并服务于数据使用者。概括介绍了北京师范大学牵头研发的GLASS(Global LAnd Surface Satellite)产品的新进展与全球气候数据集的研发情况。
Abstract
With the availability of the increased amount of remotely sensed data
quantitative remote sensing is in a period of rapid development. This paper reviews the recent development of the quantitative remote sensing of land surface from the two main aspects: inversion methodology and generation of the remote sensing data products. Because the number of environment variables in the atmosphere and land surface system is much larger than that of remote sensing observations
the nature of remote sensing inversion is an ill posed inversion problem. After reviewing the machine learning methods(e.g. artificial neural network
support vector regression
multivariate adaptive regression splines) and their applications
we mainly focus on seven regularization methods for overcoming the ill posed inversion problem: using multi-source data
a prior knowledge
constrained optimization
spatial and temporal constraints
integration of multiple inversion algorithms
data assimilation
and scaling. Another significant feature of the quantitative remote sensing development is satellite observations are transformed into different geophysical and geochemical parameters
namely remote sensing high-level products
for the user community by the data providers(e.g.
data acenters). This paper mainly introduces the latest development of the Global LAnd Surface Satellite(GLASS)products produced by Beijing Normal University
and the research and the development of the Climate Data Record for climate studies.
关键词
定量遥感反演正则化机器学习GLASS产品气候数据集
Keywords
quantitative remote sensinginversionregularizationmachine learning methodsGLASS productsclimate data records