Search

NDVI based on NOAA/AVHRR from 1982 to 2003 are used to monitor variable rules for the growing season in spring of vegetation in the north-south transect of eastern China (NSTEC). The following, mainly, are included: (1) The changing speed of greenness period in spring of most regions in NSTEC is slow and correlation with the year is not distinct; (2) The regions in which greenness period in spring distinctly change mainly presented an advance; (3) The regions in which inter-annual fluctuation of greenness period in spring is over 10 days were found in 3 kinds of areas: the area covered with agricultural vegetation types; the areas covered with evergreen vegetation types; the areas covered with steppe vegetation types; (4) changes of vegetation greenness period in spring have spatio-temporal patterns.

The research object of the present paper is the water quality of Han Shiqiao wetland water. Water spectrum and quality parameters were measured on the site and in the lab. The authors simulated the relationships between water quality parameters and the best bands or combination, and built the multiple linear regression equation to obtain characteristic spectrum of the key water quality parameters. Besides, several key issues involved in applying ASTER satellite imagery to water quality include atmospheric correction, discussing methods for ASTER data bands analysis, and choosing the best bands and band combination. Results indicated that although the simulation model is not universal, the analysis of spectral characteristics based on ground spectrometer could provide foundations for the choice of remote sensing characteristics bands. The band ratio of water quality parameters simulated from ASTER spectral characteristics moves to relatively long-wave band. Finally, based on the analysis of ASTER remote sensing characteristics bands, the authors built water quality parameters regression model. The models for water quality parameters were recommended, and the accuracies of these models were analyzed. Making use of regression model, we executed spatial distribution map of water quality parameters to achieve wetland water monitoring with remote sensing in terms of variation in space and with time.

The present paper selected Qing yundian town and Weishanzhuang town in Da Xing District, and Gaoling ying town in Shunyi District as test areas, using MODIS data and ASTER data in different scales. The feasibility of winter wheat LAI inversion by PROSAIL physical model, especially the stability of remote sensing data in different scales, was discussed, and the results from experience model inversion were compared with that from statistical methods. The values of all samples LAI inversion from experience model are close in a region, which means experience model is a reflection of general growing trend, ignoring spatial heterogeneity of the regional leaf area index. But the value of LAI inversion from physical model can be truer in reflecting spatial heterogeneity of the regional leaf area index. The value of LAI inversion from physical model is more real, compared with experience model. With the method of linear weighing, the scale conversion was accomplished, and the LAI inversion results from different remote sensing scale data were compared, and were found similar. The result shows that in the process of large-scale regional LAI inversion, physical model inversion is more valid.