Use of Light-Use Efficiency and Land-Surface Temperature As Joint Constraints in Water, Energy and Carbon Flux Retrievals.

Dr. John Monteith pioneered the use of light-use efficiency (LUE) as a simple and pragmatic method for studying the impacts of climate on crop growth.  Defined as the ratio of carbon uptake and radiation absorbed by vegetation, LUE has become an integral component of many systems modeling carbon fluxes and crop development.   In other work, Dr. Monteith explored land-surface temperature (LST) as a valuable constraint for estimating the energy balance over vegetated surfaces.  In this presentation we discuss a framework for routine mapping of coupled land-surface fluxes of carbon, water, and energy at the field to regional scales using both LUE and LST as joint constraints in remote sensing retrievals.  This multi-scale 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).  Within the modeling system, thermal infrared (TIR) satellite data provide information about the sub-surface moisture status and plant stress, while nominal LUE estimates retrieved using shortwave remote sensing data impose additional constraints on the effective canopy conductance that regulates both transpiration and carbon assimilation fluxes.  Output from the thermal-based modeling system, as applied to regions of rain fed and irrigated soy and corn agricultural landscapes within the continental U.S., are compared to flux tower observations and model performance under different climatic and crop phenological conditions is discussed.   To facilitate daily mapping of surface fluxes at sub-field scale, we describe a technique for fusing daily assessments at 1-km resolution using MODIS data with periodic high resolution (30 m) maps retrieved with Landsat shortwave and TIR imagery.  Robust carbon uptake and water use assessments at these fine spatiotemporal scales will have benefit for a wide range of agricultural management activities.