Sequential Bayesian inference for spatio-temporal probabilistic models of changes in global vegetation and ocean properties using Earth Observation da

由于人类活动和气候变化，地球的植被正在发生变化。植被的大规模变化将从根本上改变陆地生态系统，带来一系列潜在后果--从对生物多样性的影响到碳和水文循环的改变。在北部高纬度地区，随着气候变暖，植物生长得更多，导致陆地表面变绿。在接下来的50年里，冻土带生物群预计将变得适合树木生长，北方林线已经向北迁移，冻土带木本灌木的丰度正在增加。这些变化将对生态系统功能和气候反馈产生深远影响；虽然通过光合作用从大气中吸收的二氧化碳可能会增加，但更高、更密的植物树冠将降低陆地表面的反射率，导致更大的变暖。为了理解植被分布变化的影响，我们必须对空间中重要的生物物理参数随时间变化进行建模。同样，洋流在短期和长期尺度上也不同，很难将差异归因于较长期的趋势，例如气候变化。众所周知，海洋在气候变化中起着核心作用，随着它们从大气中吸收大量热量，海洋正在迅速变化，但在目前的条件下，这到底是如何发挥作用的往往是不清楚的。在这个项目中，我们专注于开发概率时空模型的推理工具，并将其应用于地球观测问题。我们考虑了从随时间获取的遥感(卫星)观测中估计生物物理参数的挑战性问题。作为一个例子，让我们集中在上述问题上，其中演变叶面积指数(LAI)的估计是预测地球植被变化的关键。由于叶面积指数在光合作用和蒸腾等植被过程中起着重要作用，并与气象/气候和生态陆地过程有关，因此跟踪地球上每个空间位置的叶面积指数随时间的演变非常重要。我们还通过考虑复杂的动力学模型来考虑海洋学应用，这些模型需要复杂的推理工具来学习演变状态的概率估计以及模型的未知参数。我们将提出新的计算方法，以克服当前更传统的基于IS的技术在这种具有挑战性的环境中的局限性，包括用于在高维空间学习静态参数的自适应IS方法[6]和将[7]扩展到具有大量数据的观测空间。这些方法的发展可以使对地观测中的许多应用受益。关于最新IS方法学进展在遥感问题中的应用，见[5]和[8]。

The Earth's vegetation is changing as a result of both human activity and climate change. Large scale shifts in vegetation will fundamentally alter terrestrial ecosystems, with a range of potential consequences - from impacts on biodiversity to altered carbon and hydrological cycling. In northern high latitudes plants are growing more as the climate warms, resulting in a "greening" of the land surface. Within the next 50 years the tundra biome is expected to become climatically suitable for trees, the boreal treeline is already shifting northwards and woody shrub abundance in tundra is increasing. These changes will have a profound impact on ecosystem function and climate feedbacks; while CO2 uptake from the atmosphere through photosynthesis is likely to increase, taller denser plant canopies will decrease the reflectivity of the land surface, resulting in greater warming. To understand the implications of changing vegetation distributions, it is vital we can model important biophysical parameters from space over time.Similarly, ocean currents also vary on both short and long time scales, with attributing differences to longer-term trends, for example climate change, being difficult. It is known that the oceans play a central role in climate change, and are changing rapidly as they absorb large amounts of heat from the atmosphere, but it is often unclear how exactly that is playing out in current conditions.In this project, we focus in developing inferential tools for probabilistic spatio-temporal models with applications in earth observation problems. We consider the challenging problem of estimating biophysical parameters from remote sensing (satellite) observations acquired across time. Just as an example, let us focus in the aforementioned problem where the estimation of the evolving Leaf Area Index (LAI) is key for forecasting the change of Earth's vegetation. It is important to track evolution of LAI through time in every spatial position on Earth because LAI plays an important role in vegetation processes such as photosynthesis and transpiration, and is connected to meteorological/climate and ecological land processes [4, 5]. We also consider oceanography applications by considering complex dynamical models that require sophisticated inferential tools for learning the probabilistic estimates of the evolving states and also the unknown parameters of the model. We will propose novel computational methods in order to overcome current limitations of more traditional IS-based techniques in such a challenging context, including adaptive IS methods for learning static parameters in high dimensional spaces [6] and extensions of [7] to observational spaces with big amount of data. Many applications in earth observation can be benefited from the development of these methodologies. See [5] and [8] for the application of recent IS methodological advances in remote sensing problems.