卫星遥感在地理国情普查中的应用

第一次全国地理国情普查工作的主要基础影像是航空摄影遥感影像,但是卫星遥感影像在本次地理国情普查中也发挥了重要作用。高分辨率卫星遥感影像可以作为无法获得航空摄影遥感影像地区的基础影像补充,并将成为地理国情普查时点核查数据更新和修正的主要数据源影像。中分辨率卫星遥感影像可以用于时点核查时快速发现变化地类,并用于评估地理国情普查数据的准确度。卫星遥感将是未来实现持续地理国情监测的主要手段。     

试用极轨气象卫星遥感监测土壤墒情

讨论了用极轨气象卫星资料监测表层土壤湿度的方法。在建立土壤湿度与卫星遥感资料的关系时，为了消除土壤条件的影响，把全省分为４个区域来研究。考虑了气候条件，植被状况不同的影响，并给出了简单的订正方法，同时也给出了卫星资料的重新地理校正，资料的输出及存储方法等。     

PMODTRAN: a parallel implementation based on MODTRAN for massive remote sensing data processing

MODerate resolution atmospheric TRANsmission (MODTRAN) is a commercial remote sensing (RS) software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth's atmosphere and the radiation observed by a remote sensor. However, when very large RS datasets must be processed in simulation applications at a global scale, it is extremely time-consuming to operate MODTRAN on a modern workstation. Under this circumstance, the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task. This paper presents PMODTRAN, an implementation of a parallel task-scheduling algorithm based on MODTRAN. PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster. In addition, PMODTRAN can distribute tasks with different levels of granularity and has some extra features, such as dynamic load balancing and parameter checking.     

利用“3S”技术进行北京地区土壤水分监测应用技术研究

该文主要对适合于北京地区的土壤水分卫星遥感监测方法，利用GIS及GPS工具，提高对土壤水分卫星遥感监测结果的分析能力进行了探讨。在分析研究北京地区土壤水分卫星遥感监测模式及适宜使用的下垫面状况和时段的基础上，提出了一批具有一定物理意义和应用价值的遥感模式。利用GIS和GPS技术实现包括遥感信息在内的多种数据的复合，以影像的方式，将地表地理状况与土壤水分卫星遥感监测结果结合起来，实现了RS、GIS及GPS的融合，提高了土壤水分卫星遥感监测的精度。     

联合PROSAIL模型和植被水分指数的低矮植被含水量估算

植被冠层含水量CWC(Canopy Water Content)和植被地上部分含水量VWC(Vegetation Water Content)对于植被健康状况和土壤干旱监测具有重要意义.本文联合PROSAIL辐射传输模型和植被水分指数NDWI(Normalized Difference Water Index),发展了...     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

基于EOF分析对南海西北海域水体光谱特性的研究

This study presents an analysis of the spectral characteristics of remote sensing reflectance(R_(rs)) in northwestern South China Sea based on the in situ optical and water quality data for August 2018.R_(rs)was initially divided into four classes,classes A to D,using the max-classification algorithm,and the spectral properties of whole R_(rs) were characterized using the empirical orthogonal function(EOF) analysis.Subsequently,the dominant factors in each EOF mode were determined.The results indicated that more than 95% of the variances of R_(rs) are partly driven by the back-scattering characteristics of the suspended matter.The initial two EOF modes were well correlated with the total suspended matter and back-scattering coefficient.Furthermore,the first EOF modes of the four classes of R_(rs)(A-D R_(rs)-EOF_1) significantly contributed to the total variances of each R_(rs) class.In addition,the correlation coefficients between the amplitude factors of class A-D R_(rs)-EOF_1 and the variances of the relevant water quality and optical parameters were better than those of the unclassified ones.The spectral shape of class AR_(rs)-EOF_1 was governed by the absorption characteristic of chlorophyll a and colored dissolved organic matter(CDOM).The spectral shape of class B R_(rs)-EOF_1 was governed by the absorption characteristic of CDOM since it exhibited a high correlation with the absorption coefficient of CDOM(a_g(λ)),whereas the spectral shape of class C R_(rs)-EOF_1 was governed by the back-scattering characteristics but not affected by the suspended matter.The spectral shape of class D R_(rs)-EOF_1 exhibited a relatively good correlation with all the water quality parameters,which played a significant role in deciding its spectral shape.     

高分辨率遥感下的盖州滩潮沟发育分维特征和贝类生境区信息研究

通过盖州滩潮沟的“资源2”号高分辨率遥感卫星影像与TM影像等多源信息融合,同时采用形态分维的结果,获取了盖州滩潮沟的图形及逆信息树特征,并以此建立了计算模式.量化计算了盖州滩南、北部潮沟发育特征,它反映了两者间的分异度;采样监督分类分出盖州滩高、中、低潮滩分布及其相应面积以及盖州滩的贝类生境区影像特征与滩涂分布的相关性.对盖州滩的工程实施问题进行了讨论.     

结合地物空间特性的高光谱图像分类方法研究

以往的高光谱或多光谱图像分类与识别,往往只关注像元光谱维上的特性,其一切特征统计也只在光谱及波段维上展开。但是自然界的复杂性、混合像元问题的存在,仅靠像元的光谱特性是不够的,常会出现"麻点"现象。针对这一问题,本文提出一种结合地物空间特性的高光谱图像分类方法,其分类过程可以分为两个阶段,第一阶段是基于像元光谱特性的图像分类,获得影像分类图;第二阶段是针对第一阶段的分类结果,结合地物空间特性进行空间后分类处理。试验研究结果表明,该方法能够保持地块的连续性和均一性,同时克服了"麻点"现象,大大提高分类的精度。     

基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型

旨在寻找叶绿素a的高光谱遥感敏感波段并建立其定量估算模型。通过对太湖水体的连续监测，获得了从2004年6月到8月3个月的太湖水体高光谱数据和水质化学分析数据。利用实测的高光谱数据分析计算太湖水体的离水辐亮度和遥感反射比；然后，通过相关分析寻找反演叶绿素a浓度的高光谱敏感波段，进而建立反演太湖水体叶绿素a浓度的高光谱遥感定量估算模型，并用相关数据对模型进行精度分析。研究发现，水体的遥感反射比光谱在719nm和725nm存在两个峰，其中719nm处的峰更明显且稳定。通过模型的对比分析，发现用这两个峰值处的遥感反射比参与建模可以提高叶绿素a的估算精度；并且认为由反射比比值变量R719／R670所建立的线性模型对叶绿素a浓度的估算精度最理想。