Author

Mingzhu He

Year of Award

2018

Document Type

Dissertation - Campus Access Only

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Systems Ecology

Department or School/College

Department of Ecosystem and Conservation Sciences

Committee Chair

John S. Kimball

Commitee Members

Steven W. Running, Anna Sala, Ashley Ballantyne, Marco Maneta

Keywords

Agriculture, Evapotranspiration, Gross primary production, Landsat, Light use efficiency, MODIS

Publisher

University of Montana

Abstract

Accurate estimations of terrestrial carbon uptake (Gross Primary Production, GPP) and water loss (Evapotranspiration, ET) are crucial for understanding the response of ecosystems to climate change and to various natural and human-induced disturbances. Satellite remote sensing offers unique opportunities for regional to global GPP and ET assessments by providing spatially and temporally continuous and consistent observations of vegetation and ecosystem properties. However, the current GPP and ET estimates contain large uncertainties, resulting in limited understanding of terrestrial ecosystem responses to climate variability. Satellite-based GPP and ET models, such as the MOD17 algorithm, Terrestrial Carbon Flux model (TCF) and MOD16 ET algorithm, have shown capabilities for regional to global GPP and ET simulations. However, the predefined parameters and inappropriate coarse scale input data limit the model performance at fine scale. This research addresses the current uncertainties in remote sensing-based GPP and ET models by integrated use of overlapping satellite observations, ancillary geophysical data, and in situ measurements from flux tower sites. The results show improvements for regional GPP, crop yield and cropland ET assessments encompassing different vegetation types and climate variability against baseline results from the original GPP and ET global models. This research provides new understanding and effective tools for improving regional GPP and ET monitoring from global satellites. This research also improves understanding of the response of regional vegetation productivity and water use to climate variability, promoting more effective agricultural water management, policy decisions and food security.

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