EAGER SitS: Can remotely imaged vegetation characteristics provide a window into soil nutrient cycles?

卫星或航空测绘或遥感土壤养分和污染物水平将对森林和牧场管理、碳预算分配、水质和农业系统有广泛的好处。利用遥感推断土壤质量的主要挑战是，大多数土壤被植被覆盖，这使土壤无法直接观测。这个项目的目标是发展利用植被的化学和物理特性的新方法，这些特性可以利用遥感技术来确定下面土壤的质量。使用遥感技术量化土壤特性的好处有两方面：(1)遥感技术独特地提供管理规模的信息，例如流域或国家森林；(2)允许重复并最终实时检测不断变化的环境条件，包括干旱、养分限制、不断变化的植物物种分布、污染、气候变化和疾病。该项目旨在通过国家生态观测站网络（NEON）机载观测平台（AOP）测量的上覆植被的反射率来解码土壤中的化学信号。AOP使用可见光到短波红外（VSWIR）传感器收集1米反射数据，以及光探测和测距（LiDAR）数据。为了建立NEON成像光谱数据与底层土壤特征之间的关系，研究团队将分析来自400多个地点的成对植被、土壤和沉积物样本档案，这些样本是在2018年6月与AOP调查一起成功收集的。该数据集覆盖了科罗拉多州上东河流域300多平方公里，包括四个地质和地形各异的水源集水区，包括两个受金属影响的流域和不同的土地利用实践。一些补充项目也将使用该数据集来描述微生物群落、裸岩矿物学和植物物种分布。在这里，机载反射率数据，当与地面采样活动配对时，将用于测试植被和土壤特性之间的关系，并在大的空间域内推断这些关系。我们的首要目标是开发一种以空间明确的方式表征土壤碳、养分和金属污染物的方法。最终，在管理规模上建立用于绘制生物地球化学过程的下一代传感器的实用性，是从基于机载的区域数据集向具有全球重复覆盖的卫星任务发展的关键一步，包括美国宇航局的高光谱红外成像仪（HyspIRI）任务和德国的环境测绘和分析计划（EnMAP）。本项目由地球科学部和环境生物学部生态系统科学组共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Satellite or airborne mapping, or remote sensing, of soil nutrient and contaminant levels would have wide benefits for forest and rangeland management, carbon budget allocation, water quality, and agricultural systems. The central challenge in the use of remote sensing to infer soil quality is that most soils are covered by vegetation, which shields the soil from direct observation. The goal of this project is to develop new methods that use the chemical and physical characteristics of vegetation that can be mapped using remote sensing techniques to determine the quality of underlying soil. The benefit of using remote sensing techniques to quantify soil properties is two-fold: (1) remote sensing techniques uniquely provide information at the scale of management, for example a watershed or national forest, and (2) allow for repeat and ultimately real-time detection of changing environmental conditions, including drought, nutrient limitation, changing plant species distribution, contamination, climatic change, and disease. This project seeks to decode the chemical signals in soils using the reflectance of the overlying vegetation, as measured by the National Ecological Observatory Network's (NEON) Airborne Observation Platform (AOP). The AOP collects 1 m reflectance data using a visible to shortwave infrared (VSWIR) sensor, as well as Light Detection and Ranging (LiDAR) data. To build a relationship between NEON imaging spectroscopy data and the underlying soil characteristics, the research team will analyze a sample archive of paired vegetation and soil and sediment samples from over 400 sites successfully collected in conjunction with the AOP survey in June of 2018. The dataset spans more than 300 km2 in the Upper East River watershed in Colorado, encompassing four headwater catchments with variable geology and topography, including two metals-impacted watersheds and diverse land-use practices. A number of complementary projects will also use the dataset to characterize microbial communities, bare rock mineralogy, and plant species distributions. Here, the airborne reflectance data, when paired with the ground sampling campaign, will be used to test for relationships between vegetation and soil properties and to extrapolate these relationships across a large spatial domain. Our overarching objective is to develop an approach to characterize soil carbon, nutrients and metal contaminants in a spatially explicit way. Ultimately, establishing the utility of next-generation sensors for mapping biogeochemical processes, at the scale of management, is a critical step in the evolution from airborne-based regional datasets to satellite missions with global, repeat coverage, including NASA's Hyperspectral Infrared Imager(HyspIRI) mission and Germany's Environmental Mapping and Analysis Programme (EnMAP). This project is jointly funded by the Division of Earth Sciences and the Ecosystem Science Cluster in the Division of Environmental Biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.