S3-GEP: Scalable Spatiotemporal Statistics for Global Environmental Phenomena

凭借当今大量的开放地球观测（EO）数据，可以在全球范围内对环境现象进行全面的地质统计分析。然而，随机场运算的计算复杂性，全局不切实际的模型假设，如平稳性，可分性和各向同性，以及数据管理问题，当数据大小超过本地存储容量时，目前限制了数据在应用程序中的实际使用，并导致不可共享，不可再现的研究。作为一个例子，使用全球海拔数据在几个TB的顺序作为协变量信息在建模降水的空间变化需要快速的方法来推断统计和数据管理的广泛努力。在这个项目中，我们的目标是开发对全球环境现象进行地统计推断的有效方法，这些方法（i）在无共享架构上具有良好的计算规模，（ii）考虑非平稳，不可分离和各向异性的时空依赖关系，（iii）能够整合多个数据源，包括遥感图像和现场观测。因此，我们将建立地质统计模型，结合联合收割机空间马尔可夫随机场与时间对流扩散过程和适应新的算法，高度可扩展的数据管理和分析系统SciDB。开发的方法将在两个用例中进行演示，包括创建全球高分辨率降水数据集和使用外部独立变量对土地利用变化进行建模。除了全球建模之外，第二个用例将强调项目结果如何促进使用最新的高分辨率卫星数据集。为此，哨兵-2和TanDEM-X任务的数据将用于国家规模的土地使用变化分析。预期的主要贡献包括在分布式计算环境中进行有效地质统计推断的算法，在全球范围内对非平稳性和各向异性进行建模的方法，以及用于可重复的大规模环境数据管理和分析的开源软件工具。

With today's amount of open Earth observation (EO) data, comprehensive geostatistical analyses of environmental phenomena can be brought to global scale. However, computational complexity of random field operations, globally unrealistic model assumptions like stationarity, separability, and isotropy, and data management issues when data size exceeds local storage capacity currently limit the practical use of the data in applications and result in unshareable, irreproducible research. As an example, using global elevation data in the order of a few terabytes as covariate information in modeling the spatial variation of precipitation requires fast methods to inferential statistics and extensive effort in data management. In this project, we aim at developing efficient methods for geostatistical inference on global environmental phenomena that (i) computationally scale well on shared nothing architectures, (ii) consider non-stationary, non-separable, and anisotropic spatiotemporal dependencies, and (iii) are capable of integrating multiple data sources including remote sensing imagery and in-situ observations. Therefore, we will build geostatistical models that combine spatial Markov random fields with temporal advection-diffusion processes and adapt novel algorithms to the highly scalable data management and analytics system SciDB. Developed methods will be demonstrated in two use cases including the creation of a global high resolution precipitation dataset and modeling land use change with external independent variables. In addition to global modeling, the second use case will emphasize how project results facilitate working with latest, high-resolution satellite-derived datasets. For this, data from the Sentinel-2 and TanDEM-X missions will be used in a national scale land use change analysis. Expected key contributions include algorithms for efficient geostatistical inference in distributed computing environments, approaches for modeling non-stationarity and anisotropy on global scale, and open source software tools for reproducible large-scale environmental data management and analyses.