Analysis of optimal narrow band RVI for estimating foliar photosynthetic pigments based on PROSPECT model

Remote sensing is an effective tool to estimate foliar pigments contents with the analysis of vegetation index. The crucial issue is how to choose the optimal bands-combination to conduct the vegetation index. In this study, RVI, a vegetation index computed by the reflectance of Red and NIR bands, has been used to estimate the contents of chlorophyll and carotenoid. The reflectance of the two bands forming the narrow band RVI was simulated by the PROSPECT model. The possible combinations of narrow band RVI were examined from 400 nm to 800 nm. The results showed that: At the leaf level, estimation of chlorophyll content can be identified in narrow band RVI. Ranges for these bands included: (1) 549-589nm, 616-636nm or 729-735nm combined with 434-454nm; (2) 663-688nm, 710-717nm, 719-728nm or 730- 739nm combined with 549-561nm; (3) 663-688nm combined with 569-615nm. However, no valid narrow-band RVI for the estimation of carotenoid content was successfully identified. Our results also showed that two rules should be followed when choosing optimal bands-combination: (1) the selected bands must have minimal interference from other biochemical constituents; (2) there should be distinct differences between the sensitivities of the bands selected for particular pigments.

[1]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[2]  Kenneth J. Koehler,et al.  Sensitivity of Chlorophyll Meters for Diagnosing Nitrogen Deficiencies of Corn in Production Agriculture , 2008 .

[3]  K. Barry,et al.  Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling , 2011 .

[4]  Xin-Guang Zhu,et al.  Improving photosynthetic efficiency for greater yield. , 2010, Annual review of plant biology.

[5]  J. S. Schepers,et al.  Use of a Chlorophyll Meter to Monitor Nitrogen Status and Schedule Fertigation for Corn , 1995 .

[6]  Huang Jingfeng,et al.  Optimal simple ratio pigment index for estimating pigment contents of rice , 2009 .

[7]  Chao Bai,et al.  Nutritious crops producing multiple carotenoids--a metabolic balancing act. , 2011, Trends in plant science.

[8]  Martha C. Anderson,et al.  Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale , 2009 .

[9]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[10]  S. Ustin,et al.  Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data , 1996 .

[11]  Emily E. Whittington,et al.  Comparison of surface reflectance measurements from three ASD FieldSpec FR spectroradiometers and one ASD FieldSpec VNIR spectroradiometer , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[12]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[13]  M. Cho,et al.  An investigation into robust spectral indices for leaf chlorophyll estimation , 2011 .

[14]  J. Norman,et al.  Leaf Optical Properties , 1991 .

[15]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[16]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[17]  Roberta E. Martin,et al.  Leaf chemical and spectral diversity in Australian tropical forests. , 2009, Ecological applications : a publication of the Ecological Society of America.

[18]  Karen M. Barry,et al.  Estimation of chlorophyll content in Eucalyptus globulus foliage with the leaf reflectance model PROSPECT , 2009 .

[19]  Fumin Wang,et al.  Relationship between Narrow Band Normalized Deference Vegetation Index and Rice Agronomic Variables , 2004 .