scholarly journals Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

2019 ◽  
Vol 11 (18) ◽  
pp. 2148 ◽  
Author(s):  
Mengmeng Xie ◽  
Zhongqiang Wang ◽  
Alfredo Huete ◽  
Luke A. Brown ◽  
Heyu Wang ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Cross Validation ◽  
Vegetation Indices ◽  
Leaf Structure ◽  
Adaxial Surface ◽  
Leaf Chlorophyll Content ◽  
Retrieval Accuracy ◽  
Leaf Chlorophyll ◽  
Leaf Surfaces ◽  
The Impact

Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was ( R 723 − R 738 ) / ( R 723 − R 722 ) with a cross-validation R2cv of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were ( R 735 − R 753 ) / ( R 715 − R 819 ) for estimating LCC from the adaxial surface (R2cv = 0.96, RMSEcv = 2.37 μg/cm2) and ( R 732 − R 754 ) / ( R 724 − R 773 ) for estimating LCC from reflectance of both sides (R2cv = 0.94, RMSEcv = 2.81 μg/cm2). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.

Redefining the Red Band-Based Vegetation Indices for More General Retrieval of Leaf Chlorophyll Content

2015 ◽  
Vol 13 (7) ◽  
pp. 605-610
Author(s):  
Ligen Xu ◽  
Jing Zhang ◽  
Xinjie Wang ◽  
Qifa Zhou
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Indices ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data

2020 ◽  
Vol 12 (16) ◽  
pp. 2574
Author(s):  
Xianfeng Zhou ◽  
Jingcheng Zhang ◽  
Dongmei Chen ◽  
Yanbo Huang ◽  
Weiping Kong ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Dynamic Change ◽  
Vegetation Indices ◽  
Growth Conditions ◽  
Crop Growth ◽  
Lookup Table ◽  
Physiological Status ◽  
Landsat 8 ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based inversion, and hybrid regression approaches. The results showed that the modified triangular vegetation index (MTVI2) exhibited the best estimate accuracy for LCC retrieval with a root mean square error (RMSE) of 5.99 μg/cm2 and a relative RMSE (RRMSE) of 10.49%. Several other vegetation indices that were established from red and near-infrared (NIR) bands also exhibited good accuracy. Models established from Gaussian process regression (GPR) achieved the highest accuracy for LCC retrieval (RMSE = 5.50 μg/cm2, RRMSE = 9.62%) compared with other MLRAs. Moreover, red and NIR bands outweighed other bands in terms of GPR modelling. LUT-based inversion methods with the “K(x) = −log (x) + x” cost function that belongs to the “minimum contrast estimates” family showed the best estimation results (RMSE = 8.08 μg/cm2, RRMSE = 14.14%), and the addition of multiple solution regularization strategies effectively improved the inversion accuracy. For hybrid regression methods, the use of active learning (AL) techniques together with GPR for LCC modelling significantly increased the estimation accuracy, and the combination of entropy query by bagging (EQB) AL and GPR had the best accuracy for LCC estimation (RMSE = 12.43 μg/cm2, RRMSE = 21.77%). Overall, our study suggest that Landsat-8 OLI data are suitable for crop LCC retrieval and could provide a basis for LCC estimation with similar multispectral datasets.

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