Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #309935
Title: Mapping coupled fluxes of carbon and water through multi-sensor data fusion

Author
item Schull, Mitchell
item Anderson, Martha
item Semmens, Kathryn
item Yang, Yun
item HAIN, C. - University Of Maryland
item HOUBORG, RASMUS - King Abdullah University Of Science And Technology

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/22/2014
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: In an effort to improve water resource management, drought monitoring, and agriculture assessment capabilities, a multi-sensor and multi-scale framework for assessing land-surface fluxes of energy and water at field to regional scales has been established. The framework employs the ALEXI (Atmosphere Land Exchange Inverse)/DisALEXI (Disaggregated ALEXI) suite of land-surface models using remotely sensed data. Land-surface temperature (LST) can be useful as a substitute for in-situ surface moisture measurements and an effective 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 water and energy fluxes by using a combination of shortwave reflective and thermal infrared (TIR) imagery from geostationary, high frequency (4-10 km; hourly), medium resolution (1 km; daily), and high spatial resolution, low frequency (30-100 m; bi-weekly) satellites. In this research the ALEXI/DisALEXI modeling suite is modified to estimate coupled carbon and water fluxes using a canopy resistance module, which replaces the original Priestley-Taylor latent heat approximation. In the module, canopy level nominal light-use-efficiency ('n) is the parameter that modulates the flux of water and carbon in and out of the canopy on a seasonal scale. Leaf chlorophyll (Chl) is a key parameter for quantifying variability in photosynthetic efficiency and can be estimated from remotely sensed data. LST conveys information regarding the surface moisture status and controls on transpiration and soil evaporation fluxes. These qualities facilitate the spatial distribution of coupled carbon and water retrievals. Regional maps of Chl are retrieved from high spatial resolution sensors (30 m) using a surface reflectance dataset as input to the REGularized canopy reFLECtance (REGFLEC) tool. The modified ALEXI/DisALEXI suite is applied to regions of irrigated and rain fed maize and soybean agricultural fields within the continental U.S. and modeled flux estimates are compared with flux tower observations. Future improvements to the model can assisted through the implementation hyperspectral sensors such as HyspIRI.