The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Xu, Dandan; Liu, Yanqing; Xu, Weixin; Guo, Xulin

doi:10.3390/rs14133031

Cited by 3 publications

(1 citation statement)

References 56 publications

(98 reference statements)

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“…In calculating and selecting the spectral index, all the combinations of wavelength indexes are in the red edge range, and the optimal wavelength position is consistent with the previous research results [29]. The red edge refers to the point where the reflectance of green plants increases fastest between 670 and 760 nm.…”

Section: Discussionsupporting

confidence: 82%

Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

Zhang,

Li,

et al. 2023

Sustainability

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Leaf area index (LAI) is essential for evaluating crop growth and development. Destructive manual measurement methods mainly achieve traditional crop LAI acquisition. Due to the advantages of being fast and non-destructive, spectroscopy technology provides a feasible method for obtaining crop LAI. In order to achieve efficient acquisition of winter oilseed rape LAI, this study collected hyperspectral data and LAI data at the full-bloom stage of winter oilseed rape. It calculated the spectral indexes related to the LAI of the original spectrum and the first-order differential spectrum, respectively. The index with the highest correlation with the LAI of winter oilseed rape at the flowering stage was selected as the optimal spectral index for input. Subsequently, three machine learning methods, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF), were used to construct the LAI model of winter oilseed rape, and the model was tested. The results show that the correlation coefficient between the spectral index calculated by the first-order differential processing of the original spectral data and the LAI of winter rapeseed is significantly improved compared with the original data. Among them, the spectral index NDVI with the best correlation coefficient with LAI can be obtained under the first-order differential: the correlation coefficient is 0.734, and the wavelength combination is 716 nm and 724 nm. At the same time, we found that when the input variables are the same, the RF model has higher estimation accuracy than the other models. The best estimation accuracy is obtained when the input variable is the first-order differential spectral index. The R2 of the model validation set is 0.810, RMSE is 0.455 cm2/cm2, MRE is 10.465%, and the model accuracy is high. The results of this study can provide a theoretical basis for crop monitoring based on spectral technology and provide a theoretical basis for crop growth.

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Section: Discussionsupporting

confidence: 82%

Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

Zhang,

Li,

et al. 2023

Sustainability

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Research on potato (Solanum tuberosum L.) nitrogen nutrition diagnosis based on hyperspectral data

Tang,

Xiang,

Zhang

et al. 2024

Agronomy Journal

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Reducing the over application of N fertilizers to potatoes (Solanum tuberosum L.) can reduce production costs and their impact on the environment. One approach to produce these impacts is to reduce over applications of fertilizers by using the nitrogen nutrition index (NNI = plant N concentration/critical N concentration) as a basis for in‐season N recommendations. The objective of this study was to create a remote sensing‐based algorithm to estimate NNI. This study collected hyperspectral data (350 to 1830 nm) during the potato tuber formation period in 2022 and 2023. The climate regime for the study area was a mid‐temperate semi‐arid continental monsoon, in our study, three different spectral parameter calculation methods were employed. Firstly, the empirical vegetation index, determined through a fixed two‐band calculation. Secondly, the optimal vegetation index, computed on a band‐by‐band basis. Lastly, the trilateral spectral approach, wherein the indicators are typically associated with the red edge, blue edge, and green edge. The optimum vegetation index had the highest correlation with NNI. The support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) models were used to create NNI prediction models. All machine learning models effectively estimated NNI, and during validation the R2 were > 0.700. In general, the RF model outperformed the other models and during validation had a R2 of 0.869, a root mean square error (RMSE) of 0.052, and mean relative error (MRE) of 5.504%. This study demonstrates the scalability, simplicity, and cost‐effectiveness of combining hyperspectral technology and machine learning for rapid potato NNI estimation.This article is protected by copyright. All rights reserved

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A comprehensive survey on quantifying non-photosynthetic vegetation cover and biomass from imaging spectroscopy

Verrelst,

Halabuk,

Atzberger

et al. 2023

Ecological Indicators

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The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Cited by 3 publications

References 56 publications

Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

Research on potato (Solanum tuberosum L.) nitrogen nutrition diagnosis based on hyperspectral data

A comprehensive survey on quantifying non-photosynthetic vegetation cover and biomass from imaging spectroscopy

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