中文摘要：

印射在多重收割区域与光形象收割系统的米饭由于云污染和数据可获得性是挑战性的；有一个减少的数据要求的一个基于物候学的算法的开发是必要的。在这研究，规范的差别植被索引(RNDVI ) 的导出 Landsat 的重新使正常化的索引基于 NDVI 单身者并且早在珍视的二个时间的窗口被建议(或迟了) 米饭显示器逆变化，然后适用区别收割系统的米饭。波伊昂·莱克区域(PLR ) ，由收割米饭(SCR，或单个米饭) 和两倍收割米饭(DCR，包括的早米饭和迟了的米饭) 的单身者的一个典型收割系统描绘了，作为一个严峻的区域被选择。结果显示出数据在八点从 Landsat 时间系列导出到十六天俘获的那 NDVI 稻米饭的时间的发展。在 SCR 和 DCR 的 NDVI 价值相反地在变化的重叠生长时期期间有二个关键 phenological 阶段，也就是，早米饭的成熟阶段和象成熟一样的单个米饭的成长阶段近来单个米饭和成长阶段上演米饭。NDVI 在二扇时间的窗户，明确地早的 8 月和早 10 月中源于场面，被用来为区别在 PLR 的新辟的低地区域收割系统的米饭构造 RNDVI，中国。有地面真相数据的比较显示高分类精确性。RNDVI 途径由于在二个时间的窗口之间的米饭生长的差别加亮 NDVI 值的反的变化。当它仅仅需要区分候选人米饭类型是否在生长的时期，这使收割系统的米饭的辨别直接(RNDVI < 0 ) 或老朽(RNDVI > 0 ) 。

英文摘要：

Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renorma- lized Index of Normalized Difference Vegetation Index （RNDVI） was proposed based on two temporal windows in which the NDVI values of single and early （or late） rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region （PLR）, characterized by a typical cropping system of single cropping rice （SCR, or single rice） and double cropping rice （DCR, including early rice and late rice）, was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice typeis in the period of growth （RNDVI 〈 0） or senescence （RNDVI 〉 0）.