基于小波分析的玉米叶绿素a与LAI高光谱反演模型研究

2003与2004年分别在长春市良种场与中国科学院海伦黑土生态实验站,实测了大田耕作与不同经营制度作用下玉米冠层高光谱反射率与叶绿素a含量、叶面积指数（LAI）数据。采用NDVI（Normalized Difference Vegetation index）、SAVI（Soil-Adjusted Vegetation Index）、TVI（Transformed Vegetation Index）、MTVI2（Modified Transformed Vegetation Index2）等植被指数建立了玉米叶绿素a与LAI反演模型;应用小波分析对采集的光谱反射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对玉米叶绿素a与LAI进行估算。研究发现NDVI、SAVI、TVI、MTVI2植被指数可以较为准确的估算玉米叶绿素a、LAI（R^2〉0.70）;小波能量系数回归模型可以进一步提高玉米叶绿素a含量、LAI的估算水平,以一个特定小波能量系数作为自变量的回归模型,玉米叶绿素a、LAI回归确定系数R2分别高达0.79、0.82;以多变量回归分析表明玉米叶绿素a、LAI实测值与预测值的线性回归确定性系数R2分别高达0.87、0.90。以上结果表明,小波分析可以对高光谱数据进行特征变量提取,进而反演玉米生理参数,对其他植被生理参数反演具有一定借鉴意义。图3,表4,参11。

Corn Chlorophyll-a Concentration and LAI Estimation Models Based on Wavelet Transformed Canopy Hyperspectral Reflectance

In order to evaluate the precision of hyperspectral reflectance model for monitoring corn Chlorophyll a concentration and leaf area index（LAI）,corn canopy reflectance data were collected with ASD spectroradiometer（350~1050nm） in 2003 and 2004 at two sampling sites,one was at Changchun suburban area,and the other was in Hailun Black Soil Ecology Station,Chinese Academy of Sciences.First,four vegetation indices（NDVI、SAVI、MTVI2、SUM_FDR） were applied to regress against corn chlorophyll a concentration and LAI;Secondly,corn canopy reflectances were transformed with wavelet analysis,and extracted wavelet energy coefficient were applied in regression model for chlorophyll a concentration and LAI estimation.It were found that these four vegetation indices have intimate correlation with corn chlorophyll a concentration and LAI,with regression determination coefficient R2 greater than 0.70.Single variable regression model based upon wavelet extracted reflectance energy coefficient can accurately estimate corn chlorophyll a concentration and LAI,R^2 for chlorophyll a concentration and LAI were 0.79,0.82 respectively,while with multivariate regression model,R^2 were 0.87,0.90 for chlorophyll a concentration and LAI respectively.By above analysis,it indicated that wavelet analysis can be applied to in-situ collected hyperspectral data for corn chlorophyll a concentration and LAI estimation with better performance.And in the future,wavelet analysis methods should be applied to hyperspectral data for other vegetation biophysical and biochemical parameters estimation.