CAJ | 학술논문

农业遥感中，利用光谱指数方法反演作物叶绿素含量一直得到广泛地应用。利用PSR‐3500光谱仪及SPAD‐502叶绿素仪同步获取了冬小麦冠层光谱数据及对应叶片的叶绿素相对含量（SPAD值），并利用高斯光谱响应模型将PSR获取的地面连续光谱数据重采样为多光谱Landsat‐TM7及高光谱 Hyperion光谱数据，然后分别计算基于两种传感器的归一化差值植被指数（normalized difference vegetation index ，NDVI）、综合叶绿素光谱指数（MCARI／OSAVI ，the ratio of the modified transformed chlorophyll absorption ratio in‐dex （MCARI） to optimized soil adjusted vegetation index （OSAVI））、三角形植被指数（triangle vegetation in‐dex ，TVI）及通用植被指数（vegetation index based on universal pattern decomposition method ，VIUPD），再将四种光谱指数与叶绿素含量进行回归分析。结果表明，针对重采样后的 TM 和 Hyperion两种传感器数据，VIU PD反演叶绿素含量精度（决定系数 R2）最高，反演能力最稳定，这与其“不受传感器影响”的特性密不可分；MCARI／OSAVI反演精度和稳定性次之，是因为引入的OSAVI削弱了土壤背景的影响；宽波段指数NDVI和TVI对模拟TM数据有较好的反演精度，对Hyperion数据反演精度却很低，可能是因为两种指数的构成形式简单，考虑的影响因素较少。以冬小麦为例，对利用光谱指数反演植被叶绿素含量的精度和稳定性进行了研究并分析了其影响因素，经比较发现利用植被指数V IU PD进行植被叶绿素含量反演时，其精度和稳定性最好。
농업요감중，이용광보지수방법반연작물협록소함량일직득도엄범지응용。이용PSR‐3500광보의급SPAD‐502협록소의동보획취료동소맥관층광보수거급대응협편적협록소상대함량（SPAD치），병이용고사광보향응모형장PSR획취적지면련속광보수거중채양위다광보Landsat‐TM7급고광보 Hyperion광보수거，연후분별계산기우량충전감기적귀일화차치식피지수（normalized difference vegetation index ，NDVI）、종합협록소광보지수（MCARI／OSAVI ，the ratio of the modified transformed chlorophyll absorption ratio in‐dex （MCARI） to optimized soil adjusted vegetation index （OSAVI））、삼각형식피지수（triangle vegetation in‐dex ，TVI）급통용식피지수（vegetation index based on universal pattern decomposition method ，VIUPD），재장사충광보지수여협록소함량진행회귀분석。결과표명，침대중채양후적 TM 화 Hyperion량충전감기수거，VIU PD반연협록소함량정도（결정계수 R2）최고，반연능력최은정，저여기“불수전감기영향”적특성밀불가분；MCARI／OSAVI반연정도화은정성차지，시인위인입적OSAVI삭약료토양배경적영향；관파단지수NDVI화TVI대모의TM수거유교호적반연정도，대Hyperion수거반연정도각흔저，가능시인위량충지수적구성형식간단，고필적영향인소교소。이동소맥위례，대이용광보지수반연식피협록소함량적정도화은정성진행료연구병분석료기영향인소，경비교발현이용식피지수V IU PD진행식피협록소함량반연시，기정도화은정성최호。
Spectral index method was widely applied to the inversion of crop chlorophyll content .In the present study ,PSR3500 spectrometer and SPAD‐502 chlorophyll fluorometer were used to acquire the spectrum and relative chlorophyll content (SPAD value) of winter wheat leaves on May 2nd 2013 when it was at the jointing stage of winter wheat .Then the measured spectra were resampled to simulate TM multispectral data and Hyperion hyperspectral data respectively ,using the Gaussian spectral re‐sponse function .We chose four typical spectral indices including normalized difference vegetation index (NDVI) ,triangle vegeta‐tion index (TVI) ,the ratio of modified transformed chlorophyll absorption ratio index (MCARI) to optimized soil adjusted vege‐tation index(OSAVI) (MCARI/OSAVI) and vegetation index based on universal pattern decomposition (VIUPD) ,which were constructed with the feature bands sensitive to the vegetation chlorophyll .After calculating these spectral indices based on the resampling TM and Hyperion data ,the regression equation between spectral indices and chlorophyll content was established .For TM ,the result indicates that VIUPD has the best correlation with chlorophyll (R2 = 0.819 7 ) followed by NDVI (R2 =0.791 8) ,while MCARI/OSAVI and TVI also show a good correlation with R2 higher than 0.5 .For the simulated Hyperion da‐ta ,VIUPD again ranks first with R2 =0.817 1 ,followed by MCARI/OSAVI (R2 =0.658 6) ,while NDVI and TVI show very low values with R2 less than 0.2 .It was demonstrated that VIUPD has the best accuracy and stability to estimate chlorophyll of winter wheat whether using simulated TM data or Hyperion data ,which reaffirms that VIUPD is comparatively sensor inde‐pendent .The chlorophyll estimation accuracy and stability of MCARI/OSAVI also works well ,partly because OSAVI could re‐duce the influence of backgrounds .Two broadband spectral indices NDVI and TVI are weak for the chlorophyll estimation of simulated Hyperion data mainly because of their dependence on few bands and the strong influence of atmosphere ,solar altitude , viewing angle of sensor ,background and so on .In conclusion ,the stability and consistency of chlorophyll estimation is equally important to the estimation accuracy by spectral index method .VIUPD introduced in the study has the best performance to esti‐mate winter wheat chlorophyll ,which illustrates its potential ability in the area of estimating vegetation biochemical parameters .