Construction of hyperspectral reflectance and spectral index inversion model for the water content of Catalpa bungei leaves

Lv, Siyu; Wang, Junhui; Wang, Zhengde; Fang, Yang; Wang, Shanshan; Wang, Fuyu; Wang, Xiaoxi; Qu, Guanzheng; Ma, Wenjun

doi:10.1016/j.microc.2023.109811

Cited by 3 publications

(1 citation statement)

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“…In recent years, hyperspectral remote sensing technology has been developing rapidly: hyperspectral equipment provides a fast, non-destructive, and timely method of data collection, which can be used to measure the nutrient status of crops and to determine the growth of plants [8]. Scholars have conducted in-depth studies on the hyperspectral remote sensing of vegetation, including estimation of biomass [9], nitrogen content [10], water content [11], and leaf area index [12].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm

Li,

Xu,

et al. 2024

Remote Sensing

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Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves’ chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3, R2 = 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring.

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