scholarly journals Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

2020 ◽  
Vol 12 (2) ◽  
pp. 248 ◽  
Author(s):  
Yuhao He ◽  
Bin Yang ◽  
Hui Lin ◽  
Junqiang Zhang
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accurate Information ◽  
Generalized Regression Neural Network ◽  
Scattering Angle ◽  
Natural Land ◽  
Generalized Regression ◽  
Land Surfaces ◽  
Aerosol Properties ◽  
Polarized Reflectance

Retrieval of complete aerosol properties over land through remote sensing requires accurate information about the polarization characteristics of natural land surfaces. In this paper, a new bidirectional polarization distribution function (BPDF) is proposed, using the generalized regression neural network (GRNN). This GRNN-based BPDF model builds a quite accurate nonlinear relationship between polarized reflectance and four input parameters, i.e., Fresnel factor, scattering angle, red, and near-infrared reflectances. It learns fast because only a smoothing parameter needs to be adjusted. The GRNN-based model is compared to six widely used BPDF models (i.e., Nadal–Bréon, Maignan, Waquet, Litivinov, Diner, and Xie–Cheng models), using the Polarization and Directionality of the Earth’s Reflectance (POLDER) measurements. Experiments suggest that the GRNN-based BPDF model is more accurate than these models. Compared with the best current models, the averaged root-mean-square error (RMSE) from the GRNN-based BPDF model can be reduced by 13.4% by using data collected during the whole year and is lower for 97.4% cases with data collected during every month. Moreover, compared to the widely used BPDF models, the GRNN-based BPDF model provides better performance when the scattering angle is small, and it is the first model that is able to reproduce negative polarized reflectance. The GRNN-based BPDF model is thus useful for the remote sensing of complete aerosol properties over land.

scholarly journals Modeling Bidirectional Polarization Distribution Function of Land Surfaces Using Machine Learning Techniques

2020 ◽  
Vol 12 (23) ◽  
pp. 3891
Author(s):  
Siyuan Liu ◽  
Yi Lin ◽  
Lei Yan ◽  
Bin Yang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Distribution Function ◽  
Accurate Estimation ◽  
Polarization Distribution ◽  
Scattering Angle ◽  
Non Linear ◽  
Linear Problems ◽  
Input Variables ◽  
Land Surfaces

Accurate estimation of polarized reflectance (Rp) of land surfaces is critical for remote sensing of aerosol optical properties. In the last two decades, many data-driven bidirectional polarization distribution function (BPDF) models have been proposed for accurate estimation of Rp, among which the generalized regression neural network (GRNN) based BPDF model has been reported to perform the best. GRNN is just a simple machine learning (ML) technique that can solve non-linear problems. Many ML techniques were reported to work well in solving non-linear problems and consequently may provide better performance in BPDF modeling. However, incorporating various ML techniques with BPDF modeling and comparing their performances have never been well documented. In this study, three widely used ML algorithms—i.e., support vector regression (SVR), K-nearest-neighbor (KNN), and random forest (RF)—were applied for BPDF modeling. Using measurements collected by the Polarization and Directionality of the Earth’s Reflectance onboard PARASOL satellite (POLDER/PARASOL), non-linear relationships between Rp and the input variables, i.e., Fresnel factor (Fp), scattering angle (SA), reflectance at 670 nm (R670) and 865 nm (R865), were built using these ML algorithms. Results showed that taking Fp, SA, R670, and R865 as input variables, the performance of the four ML-based BPDF models was quite similar. The KNN-based BPDF model provided slightly better results, and improved the accuracy of the semi-empirical BPDF models by 9.55% in terms of the overall root mean square error (RMSE). Experiments of different configuration of input variables suggested that using multi-band reflectance as input variables provided better results than using vegetation indices. The RF-based BPDF model using all reflectances at six bands as input variables produced the best results, improving the overall accuracy by 6.62% compared with the GRNN-based BPDF model. Among all the input variables, reflectance at absorbing spectral bands—e.g., 490 nm and 670 nm—played more significant roles in RF-based BPDF modeling due to the domination of polarized partition in total reflectance. Fresnel factor and scattering angle were also important for BPDF modeling. This study confirmed the feasibility of applying ML techniques to more accurate BPDF modeling, and the RF-based BPDF model proposed in this study can be used to increase the accuracy of remote sensing of the complete aerosol properties.

Prediction of microfibril angle in Dahurian larch wood using visible–near infrared spectroscopy and chemometric techniques

2019 ◽  
Vol 27 (4) ◽  
pp. 270-277
Author(s):  
Ying Li ◽  
Brian K Via ◽  
Qingzheng Cheng ◽  
Yaoxiang Li
Keyword(s):  
Neural Network ◽  
Stepwise Regression ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Coupled Model ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Generalized Regression

The microfibril angle of the S2 layer in the secondary cell wall of the tracheid is important for molecular and microscopic properties that influence collapse resistance, longitudinal modulus of elasticity and other lateral properties of conifers at the macroscopic level. This research aimed to investigate the feasibility of using a fruit fly optimization algorithm for visible and near infrared modeling optimization of Dahurian larch wood microfibril angle prediction. Originally, the linear relationship between microfibril angle and their raw spectra and visible and near infrared spectra pretreated by wavelet transform was established. Then, a nonlinear coupled model was built by combining the stepwise regression analysis and generalized regression neural network methods. As a final point, fruit fly optimization algorithm was used for optimizing stepwise regression analysis–generalized regression neural network coupled model. It was found that stepwise regression analysis–generalized regression neural network coupled model coupled model based on the optimization of fruit fly optimization algorithm simplify visible and near infrared spectral data and its prediction results ([Formula: see text] = 0.90, RMSEP = 0.75, mean average percentage error ([Formula: see text]) = 0.05) outperforms original partial least squares model ([Formula: see text] = 0.86, RMSEP = 0.88, [Formula: see text] = 0.06). This work demonstrated the feasibility of using improved chemometric techniques for improving the precision of visible and near infrared spectra in the prediction of microfibril angle.

scholarly journals Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 778 ◽  
Author(s):  
Zhou ◽  
Wu ◽  
Zhou ◽  
Fang ◽  
Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Basal Area ◽  
Tree Age ◽  
Generalized Regression Neural Network ◽  
Independent Variables ◽  
Modeling And Prediction ◽  
Independent Variable ◽  
Generalized Regression ◽  
Better Than

The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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