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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #299228
Title: Mapping surface fluxes and moisture conditions from field to global scales using ALEXI/DisALEXI

Author
item Anderson, Martha
item Kustas, William - Bill
item HAIN, C - University Of Maryland
item CAMMALLERI, C - Collaborator
item Gao, Feng
item YILMAZ, M - Collaborator
item Mladenova, Iliana
item OTKIN, J - University Of Wisconsin
item Schull, Mitchell
item HOUBORG, R - Collaborator

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 6/26/2013
Publication Date: 10/28/2013
Citation: Anderson, M.C., Kustas, W.P., Hain, C., Cammalleri, C., Gao, F.N., Yilmaz, M.T., Mladenova, I., Otkin, J., Schull, M.A., Houborg, R. 2013. Mapping surface fluxes and moisture conditions from field to global scales using ALEXI/DisALEXI.In: Petropoulos, G. Remote Sensing of Energy Fluxes and Soil Moisture Content. Boca Raton, FL: CRC Press.

Interpretive Summary:

Technical Abstract: Land-surface temperature (LST) maps derived from thermal infrared (TIR) satellite data convey valuable information for detecting moisture stress conditions and for constraining diagnostic surface flux estimates based on remote sensing. Soil surface and vegetation canopy temperatures rise as available water in the surface layer and root zone is depleted, with thermal stress signals typically preceding significant change in vegetation structure or reduction in biomass. Among surface moisture monitoring methods, thermal remote sensing is unique in its range of achievable spatial resolution – providing information at scales from individual farm fields to continental and global coverage. Prognostic land-surface models (LSMs) also have broad applications in drought monitoring and water resource management. LSMs can provide time-continuous and interconsistent quantitative estimates for a full suite of hydrologic variables. However, they require extensive parameterization and critical physical processes may in some cases be inadquately represented, leading to regional and seasonal errors. Biases can occur due to inaccurate modeling assumptions, observational errors in the forcing data, and a reliance on surface parameter fields that may not be available with the required accuracy or spatial resolution. Because they are principally constrained by the accuracy of the precipitation inputs, LSMs are typically limited in spatial resolution (several kilometers or coarser), and are only moderately portable to regions with sparse ground-based rain gauge networks required for accurate calibration. They are unable to capture surface flux response to changes in land cover conditions (e.g., deforestation), agricultural management practices (e.g., irrigation scheduling, crop rotation), or shallow water tables without significant a priori knowledge of when and where these processes are important. Data assimilation strategies have been developed to integrate in situ and remote sensing data into LSMs to reduce impacts of input biases and model parameterization errors in the prognostic model, while improving temporal characteristics of the diagnostic estimates. This will likely be the optimal solution for future global hydrologic monitoring efforts. In preparation, intercomparisons between prognostic and diagnostic moisture indicators provide insight regarding relative regional and seasonal performance of different modeling systems. In this chapter we describe a multi-scale thermal modeling system for retrieving surface energy fluxes, evapotranspiration (ET) and available soil moisture, fusing information from geostationary and polar orbiting TIR imaging sensors to provide data at both high spatial and temporal resolution. Applications for model output in monitoring drought and crop condition are presented. The value of field-scale remotely sensed water use and availability information in data-sparse regions of the world is emphasized, particularly for improving climate resilience in vulnerable agroecosystems. These remote sensing data can both serve as an independent check that physical processes are adequately represented in prognostic hydrologic and atmospheric modeling systems, and as a means for monitoring land-surface conditions in greater spatial detail than can be captured by mesoscale and global LSMs and forecasting systems.