基于高光谱指数的水稻砷污染胁迫多重判别模型

水稻中过量砷(As)能够影响叶片中叶绿素含量、水分含量以及细胞内部结构,进而改变水稻在光谱上的特征表现.以表征叶绿素、水分含量、细胞结构的NDVI、NDWI、SIPI等9种高光谱指数和实测水稻叶片砷含量数据为基础,利用相关分析、主成分分析和独立变量分析方法,获得诊断指标对水稻砷污染胁迫进行多重光谱判别.结果表明,表征不同生理参数的高光谱指数PSNDa、fWBI、SIPI与水稻叶片砷含量高度相关,可作为砷污染单级光谱诊断参数;主成分因子F1、F2和独立变量因子ICA1、ICA2对水稻砷污染胁迫具有特殊反应,分别作为水稻砷污染胁迫的主成分诊断指标和独立分量诊断指标.综合上述3类诊断参数,构建水稻砷污染胁迫多重判别空间体系即光谱指数诊断空间(PSNDa-fWBI、PSNDa-SIPI、fWBI-SIPI)、主成分诊断空间(F1-F2)和独立变量诊断空间(ICA1-ICA2),从不同层面上综合诊断了实验区水稻砷污染胁迫情况,其中以表征叶绿素和细胞结构的光谱指数空间PSNDa-SIPI与主成分空间F1-F2诊断效果为最佳.

Multi-diagnosis Space Models of As Stress in Rice Based on Hyperspectral Indices

High arsenic content in rice can influence the chlorophyll,water content and structure in their leaves, reduce the rate of photosynthesis and change their spectral features. Multiple models for diagnosing As contamination in rice based on spectral parameters were studied. Sixty samples belonging to mature rice in three different areas were scanned by ASD field pro3 for optical data. Arsenic reference values were obtained by atomic absorption spectrometry. First, correlation analysis was used between 9 hyperspectral indices and As content in rice, and three indices(PSNDa,fWBI,SIPI)were extracted to diagnose As contamination in rice, which were respectively sensitive to chlorophyll, water content and structure of leaves, then took the three indices to form a diagnosis spectral indices space (PSNDa-fWBI,PSNDa-SIPI,fWBI-SIPI) of As stress in rice. Second, principal component analysis and independent component analysis were also applied in these 9 hyperspectral indices, and two principal components(F1,F2) and two independent components(ICA1,ICA2) were extracted. These four components(F1,F2, ICA1,ICA2) were all correlated with As content in rice, and composed another two diagnosis spaces (F1-F2, ICA1-ICA2) for predicting As contamination. And these spaces composed a multiple diagnosis space model which diagnosed As contamination in rice of test area from different level, and showed a good result.