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Mapping land-surface fluxes of carbon, water and energy from field to regional scales
Mitchell
Schull, USDA-ARS Hydrology & Remote Sensing Lab, mitchell.schull@ars.usda.gov
(Presenter)
Martha
Anderson, USDA-ARS Hydrology & Remote Sensing Lab, martha.anderson@ars.usda.gov
Bill
Kustas, USDA-ARS Hydrology & Remote Sensing Lab, bill.kustas@ars.usda.gov
Carmelo
Cammalleri, USDA-ARS Hydrology & Remote Sensing Lab, carmelo.cammalleri@ars.usda.gov
Feng
Gao, USDA-ARS Hydrology & Remote Sensing Lab, feng.gao@ars.usda.gov
Rasmus
Houborg, KAUST, Environmental Sciences and Engineering Division, Saudi Arabia, rasmus.houborg@kaust.edu.sa
A multi-scale and multi-sensor framework for routine mapping of land-surface fluxes of carbon, water, and energy at the field to regional scales has been established in an effort to improve drought monitoring, water resource management, and agricultural monitoring capabilities. The framework uses the ALEXI/DisALEXI suite of land-surface models in conjunction with remotely sensed data from Landsat, MODIS (MODerate resolution Imaging Spectroradiometer), and GOES (Geostationary Operational Environmental Satellite). In order to obtain high resolution in both space and time, we employ the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to obtain time-continuous datasets of land-surface fluxes at Landsat spatial resolution using Landsat (30 m) high spatial and MODIS high temporal resolutions. Thermal infrared (TIR) data provides valuable information about the sub-surface moisture status, and land-surface temperature can be an effective substitute for in-situ surface moisture observations and a valuable metric for constraining land-surface fluxes at sub-field scales. The adopted multi-scale thermal-based land surface modeling framework facilitates regional to local downscaling of carbon, water and energy fluxes by using a combination of shortwave reflective and TIR imagery from GOES, MODIS and Landsat. In addition biophysical vegetation properties are retrieved at 30 m resolution using a surface reflectance dataset as input to the REGularized canopy reFLECtance (REGFLEC) tool. REGFLEC facilitates retrievals of leaf chlorophyll (Cab), a biophysical parameter that has been recognized as a key parameter to quantify variability in photosynthetic efficiency. Cab is used here to estimate spatio-temporal variations in nominal light-use-efficiency (LUEn), a fundamental parameter that modulates the fluxes of carbon and water in the land-surface model. The integrated thermal-based modeling system has been applied to regions of rain fed and irrigated soy and corn agricultural landscapes within the continental U.S. and flux simulations have been compared with flux tower observations.
Presentation:
2013_Poster_Schull_75_13.pdf (6569k)
Presentation Type: Poster
Session: Poster Session 2-A
(Wed 11:00 AM)
Associated Project(s):
Poster Location ID: 75
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