, as might have been expected. However, other factors such as vegetation cover (R² = 0.52, α = 0.00) and rainfall (R² = 0.41 and α = 0.58) have a moderately weaker influence on the overall volume of gullies. Overall, the findings of this study highlight the importance of using remote sensing and Geographic Information Systems technologies in investigating gully erosion occurrence along major roads where detailed field work remains a challenge.