Influence of polarized reflection on airborne remote sensing of canopy foliar nitrogen content

Liu, Siyuan; Yang, Bin; Zhang, Zihan; Xiang, Yun; Wu, Tong; Zhao, Yunsheng; Zhang, Feizhou

doi:10.1080/01431161.2020.1718242

Cited by 9 publications

(5 citation statements)

References 58 publications

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“…However, with additional prior knowledge, polarization information can reduce uncertainty in estimation of soil contamination levels. Liu et al (2020) illustrate the influence of polarized reflection from the leaf surface on the total canopy scattering and therefore on the estimation of foliar nitrogen content. They use models of polarization to generate a polarization canopy-structure correction method to improve the accuracy of estimates of canopy scattering.…”

Section: Land Surface Polarizationmentioning

confidence: 99%

Optical polarization remote sensing

Chen

Yan

Chandrasekar

2020

International Journal of Remote Sensing

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Section: Land Surface Polarizationmentioning

confidence: 99%

Optical polarization remote sensing

Chen

Yan

Chandrasekar

2020

International Journal of Remote Sensing

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“…The data are from the year 2008 and guarantee good continuity and quality of the observations [22]. This is the latest and most widely used database for modeling of land surface polarization and related studies [4,13,20,21]. The database contains monthly and yearly datasets.…”

Section: A Polder Brdf-bpdf Databasementioning

confidence: 99%

“…Polarized radiation of earth surface is caused primarily by specular reflection that occurs on ground targets [1]. Earth surface characteristics, such as roughness [2], water content [3] and biochemical and structural information of vegetation [4][5][6][7] can be retrieved with the help of either surface polarized reflectance (Rp) or degree of linear polarization (DOLP). On top of that, as a boundary condition, polarized characteristics of land surfaces are essential for retrieval of atmospheric properties, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, unlike Rp, DOLP is wavelength-dependent, indicating its ability to reveal the polarized properties of the targets that are complementary to BRF. For example, DOLP shows different values among vegetation in different agronomic status [4,7,24]. Soybean canopy with and without water stress can be classified using DOLP but cannot be well detected only from BRF measurements [7,12].…”

Section: Introductionmentioning

confidence: 99%

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Degree of Linear Polarization of Land Surfaces: Analyses Using POLDER/PARASOL Measurements

2020

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Polarized reflectance (Rp) and degree of linear polarization (DOLP) provide essential information about polarized characteristics of land surfaces. For a given target, DOLP determines the magnitude of Rp. It has been proved that DOLP can be used for some remote monitoring cases that cannot be well detected with either non-polarized or polarized reflectance. Several bidirectional polarization distribution function (BPDF) models have been proposed in the last several decades to reproduce the angular distribution of Rp, but much less attention has been devoted to modeling and analyzing of DOLP. In this study, the Nadal-Bré on BPDF model was transferred for calculating the DOLP of earth targets, and characteristics of DOLP were analyzed based on the modeling results. To evaluate the model's feasibility, two experiments were executed: a fitting and a a priori modeling. The results showed good correlations (r>0.9) between estimated and measured DOLP when the model was fitted with POLDER/PARASOL (a space-borne multiangle multi-spectral polarimetric sensor) measurements. An increase of accuracy from 490 nm to 865 nm for fitting modeling was achieved and the highest accuracy was found at 865 nm for both experiments, with overall relative root mean square errors of 1.1 and 1.3 for fitting and a priori modeling, respectively. Classbased free parameters can be used for the a priori model of DOLP. The dispersion of the target-based free parameters controls the correlation of the a priori modeling results. Moreover, the maximum DOLP was found to be strongly determined by the corresponding bidirectional reflectance factor for every surface type (R 2 =0.86). This study provides an additional approach for obtaining DOLP from remote sensing platform and is helpful for studies of typical land surfaces.

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“…Object detection has important application value in remote sensing, and the analysis of terrain changes can provide timely information regarding changes in large-scale ground objects on the Earth surface [ 1 , 2 ]. Remote-sensing hyperspectral technology has been widely applied in agriculture, geology, and ecology [ 3 ]. Mastering the surface object information is of substantial significance for improving the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Wang

Gao

et al. 2020

Computational Intelligence and Neuroscience

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To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

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Influence of polarized reflection on airborne remote sensing of canopy foliar nitrogen content

Cited by 9 publications

References 58 publications

Optical polarization remote sensing

Optical polarization remote sensing

Degree of Linear Polarization of Land Surfaces: Analyses Using POLDER/PARASOL Measurements

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

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