Detecting snow under and within trees with satellite lidar for improved climate and weather modelling

背景雪是陆地表面最大的瞬态特征。它为很大一部分人口提供饮用水，影响天气，并通过水的可用性控制植物生长和野火。积雪范围的地图是由一系列卫星制作的。这些被用来驱动天气和水文预报，并测试气候模型;随着温度变化的积雪范围是气候模型灵敏度准确性的关键指标（Mudryk等人，2020年）。目前，这些地图是由被动遥感生成的。由于来自地面和植物的能量混合，这些往往会低估森林地区的积雪程度，并且无法轻易检测到树木中的积雪。被树木捕获的雪可以升华到大气中，而树下的雪则被太阳遮蔽，改变水文，因此这些过程对于准确预报非常重要（Ly等人，2019）。新一代激光雷达（激光测距）卫星可以分离出来自地面和树冠的信号（Armston等人，2013）。这有可能绘制树下的雪，并估计树内有多少雪（Russell et al 2020）。这些新地图可以允许气候和天气预测中雪的准确性发生阶跃变化。树木中的积雪是基于非常有限的数据建模的，因此拥有大比例尺的地图将允许第一次详细测试树木中的积雪对天气和水文的影响。精确的树下积雪地图将允许对天气模型进行大规模测试，这是目前天气和气候预报模型中的一个很大的不确定性。（与NASA ICESat-2植被产品负责人合作），第一步是确定卫星激光雷达是否能够足够准确地测量地面反射率，以预测树冠下的积雪。这里的主要错误是地面发现，因此任何新的发现都可以用于改进所有其他激光雷达数据产品，包括高度和生物量。这将与地面相机和高分辨率卫星图像进行比较。一种精确的方法将使ICESat-2的数据能够绘制地球大面积地区的次冠层积雪图，可以对冠层反射率进行调查，以确定是否可以用它来测量树木内的积雪量。这是目前雪模型中未知的，可能会导致水平衡的大偏差（Russell等人，2021年）。任何大规模的观测都将有助于改善预报。这可能涉及到对受雪影响的森林（斯堪的纳维亚或北美）进行实地考察，利用地面激光扫描和雪量测量来监测降雪和被树木捕获的雪。与被动卫星相比，激光雷达的时间覆盖范围较窄，因此ICEsat-2不适合在季节性时间分辨率下测试模型。为实现这一目标，ICESat-2数据可用于校准无源光学和微波卫星，以通过机器学习技术估计冠层下积雪，从而能够以高时间分辨率（每月到每日）进行大规模测绘，这些更新的地图可用于测试受积雪影响的森林的天气和气候模型，从而能够应用于气候模型、水文预报和野火估计。最终申请的选择可以由博士生在监督团队的支持下决定。

BackgroundSnow is the largest transient feature of the land surface. It provides drinking water to a significant fraction of the population, affects the weather, and controls plant growth and wildfires fire through water availability. Maps of snow extent are produced by a range of satellites. These are used to drive weather and hydrological forecasting and to test climate models; changing snow extent with temperature is a key metric of the accuracy of climate models' sensitivity (Mudryk et al 2020). Currently these maps are generated by passive remote sensing. Due to the mixing of energy from the ground and plants, these tend to underestimate the extent of snow in forested areas and cannot easily detect snow within trees. Snow that is caught in trees can sublime into the atmosphere, whilst snow under trees is shaded from the sun, changing the hydrology and so these processes are important for accurate forecasts (Ly et al 2019).A new generation of lidar (laser ranging) satellites can separate out signals from the ground and canopy (Armston et al 2013). This holds the potential to map snow under trees and to estimate how much is held within trees (Russell et al 2020). These new maps could allow step changes in the accuracy of snow in climate and weather predictions. The snow caught in trees is modelled based on very limited data, so having large-scale maps would allow the first detailed test of the impact of snow in trees on weather and hydrology. Accurate maps of snow under trees would allow large scale testing of weather models, which is currently a large uncertainty in weather and climate forecast models.Experimental planBuilding on work to determine ground and vegetation canopy reflectance from NASA's ICEsat-2 and GEDI satellite lidars (working with the NASA ICESat-2 vegetation product lead), the first step would be to determine whether the satellite lidars can measure ground reflectance accurately enough to predict sub-canopy snow cover. The primary error here is ground finding, and so any novel findings could be used to improve all other lidar data products, including height and biomass. This will be compared against ground cameras and high-resolution satellite images. An accurate method will allow ICESat-2 data to map sub-canopy snow over large areas of the Earth.The canopy reflectance can be investigated to determine whether it can be used to measure the amount of snow held within trees. This is currently an unknown in snow modelling and may be causing large biases in the water balance (Russell et al 2021). Any large-scale observations would help improve forecasts. This can involve fieldwork to snow affected forests (Scandinavia or North America), making use of terrestrial laser scanning and snow mass measurements to monitor snow falling and being caught within trees.Lidar has sparse temporal coverage compared to passive satellites and so ICEsat-2 will not be suitable for testing models at seasonal temporal resolutions. To achieve that, ICESat-2 data can be used to calibrate passive optical and microwave satellites to estimate sub-canopy snow through machine learning techniques, allowing large-scale mapping at high-temporal resolution (monthly to daily).These updated maps can be used to test weather and climate models in snow-affected forests, allowing applications in climate models, hydrological forecasting and wildfire estimation. The choice of which final applications to pursue can be determined by the PhD student, with the support of the supervision team.