title: [Research on Spectral Scale Effect in the Estimation of Vegetation Leaf Chlorophyll Content].
abstract: Spectral indices (SIs) method has been widely applied in the prediction of vegetation biochemical parameters. Take the diversity of spectral response of different sensors into consideration, this study aimed at researching spectral scale effect of SIs for estimating vegetation chlorophyll content (VCC). The 5 nm leaf reflectance data under 16 levels of chlorophyll content was got by the radiation transfer model PROSPECT and then simulated to multiple bandwidths spectrum (10-35 nm), using Gaussian spectral response function. Firstly, the correlation between SIs and VCC was studied. And then the sensitivity of SIs to VCC and bandwidth were analyzed and compared. Lastly, 112 samples were selected to verify the results above mentioned. The results show that Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD) is the best spectral index due to its high sensitivity to VCC but low sensitivity to bandwidth, and can be successfully used to estimate VCC with coefficient of determination R2 of 0.99 and RMSE of 3.52 μg x cm(-2). Followed by VIUPD, Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SRI) presented a comparatively good performance for VCC estimation (R2 > 0.89) with their prediction value of chlorophyll content was lower than the true value. The worse accuracy of other indices were also tested. Results demonstrate that spectral scale effect must be well-considered when estimating chlorophyll content, using SIs method. VIUPD introduced in the present study has the best performance, which reaffirms its special feature of comparatively sensor-independent and illustrates its potential ability in the area of estimating vegetation biochemical parameters based on multiple satellite data.
authors.forename: Hai-ling
authors.initials: HL
authors.lastname: Jiang
authors.forename: Li-fu
authors.initials: LF
authors.lastname: Zhang
authors.forename: Hang
authors.initials: H
authors.lastname: Yang
authors.forename: Xiao-pine
authors.initials: XP
authors.lastname: Chen
authors.forename: Qing-xi
authors.initials: QX
authors.lastname: Tong
mesh_terms: D002734:Chlorophyll / Q000032:analysis*; D008962:Models, Theoretical; D018515:Plant Leaves / Q000737:chemistry*; D012044:Regression Analysis; D013057:Spectrum Analysis
chemical_list: D002734:Chlorophyll