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

土壤养分和污染物水平的卫星或航空测绘或遥感将对森林和牧场管理、碳预算分配、水质和农业系统产生广泛的好处。使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。该项目的目标是开发新的方法，利用可利用遥感技术绘制的植被的化学和物理特性来确定底层土壤的质量。使用遥感技术量化土壤特性的好处有两个方面：（1）遥感技术独特地提供了管理规模的信息，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，植物物种分布变化，污染，气候变化和疾病。 该项目试图利用国家生态观测网（氖）机载观测平台（AOP）测量的覆盖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米反射率数据，以及光探测和测距（LiDAR）数据。为了建立氖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2018年6月AOP调查成功收集的400多个地点的成对植被、土壤和沉积物样本的样本档案。该数据集跨越超过300平方公里的上东河流域在科罗拉多，包括四个河源流域与可变的地质和地形，包括两个金属影响流域和不同的土地利用方式。一些补充项目也将使用该数据集来表征微生物群落、裸岩矿物学和植物物种分布。在这里，空中反射率数据，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，并在一个大的空间域中推断这些关系。我们的总体目标是开发一种方法来表征土壤碳，养分和金属污染物在空间上明确的方式。最后，在管理规模上建立下一代传感器用于测绘地球化学过程的效用，是从基于航空航天的区域数据集发展到具有全球重复覆盖的卫星任务的关键一步，其中包括美国航天局的高光谱红外成像仪（HypspIRI）使命和德国的环境测绘和分析方案（EnMAP）。 该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NSF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.