Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition

Lianghong, 蔡亮红 Cai; Jianli, 丁建丽 Ding

doi:10.3788/lop55.013001

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 4, the Rc2 increased gradually first and then declined with increasing decomposition levels, and similar conclusions were also deduced by Cai [27] in the study on soil moisture content (SMC). Among models with first to fifth levels of the db2 wavelet, the optimal decomposition level is fourth because the Rc2 was relatively higher than that of other models indicating that the noise or irrelative signal was removed.…”

Section: Results and Analysissupporting

confidence: 85%

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Via

Cheng

et al. 2018

Sensors

View full text Add to dashboard Cite

The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters (Rc2 = 0.834, RMSEC = 0.262, RPDc = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) (Rc2 = 0.816, RMSEC = 0.276, RPDc = 2.331) and raw spectra (Rc2 = 0.822, RMSEC = 0.271, RPDc = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.

show abstract

Section: Results and Analysissupporting

confidence: 85%

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Via

Cheng

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

Comparison of Hyperspectral Soil Water Content Inversion Models Based on PCA

张¹

2021

AAM

View full text Add to dashboard Cite

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

show abstract

Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition

Cited by 3 publications

References 0 publications

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Comparison of Hyperspectral Soil Water Content Inversion Models Based on PCA

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Contact Info

Product

Resources

About