Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods

Shen, Lanzhi; Gao, Maofang; Yan, Jingwen; Wang, Qizhi; Su, Hua

doi:10.3390/rs14184660

Cited by 18 publications

(12 citation statements)

References 59 publications

(27 reference statements)

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“…FD reflects the slope of the spectrum, while SD represents the change in the slope of the reflection spectrum. While SD identifies more absorption peaks, it also introduces noise and may result in errors ( Shen et al., 2022 ). Therefore, FD is strongly correlated with AGB and can be effectively utilized for estimating rice AGB.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Nian,

Su,

Yue

et al. 2024

Front. Plant Sci.

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Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

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Section: Discussionmentioning

confidence: 99%

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Nian,

Su,

Yue

et al. 2024

Front. Plant Sci.

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“…This further proves that, if only the correlation analysis method is used to select the characteristic bands, some characteristic values will be ignored. Wavelet packet, as an effective method to remove noise during signal processing, has been used by many other scholars for the preprocessing of soil spectra [63,64]. The Shannon entropy wavelet packet transformation can solve the problem that the wavelet coefficients need to be artificially selected multiple times in the pruning judgment of the wavelet packet transformation.…”

Section: Pso-lssvr Modelingmentioning

confidence: 99%

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

Liu,

Huo,

Zhou

et al. 2023

Remote Sensing

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The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.

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“…This study offers valuable research support for future predictions of maize SPAD. In the study from Shen Lanzhi et al [21], they performed preprocessing on hyperspectral data using a combination of wavelet packet denoising, first-order derivative transformation, and principal component analysis. The results indicated that this preprocessing approach enhanced the accuracy and stability of the PLSR model.…”

Section: Introductionmentioning

confidence: 99%

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

Xie,

Wang,

Chen

et al. 2023

Remote Sensing

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The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

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Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods

Cited by 18 publications

References 59 publications

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

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