基于高光谱植被指数的马铃薯叶片叶绿素含量估测模型

【目的】筛选相关性好的植被指数构建马铃薯叶片叶绿素a、叶绿素b估测模型,为科学、无损地进行马铃薯叶片叶绿素含量估算提供技术支撑。【方法】采用便携式高光谱地物波谱仪,获取不同施氮水平下不同生育时期的马铃薯植株叶片光谱反射率,提取植被指数,测定马铃薯叶片叶绿素a、叶绿素b含量,并研究叶绿素含量与植被指数的相关性。【结果】12个植被指数与叶绿素a、叶绿素b含量相关性较好,其中修正归一化差异指数(mND_(705))、修正简单比值指数(mSR_(705))、地面叶绿素指数(MTCI)、修改叶绿素吸收反射指数(MCARI)与叶绿素a、叶绿素b含量相关性最好。基于这4个植被指数建立的估测模型中,MTCI构建的乘幂模型估测叶绿素a含量的效果最佳,mND_(705)构建的指数模型估测叶绿素b含量的效果最佳。【结论】MTCI构建的乘幂模型能较为精确地估测叶绿素a含量,mND_(705)构建的指数模型能较为精确地估测叶绿素b含量;这2种模型可用于间接监测马铃薯植株的氮营养亏缺状态。

Estimation models of chlorophyll contents in potato leaves based on hyperspectral vegetation indices

Objective] To build models for estimating chlorophyll a and chlorophyll b contents in potato, Solanum tuberosum, leaves by using hyperspectral vegetation indices, and provide a basis for scientific and nondestructive estimation of chlorophyll content in potato leaves. Method]Leaf spectral reflectance was measured for potato plants at different growth stages under different nitrogen levels by using a portable spectrometer. The hyperspectral vegetation indices were extracted from leaf spectral reflectance. The con-tents of chlorophyll a and chlorophyll b in potato leaves were measured, and the relationship between hyperspectral vegetation index and chlorophyll content was analyzed. Result]There were 12 vegetation indices with relatively good correlations with chlorophyll content. Modified normalized difference index ( mND705 ) , modified simple ratio index( mSR705 ) ,MERIS terrestrial chlorophyll index( MTCI) and modi-fied chlorophyll absorption reflectance index( MCARI) had the highest correlations with chlorophyll con-tents among the 12 vegetation indices. Estimation models were established based on above-mentioned four indices. The power model based on MTCI offered the best estimation of chlorophyll a content, and the in-dex model based on MCARI offered the best estimation of chlorophyll b content.Conclusion]The power model based on MTCI and the index model based on mND705 can be respectively used to estimate chloro-phyll a and chlorophyll b contents in potato leaves with relatively high accuracy,and indirectly monitor nitrogen deficiency in potato plants.