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

使用卫星激光雷达检测树下和树内的积雪，以改进气候和天气建模

• 批准号: 2890089
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890089
• 关键词: Detecting; snow; under; within; trees

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.

背景诺是土地表面的最大瞬态特征。它为大部分人口提供饮用水，影响天气，并通过供水来控制植物的生长和野火。积雪范围的地图由一系列卫星产生。这些用于推动天气和水文预测和测试气候模型；随着温度的变化而变化是气候模型灵敏度准确性的关键指标（Mudryk等2020）。当前，这些地图是通过被动遥感生成的。由于地面和植物的能量混合在一起，这些能量往往低估了森林地区的降雪程度，并且无法轻易发现树木内的雪。树木被捕获的雪可以崇高大气，而树下树下的雪从太阳上遮盖，改变了水文学，因此这些过程对于准确的预测很重要（Ly等人，2019年）。新一代LIDAR（Laser Ranging）卫星可以将信号与地面和Canopy和Canopy（Armston等人2013）分开。这有可能在树下降雪并估计树木中持有多少雪（Russell et al 2020）。这些新地图可以使气候和天气预测中的雪的准确性变化。基于非常有限的数据对树木捕获的雪是建模的，因此拥有大规模的地图将允许对雪对天气和水文学的影响进行首次详细测试。 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子镜片雪覆盖。这里的主要错误是地面发现，因此任何新颖的发现都可以用来改善所有其他LiDAR数据产品，包括高度和生物量。这将与地面摄像头和高分辨率卫星图像进行比较。一种准确的方法将允许ICESAT-2数据在地球大面积上绘制亚架雪。可以研究冠层反射率，以确定是否可以使用它来测量树木中持有的雪数量。目前，这是雪建模的未知数，并且可能导致水平平衡中的巨大偏见（Russell等人2021）。任何大规模观察都将有助于改善预测。这可能涉及对受雪的森林（斯堪的纳维亚半岛或北美）的实地考验，利用陆地激光扫描和雪质量测量值来监测降雪并被树木陷入树木中。与被动卫星相比，lidar的时间覆盖稀少，因此ICESAT-2不适合在季节性时间分辨率上测试模型。为此，ICESAT-2数据可用于校准被动的光学和微波卫星，以通过机器学习技术估算子囊性雪，允许在高速分辨率（每月到每天）以高规模的映射（每天到每天）。这些更新的地图可用于测试雪森林中的天气和气候模型，允许在风格的野外型号和水杂种中进行杂种，并允许在水杂种中进行杂种，并进行水杂种。在监督团队的支持下，可以选择最终申请的最终申请。