[Climatic suitability model for spring maize in Northeast China].

Crop growth is a continuous and dynamic process, and thus, the optimal meteorological indices for crop growth also change continuously. In this paper, the estimation algorithms of the daily available precipitation and the temporal interpolation methods of meteorological indices for crop growth were developed, and the climatic suitability model at daily scale was established, which was tested by the spring maize data of agro-meteorological stations in Northeast China. The simulated climatic suitability had significantly positive correlations with the height and 100-grain mass of spring maize. The correlation coefficient (R2) between the climatic suitability and the spring maize height was over 0.58 at vegetative growth stage, and over 0.45 at reproductive growth stage. The established climatic suitability model at daily scale could objectively reflect the interactions between crop growth and meteorological factors at vegetative growth stage and reproductive growth stage.

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

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.

[Dynamic assessment of grassland degradation in Naqu of northern Tibet].

Based on the past years vegetation cover, annual maximal grass yield and June-September mean modified soil-adjusted vegetation index (MSAVI) that were inversely deduced from the 10-day composite data of NOAA/AVHRR channels 1 and 2 and NDVI in 1982-2000, this paper analyzed the recent 20 years dynamics of grassland desertification in Naqu of northern Tibet. The results showed that in recent 20 years, the area of degraded grassland in Naqu was averagely 43.1% of the total land area. It was decreased in the former ten years while increased in the latter ten years, but overall, had a decreasing trend. The degraded area was larger in west part of Naqu than in its other regions. Among the eight climatic factors including temperature, precipitation, potential evapotranspiration, vapor pressure, wind velocity, sunshine hour, ratio of precipitation to evapotranspiration, and ratio of temperature to precipitation, the most remarkable factor affecting the dynamic of grassland degradation was the potential evapotranspiration.

[Classification and dynamic changes of grasslands in northern Tibet based on recent 20 years satellite data].

Based on the NOAA/AVHRR 10-day composite NDVI data from 1982 to 2000 and by the methods of principal component analysis and unsupervised classification, the vegetations in Naqu District of northern Tibet were classified, and the annual and inter-annual variations of NDVI on the pixels selected from different grassland types were analyzed. The regions where the mean NDVI values during the main growth season of grass were equal to or great than 0.1 were defined as vegetation regions, while those where the mean NDVI values were less than 0.1 were defined as non-vegetation regions. The spatiotemporal changes of NDVI in each pixel in vegetation regions were analyzed, and the results showed that the grasslands in northern Tibet could be classified into four types, i. e., high-cold meadow, high-cold meadow steppe, high-cold steppe, and high-cold desert. This classification was accorded with practical status. The annual changes of the NDVI in the four grassland types showed one peak value, with the maximum in August. In recent 20 years, the mean NDVI from July to August in vegetation regions decreased gradually from southeast to northwest, ranging from 0.1 to 0.6, and the variation coefficient changed between 0.05 and 0.40, which was smaller in the place of high NDVI than in the place of low NDVI. The annual variability of NDVI changed between -0.005 and 0.008. In recent 20 years, the vegetations had no observable change, but the vegetation development in 20% of vegetation regions, which mainly located in Nima in western Naqu and in Jiali, Biru, Suoxian and Baqing in eastern Naqu, was weakened.

[Variation trends of China terrestrial vegetation net primary productivity and its responses to climate factors in 1982-2000].

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.