Satellite microwave measurement of land surface phenology: Clarifying vegetation phenology response to climatic drivers and extreme events.

摘要

The seasonality of terrestrial vegetation controls feedbacks to the climate system including land-atmosphere water, energy and carbon (CO 2) exchanges with cascading effects on regional-to-global weather and circulation patterns. Proper characterization of vegetation phenology is necessary to understand and quantify changes in the earth's ecosystems and biogeochemical cycles and is a key component in tracking ecological species response to climate change. The response of both functional and structural vegetation phenology to climatic drivers on a global scale is still poorly understood however, which has hindered the development of robust vegetation phenology models. In this dissertation I use satellite microwave vegetation optical depth (VOD) in conjunction with an array of satellite measures, Global Positioning System (GPS) reflectometry, field observations and flux tower data to 1) clarify vegetation phenology response to water, temperature and solar irradiance constraints, 2) demonstrate the asynchrony between changes in vegetation water content and biomass and changes in greenness and leaf area in relation to land cover type and climate constraints, 3) provide enhanced assessment of seasonal recovery of vegetation biomass following wildfire and 4) present a method to more accurately model tropical vegetation phenology. This research will establish VOD as a useful and informative parameter for regional-to-global vegetation phenology modeling, more accurately define the drivers of both structural and functional vegetation phenology, and help minimize errors in phenology simulations within earth system models. This dissertation also includes the development of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) vegetation health climate indicators as part of a NASA funded project entitled "Development and Testing of Potential Indicators for the National Climate Assessment; Translating EOS datasets into National Ecosystem Biophysical Indicators".