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Title:   Author  item
                                            Yang, Yunitem
                                            Anderson, Marthaitem
                                            Gao, Fengitem
                                            HAIN, C. - University Of Marylanditem
                                            Kustas, William

                                                         - *Bill*item
                                            NOORMETS, A. - North Carolina State Universityitem
                                            WYNNE, R. - Virginia State Universityitem
                                            THOMAS, V.A. - Virginia State Universityitem
                                            SUN, G. - Forest Service (FS)
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 9/20/2017
                                    Publication  Date: 1/10/2016
                                    Citation: Yang, Y., Anderson, M.C., Gao, F.N., Hain, C., Kustas, W.P., Noormets, A., Wynne, R., Thomas, V., Sun, G. 2016. Estimating daily Landsat-scale evapotranspiration over a managed pine plantation in North Carolina, USA using a data fusion method. Meeting Abstract. New Orleans, Louisiana, 10th -14th January 2016 CDRDM.Interpretive Summary:
                                Technical Abstract: As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and biological processes and is directly affected by water management, land use change and climate change. The two source energy balance (TSEB) model has been widely applied to quantify field scale ET using satellite data. However, limitations on the spatial and temporal resolution of the satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing multi-satellite data with varying spatial and temporal resolutions can give a more continuous estimation of daily ET at field scale. In this study, we used the regional TSEB modeling system (Atmosphere-Land Exchange Inverse; ALEXI) to map ET at 4-km resolution over the continental U.S. using land-surface temperature maps retrieved from thermal infrared geostationary satellite imagery. These 4-km regional estimates were disaggregated to 1-km using Moderate Resolution Imaging Spectroradiometer (MODIS) input data and down to 30-m with Landsat data using the disaggregation scheme DisALEXI over a managed pine plantation in North Carolina, USA. The MODIS retrieved ET (daily, 1-km) and Landsat retrieved ET (8 scenes during the growing season, 30-m) were combined using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM) to estimate daily ET estimates at 30-m resolution. The modeled daily ET was then compared with observations at two Ameriflux eddy covariance flux tower sites (US-NC2 and US-NC3). The NC2 tower is located in a mid-rotation (20-year old) pine stand and the NC3 tower is located in a recently clear cut and replanted field site. The modeled ET was in good agreement with the observed ET at both sites.. The influence of land cover type and stand age on seasonal water use using the daily Landsat-scale ET dataset was investigated. The analysis showed that seasonal ET patterns vary significantly with stand age, with higher rates of ET from mid-rotation stands in comparison with younger stands. For stands older than approximately 20 years, there was little dependence of water use on stand age. The dataset also showed that pixels located within natural forest and mid-rotation plantations had higher ET than the young plantation and cropped areas. The results demonstrate the capability of using ALEXI/DisALEXI with multi-satellite data to estimate daily field scale ET over managed forest plantations. The method presented in this study provides new insights about the effects of land use change on site hydrological water balance, and has the potential to be used to routinely monitor hydrology at the regional and continental scale using satellite data.


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