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

GSTDTAP

项目编号	1841547
	EAGER SitS: Can remotely imaged vegetation characteristics provide a window into soil nutrient cycles?
	Katharine Maher
主持机构	Stanford University
项目开始年	2018
	2018-09-01
项目结束日期	2020-08-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	299820(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/73284
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Katharine Maher.EAGER SitS: Can remotely imaged vegetation characteristics provide a window into soil nutrient cycles?.2018.