Rice is one of the most important grain crops in Northeast China （NEC） and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S （conversion of land use and its effects at small region extent） model was ifrst updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods： 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This conifrmed that climate change, increases in temperature in particular, greatly inlfuenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1＆#176;C isoline migration pattern, but with an obvious time-lag effect. These ifndings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.
XIA TianWU Wen-binZHOU Qing-boYU Qiang-yiPeter H VerburgYANG PengLU Zhong-junTANG Hua-jun
The leaf area index（LAI） is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types：approaches based on statistical models,physical models（i.e.,canopy reflectance models）,and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches（e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models）.Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the ＂curse of dimensionality＂ and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.
