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摘要： 考虑土壤调节参数的植被指数能够减小土壤背景影响，广泛应用于各种植被遥感监测中，然而水稻种植在覆水土壤之上，有别于常见的各种土壤背景。为此，通过两年水稻小区试验，获取水稻冠层光谱数据和对应的叶面积指数(LAI)数据，利用在一定范围内变化的土壤调节参数计算得到一个系列植被指数(EVI、SAVI、WDVI)，分析这些植被指数对水稻LAI的估算能力，确定水稻特殊背景下各植被指数的最佳土壤调节参数，研究发现，对EVI的最佳土壤调节参数为L=0.25，对SAVI的最佳土壤调节参数为L=0.10，对WDVI的最佳土壤调节参数为a=1.25，进而比较基于最佳土壤调节参数的植被指数与基于常规土壤调节参数的植被指数对水稻LAI估算效果。在以EVI为自变量的模型中，最佳参数RMSE相比常规参数降低6.82%；在SAVI模型中，最佳参数的RMSE相比常规参数降低10.23%。这些结果表明:针对水稻背景修正的土壤调节植被指数能够提高遥感反演水稻叶面积指数的准确度。  关键词： 植被指数;  叶面积指数;  水稻;  土壤调节参数;  遥感     Abstract: The vegetation indices that take the soil adjustment factor into consideration can reduce the influence of soil background conditions and have been widely used in monitoring all kinds of vegetation.However，the rice has been planted in the soil covered by a certain thickness of layer of water，which is different with other various soil backgrounds.Therefore，in this paper，through two years of rice plot experiments，we obtained the rice canopy spectral data and the corresponding leaf area index (LAI) data，and then calculated a series of vegetation indices (EVI，SAVI，WDVI) by using different soil adjustment factors changing within a certain range.We compared the abilities of these vegetation indices for rice LAI estimation，and then determine the optimum soil adjustment factors of vegetation indices to adjust the background of rice.In the study，we found that the best soil adjustment factor L for EVI，L of SAVI，a of WDVI are 0.25，0.10 and 1.25 respectively，and we further compared the LAI estimation results of the best soil adjustment factor with those of the conventional soil adjustment factor.For the model taking EVI as an independent variable，the RMSE of LAI estimation using the best soil adjustment factor is 6.82 % lower than that using the conventional soil adjustment factor；In SAVI model，the RMSE using the best soil adjustment factor is 10.23% lower than that using the conventional soil adjustment factor .These results indicate that the corrected vegetation indices considering the background of rice can improve the accuracy of rice leaf area index using remotely sensed data.

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