Mapping of Land-Atmosphere Heat Fluxes and Surface Parameters with Remote Sensing Data

A variational data assimilation scheme is used to infer two key parameters ofthe surface energy balance that control the partitioning of available energy intolatent, sensible, and ground heat fluxes (LE, H, and G). Remotely sensedland surface temperature (LST) is the principal data source. Maps ofdiurnal energy balance components are presented for a basin with varied landcover (Arno Basin, Italy) for a 18-day period in July 1996.Given available energy, the major unknown (dimensionless) parameters requiredfor partitioning among fluxes are: (1) Landscape effects on near-surfaceturbulence as captured by the bulk heat transfer coefficient CBN underneutral conditions and (2) surface control of the relative magnitudes of LEand H as represented by the evaporative fraction EF. The data assimilationscheme merges 1.1-km resolution remotely sensed LST images (based onoptical, thermal and microwave measurements from two different satelliteplatforms) into a parsimonious model of heat diffusion. Both the measurementsand the model predictions are considered uncertain. Posterior error statisticsthat represent uncertainty of the estimated parameters are also derived.Maps of CBN show spatial patterns consistent with the dominant land useand basin physiography. Daily maps of EF exhibit spatial variationscorresponding to land cover and land use – the day-to-day variations inEF show fluctuations consistent with rain events and drydowns experiencedduring the period. Based on these parameters and available environmentalvariables, maps of diurnal LE and H may be produced (in this paper daytimeLE maps are reported).The application demonstrates that remotely sensed land surface temperaturesequences contain significant amount of information of the partitioning ofavailable energy among the fluxes. The variational data assimilation frameworkis shown to be an efficient and parsimonious approach without reliance onempirical relationships such as those based on vegetation indices.

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