Net primary productivity(NPP) and evapotranspiration(ET) are two key variables in the carbon and water cycles of terrestrial ecosystems.In this study,to test a newly developed NPP algorithm designed for HJ-1 A/B data and to evaluate the usage of HJ-1 A/B data in the quantitative assessment of environments,NPP and ET in Jinggangshan city,Jiangxi province,are calculated using HJ-1 A/B data.The results illustrate the following:(1) The NPP and ET in Jinggangshan city in 2010 both show obvious seasonal variation,with the highest values in summer and the lowest values in winter,and relatively higher values were observed in autumn than in spring.(2) The spatial pattern indicates that the annual NPP is high in the southern area in Jinggangshan city and low in the northern area.Additionally,high NPP is distributed in forests located in areas with high elevation,and low NPP is found in croplands at low elevations.ET has no significant north-south difference,with high values in the southeast and northwest and low values in the southwest,and high ET is distributed in forests at low elevations in contrast to low ET in forests in high-elevation areas and in cropland and shrub grassland in low-elevation areas.(3) Compared to the MODIS product,the range of HJ-1 NPP is larger,and the spatial pattern is more coincident with the topography.The range of HJ-1 ET is smaller than that of the MODIS product,and ET is underestimated to some extent but can reflect the effect of topography.This study suggests that the algorithm can be used to estimate NPP and ET in a subtropical monsoon climate if remotely sensed images with high spatial resolution are available.
Land surface process modeling of high and cold area with vegetation cover has not yielded satisfactory results in previous applications. In this study, land surface energy budget is simulated using a land surface model for the A'rou meadow in the upper-reach area of the Heihe River Basin in the eastern Tibetan Plateau. The model performance is evaluated using the in-situ observations and remotely sensed data. Sensible and soil heat fluxes are overestimated while latent heat flux is underestimated when the default parameter setting is used. By analyzing physical and physiological processes and the sensitivities of key parameters, the inappropriate default setting of optimum growth and inhibition temperatures is identified as an important reason for the bias. The average daytime temperature during the period of fastest vegetation growth(June and July) is adopted as the optimum growth temperature, and the inhibition temperatures were adjusted using the same increment as the optimum temperature based on the temperature acclimation. These adjustments significantly reduced the biases in sensible, latent, and soil heat fluxes.
