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Based on aquatic optics Monte Carlo hyperspectral simulation, the interactions between spectral characteristics of chlorophyll a, total suspended matter (TSM) and colored dissolved organic matter (CDOM) were discussed. The result shows that the nonlinear and spectrally varying interactions between different water components are extremely highly asymmetric. The existing of chlorophyll a and CDOM has little effects on the characteristic wavebands selection and information retrieval of TSM, while the effects of TSM on chlorophyll a are obvious. With the stepwise additions of TSM, the response of chlorophyll a to its concentration becomes weak. When the concentration of TSM increases to a certain degree, the spectral response of chlorophyll a concentration will disappear. Even at the sensitivity waveband of chlorophyll a such as 670 nm, when the TSM is in high concentration, the spectral reflectance will not change with chlorophyll a concentration, which lead to difficulty to extract the chlorophyll a concentration in turbid water dominated by suspended matter. The existing of CDOM causes the blue and green band ratio algorithm to fail when the chlorophyll a is in middle to high concentration. The spectral effects on CDOM of the water body dominated by TSM are more obvious than that dominated by chlorophyll a. There are strong inhibition effects of TSM on the CDOM spectral properties in the short bands. The research results can provide theoretical basis for characteristic waveband selection, the application scope of water component concentration inversion algorithm and the waveband setting for case 2 water remote sensing.

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

A new estimation model of vegetation net primary production (NPP) based on remote sensing data and climatic data was presented, with which, the NPP of China terrestrial vegetation in 1982-2000 was estimated, and the intra- and inter- annual variation patterns of the NPP and its responses to climate factors were studied. The results showed that there was an obvious seasonal regularity in the intra-annual variation of the NPP. In 1982-2000, all the terrestrial vegetation types presented an increasing annual NPP, with the greatest increment for deciduous needle leaf forests and the smallest one for grasses. Evergreen broadleaf forests had the largest inter-annual variation, while grasses had the smallest one. Comparing with temperature, precipitation played a stronger driving role in the intra-annual variation of the NPP, and the effects of precipitation and temperature were more obvious in North China than in South China. The driving roles of the climate factors varied with season and latitude.