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The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

In order to make the prediction of land surface heat fluxes more robust, two improvements were made to an operational two-layer model proposed previously by Zhang. These improvements are: 1) a surface energy balance method is used to determine the theoretical boundary lines (namely ‘true wet/cool edge’ and ‘true dry/warm edge’ in the trapezoid) in the scatter plot for the surface temperature versus the fractional vegetation cover in mixed pixels; 2) a new assumption that the slope of the Tm – f curves is mainly controlled by soil water content is introduced. The variables required by the improved method include near surface vapor pressure, air temperature, surface resistance, aerodynamic resistance, fractional vegetation cover, surface temperature and net radiation. The model predictions from the improved model were assessed in this study by in situ measurements, which show that the total latent heat flux from the soil and vegetation are in close agreement with the in situ measurement with an RMSE (Root Mean Square Error) ranging from 30 w/m2~50 w/m2,which is consistent with the site scale measurement of latent heat flux. Because soil evaporation and vegetation transpiration are not measured separately from the field site, in situ measured CO2 flux is used to examine the modeled λEveg. Similar trends of seasonal variations of vegetation were found for the canopy transpiration retrievals and in situ CO2 flux measurements. The above differences are mainly caused by 1) the scale disparity between the field measurement and the MODIS observation; 2) the non-closure problem of the surface energy balance from the surface fluxes observations themselves. The improved method was successfully used to predict the component surface heat fluxes from the soil and vegetation and it provides a promising approach to study the canopy transpiration and the soil evaporation quantitatively during the rapid growing season of winter wheat in northern China.

In order to make the prediction of land surface heat fluxes more robust, two improvements were made to an operational two-layer model proposed previously by Zhang. These improvements are: 1) a surface energy balance method is used to determine the theoretical boundary lines (namely ‘true wet/cool edge’ and ‘true dry/warm edge’ in the trapezoid) in the scatter plot for the surface temperature versus the fractional vegetation cover in mixed pixels; 2) a new assumption that the slope of the Tm – f curves is mainly controlled by soil water content is introduced. The variables required by the improved method include near surface vapor pressure, air temperature, surface resistance, aerodynamic resistance, fractional vegetation cover, surface temperature and net radiation. The model predictions from the improved model were assessed in this study by in situ measurements, which show that the total latent heat flux from the soil and vegetation are in close agreement with the in situ measurement with an RMSE (Root Mean Square Error) ranging from 30 w/m2~50 w/m2,which is consistent with the site scale measurement of latent heat flux. Because soil evaporation and vegetation transpiration are not measured separately from the field site, in situ measured CO2 flux is used to examine the modeled λEveg. Similar trends of seasonal variations of vegetation were found for the canopy transpiration retrievals and in situ CO2 flux measurements. The above differences are mainly caused by 1) the scale disparity between the field measurement and the MODIS observation; 2) the non-closure problem of the surface energy balance from the surface fluxes observations themselves. The improved method was successfully used to predict the component surface heat fluxes from the soil and vegetation and it provides a promising approach to study the canopy transpiration and the soil evaporation quantitatively during the rapid growing season of winter wheat in northern China.

We have measured the water quality and bio-optical parameters of 94 samples from Taihu Lake in situ and/or in the lab between June 10-18, 2007. A transparencyassisted decision tree was developed to more accurately divide the aquatic vegetation zone into a floating vegetation-dominated zone and a submerged vegetation-dominated zone, whose respective present biomass retrieval models were easily developed with an empirical approach because of the quasi-concurrence of ground field investigations with the satellite sensor flight over the lake. The significant quantitative relationships between the vegetation index NDVI (Normalized Difference Vegetation Index) of different images at different times were used to help develop the past biomass retrieval model on the basis of the present developed model. In Taihu Lake, the total covering area of aquatic vegetations decreased from 454.6 km2 in 2001 to 364.1 km2 in 2007. Correspondingly, the total biomass decreased from 489,000 tons in 2001 to 406,000 tons in 2007, suggesting that a great change in the ecological environment has been taking place in Taihu Lake over this period.

We have measured the water quality and bio-optical parameters of 94 samples from Taihu Lake in situ and/or in the lab between June 10-18, 2007. A transparencyassisted decision tree was developed to more accurately divide the aquatic vegetation zone into a floating vegetation-dominated zone and a submerged vegetation-dominated zone, whose respective present biomass retrieval models were easily developed with an empirical approach because of the quasi-concurrence of ground field investigations with the satellite sensor flight over the lake. The significant quantitative relationships between the vegetation index NDVI (Normalized Difference Vegetation Index) of different images at different times were used to help develop the past biomass retrieval model on the basis of the present developed model. In Taihu Lake, the total covering area of aquatic vegetations decreased from 454.6 km2 in 2001 to 364.1 km2 in 2007. Correspondingly, the total biomass decreased from 489,000 tons in 2001 to 406,000 tons in 2007, suggesting that a great change in the ecological environment has been taking place in Taihu Lake over this period.