Agricultural and Forest Meteorology

Because of China's large size, satellite observations are necessary for estimation of the land surface latent heat flux (LE). We describe here a satellite-driven Priestley–Taylor (PT)-based algorithm constrained by the Normalized Difference Vegetation Index (NDVI) and Apparent Thermal Inertia (ATI) derived from temperature change over time. We compare to the satellite-driven PT-based approach, PT-JPL, and validate both models using data collected from 16 eddy covariance flux towers in China. Like PT-JPL, our proposed algorithm avoids the computational complexities of aerodynamic resistance parameters. We run the algorithms with monthly Moderate Resolution Imaging Spectroradiometer (MODIS) products (0.05° resolution), including albedo, Land Surface Temperature (LST), surface emissivity, and NDVI; and, Insolation from the Japan Aerospace Exploration Agency (JAXA). We find good agreement between our estimates of monthly LE and field-measured LE, with respective Root Mean Square Error (RMSE) and bias differences of 12.5 W m−2 and −6.4 W m−2. As compared with PT-JPL, our proposed algorithm has higher correlations with ground-measurements. Between 2001 and 2010, LE shows generally negative trends in most regions of China, though positive LE trends occur over 39% of the region, primarily in Northeast, North and South China. Our results indicate that the variations of terrestrial LE are responding to large-scale droughts and afforestation caused by human activity with direct links to terrestrial energy exchange, both spatially and temporally.