CAJ | 학술논문

叶片含水量是反映作物生理特性的一个重要参数，对生态环境的研究具有重要意义。采用小波分析方法，分析叶片含水量对反射率的影响特征，建立综合利用多波段信息的作物叶片水分含量反演模型。基于PROSPECT 模型的辐射传输理论，推导出由叶片反射率光谱的小波系数反演叶片水分含量 CW 的理论模型。利用六种常用的小波函数，对叶片组分水、干物质和白化基本层的吸收光谱进行小波分解。选取对水分变化最敏感，同时对其他组分不敏感的分解尺度和波段位置，找到能稳定突出水的光谱特征的小波系数。结果表明：bio r1.5小波函数在尺度为200 nm ，波段位置为1405和1488 nm的小波系数具有上述特征。建立由叶片反射率光谱的bio r1.5小波系数反演叶片水分含量 CW 的反演模型，模型有两个转换系数 a和Δ都受叶片结构参数N的影响。利用PROSPECT模型生成模拟光谱数据集，校正建立的叶片水分含量反演模型中的两个转换系数 a和Δ，并与LOPEX93实验光谱数据集结合验证反演模型。结果表明：反演模型不仅比传统基于植被指数的统计模型在精度上有提高（反演值与实测值的 R2最高达到0.987），而且更加稳定，普适性更高。研究表明，小波分析方法在利用高光谱数据反演作物叶片水分含量方面具有独特的优势。
협편함수량시반영작물생리특성적일개중요삼수，대생태배경적연구구유중요의의。채용소파분석방법，분석협편함수량대반사솔적영향특정，건립종합이용다파단신식적작물협편수분함량반연모형。기우PROSPECT 모형적복사전수이론，추도출유협편반사솔광보적소파계수반연협편수분함량 CW 적이론모형。이용륙충상용적소파함수，대협편조분수、간물질화백화기본층적흡수광보진행소파분해。선취대수분변화최민감，동시대기타조분불민감적분해척도화파단위치，조도능은정돌출수적광보특정적소파계수。결과표명：bio r1.5소파함수재척도위200 nm ，파단위치위1405화1488 nm적소파계수구유상술특정。건립유협편반사솔광보적bio r1.5소파계수반연협편수분함량 CW 적반연모형，모형유량개전환계수 a화Δ도수협편결구삼수N적영향。이용PROSPECT모형생성모의광보수거집，교정건립적협편수분함량반연모형중적량개전환계수 a화Δ，병여LOPEX93실험광보수거집결합험증반연모형。결과표명：반연모형불부비전통기우식피지수적통계모형재정도상유제고（반연치여실측치적 R2최고체도0.987），이차경가은정，보괄성경고。연구표명，소파분석방법재이용고광보수거반연작물협편수분함량방면구유독특적우세。
Leaf water content is a fundamental physiological characteristic parameter of crops ,and plays an important role in the study of the ecological environment .The aim of the work reported in this paper was to focus upon the retrieval of leaf water con-tent from leaf-scale reflectance spectra by developing a physical inversion model based on the radiative transfer theory and wavelet analysis techniques .A continuous wavelet transform was performed on each of leaf component specific absorption coefficients to pick wavelet coefficients that were identified as highly sensitive to leaf water content and insensitive to other components .In the present study ,for identifying the most appropriate wavelet ,the six frequently used wavelet functions available within MATLAB were tested .Two bior1.5 wavelet coefficients observed at the scale of 200 nm are provided with good performance ,their wave-length positions are located at 1 405 and 1 488 nm ,respectively .Two factors (a and Δ) of the predictive theoretical models based on the bior1.5 wavelet coefficients of the leaf-scale reflectance spectra were determined by leaf structure parameter N .We built a database composed of thousands of simulated leaf reflectance spectra with the PROSPECT model .The entire dataset was split into two parts ,with 60% the calibration subset assigned to calibrating two factors (a and Δ) of the predictive theoretical model . The remaining 40% the validation subset combined with the LOPEX93 experimental dataset used for validating the models .The results showed that the accuracy of the models compare to the statistical regression models derived from the traditional vegetation indices has improved with the highest predictive coefficient of determination (R2 ) of 0.987 ,and the model becomes more robust . This study presented that wavelet analysis has the potential to capture much more of the information contained with reflectance spectra than previous analytical approaches which have tended to focus on using a small number of optimal wavebands while dis-carding the majority of the spectrum .