An operational method to estimate evapotranspiration using MODIS data during winter wheat growing season

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan, sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The Penman–Monteith (P-M) model has been widely used to estimate actual evapo-transpiration (ET). However, its application is mainly constrained to within field scales because the surface resistance under water stress at large scales is difficult to define. The Normalized Difference Water Index (NDWI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data was shown to be sensitive to the crop water content and water deficit, and used to estimate the surface resistance in the P-M model. The modelled latent heat fluxes matched well with the eddy correlation observations, and the spatial distributions also showed a similar pattern as the results from the one-layer model in an irrigated area at the downstream of Yellow River. To reduce the influence of cloud and other atmospheric disturbances, the daily surface resistance was retrieved from 8-day temporal composite MODIS NDWI. The modelled daily ET showed consistent temporal changes with the observations during the wheat growing season. This method showed advantages over the other remote sensing models, for example, the one-layer model, which required daily radiative temperature inputs and cannot be implemented under cloudy conditions.