Fifty-three leaves were randomly sampled on different deciduous tree species, representing a wide range of chlorophyll contents, tree ages, and leaf structural features. Their reflectance was measured between 400 and 800 nm with a 1-nm step, and their chlorophyll content determined by extraction. A larger simulated database (11,583 spectra) was built using the PROSPECT model, in order to test, calibrate, and obtain universal indices, i.e., indices applicable to a wide range of species and leaf structure. To our knowledge, almost all leaf chlorophyll indices published in the literature since 1973 have been tested on both databases. Fourteen canonical types of indices (published ones and new ones) were identified, and their wavelengths calibrated on the simulated database as well as on the experimental database to determine the best wavelengths and, hence, the best performances in chlorophyll estimation for each index types. These indices go from simple reflectance ratios to more sophisticated indices using reflectance first derivatives (using the Savitzky and Golay method). We also tested other nondestructive methods to obtain total chlorophyll concentration: SPAD (Minolta Camera, Osaka, Japan) and neural networks. The validity of the actual PROSPECT model is challenged by our results: Important discordances are found when the indices are calculated with PROSPECT compared to experimental data, especially for some indices and wavelengths. The discordance is even greater when the indices are determined with PROSPECT and applied on the experimental database. A new calibration of PROSPECT is therefore necessary for any study aiming at using simulated spectra to determine or to calibrate indices. The “peak jump” and the multiple-peak feature observed on the first derivative of the reflectances (e.g., in the Red-Edge Inflection Point (REIP) index) has been investigated. It was shown that chlorophyll absorption alone can explain this feature. The peak jump disqualifies the REIP to be a valuable chlorophyll index. A simple modified difference ratio gave the best results among all published indices (cross-validated RMSE=2.1 μg/cm 2 on the experimental database). After calibration on the experimental database, modified Simple Ratio (mSR) and modified Normalized Difference (mND) indices gave the best performances (RMSECV=1.8 μg/cm 2 on the experimental database). The new Double Difference (DD) index, although not the best on the experimental database (RMSECV=2.9 μg/cm 2 ), has the best results on the larger simulated database (RMSE=3.7 μg/cm 2 ) and is expected to give good results on larger experimental databases. The best reflectance-based indices give better performances than the current commercial nondestructive device SPAD (RMSECV=4.5 μg/cm 2 ). In this leaf-level study, the best indices are very near from each other, so that complex methods are useless: REIP-like, neural networks, and derivative-based indices are not necessary and give worst results than simpler properly chosen indices. These conclusions will certainly be different for a canopy-level study, where the derivative-based indices may perform significantly better than the other ones.
Remote Sensing of Environment – Elsevier
Published: Jan 15, 2004
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