Study on Inverting Soil Moisture Content Based on the Field Bidirectional Reflectance Information with the Reflection Model

Yunfeng, 吕云峰 Lü; Zhongqiu, 孙仲秋 Sun; Yunsheng, 赵云升 Zhao

doi:10.3788/aos201535.0328001

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Zhao Yunsheng et al [2] [3] used multi-angle polarization measuring device to measure soil, vegetation and other ground objects, and studied the polarization reflectance characteristics of various objects and related applications. Lv Yunfeng et al [4] used bidirectional reflection model to retrieve soil water content. In the above studies, the backscattering component of the target is ignored, and the model cannot describe the generation process of volume scattering well.…”

Section: Introductionmentioning

confidence: 99%

Model and analysis of six-parameter polarized BRDF for cement board based on genetic algorithm method

WU,

Wu,

HUANG

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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Six-parameter polarized BRDF Models were used to describe spectropolarimetric BRDF of cement board with experimental data. The nonlinear model parameters were obtained by genetic algorithm. The results show that the genetic algorithm has better performance and can get the nonlinear model parameters accurately. Through modeling analysis: cement board within visible spectrum shows the anisotropic characteristic, which is in consistency with the distribution regularity of polarized reflectance ratio factor of the target. In the region of the forward scattering, polarization are greatly influenced by observation geometry. Observing zenith Angle is smaller, the lower the degree of polarization, and with the increase of observing zenith Angle, polarization degree also increased gradually in the the principal plane. In back scattering range, there is also a local hot spot in the direction of the light source.

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Section: Introductionmentioning

confidence: 99%

Model and analysis of six-parameter polarized BRDF for cement board based on genetic algorithm method

WU,

Wu,

HUANG

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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Hyperspectral Inversion of Soil Moisture Content Based on SOILSPECT Model

Yao

Liu

Gao

et al. 2018

2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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Study on the Effect of Surface Roughness on the Spectral Unmixing of Mixed Pixels

Zhang

Luo

et al. 2020

Journal of Spectroscopy

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In the spectrum measurement experiment, the roughness of the object surface is an essential factor that cannot be ignored. In this experiment, a group of mixed pixel samples with different mixing ratios were designed, and these samples were printed on four kinds of papers with different roughness. The spectral characteristics of mixed pixels with different roughness are quantitatively analyzed by using the measured spectral data. The linear spectral mixture model is used for spectral decomposition, and the effect of roughness on the unmixing precision of mixed pixels was studied. The surface roughness will affect the reflectivity of the mixed pixel. Specifically, the higher the roughness is, the higher the reflectivity of the sample is. This phenomenon is more noticeable when the proportion of white endmember (PWE) is large, and as the white area ratio decreases, the reflectance difference gradually decreases. When the surface roughness of the sample is less than 3.339 μm, the spectral decomposition is performed using a linear spectral mixing model in the visible light band. The average error of the unmixing is less than 0.53%, which is lower than the conventional standard spectral measurement error. In other words, when the surface roughness of the sample is controlled within a specific range, the effect of roughness on the unmixing accuracy of the mixed pixels is small, and this effect can be almost ignored. Multiple scattering within the pixels is the key to model selection and unmixing accuracy, when using the ASD FieldSpec3 spectrometer to perform spectral reflectance measurement and linear spectral unmixing experiments. If the surface roughness of the sample to be measured is less than the maximum wavelength of the spectrometer, the experimental results believe that the photon energy is mainly mirror reflection on the surface of the object and diffuse reflection. At this time, it is still a better choice to use a linear spectral mixing model to decompose the mixed pixels.

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Study on Inverting Soil Moisture Content Based on the Field Bidirectional Reflectance Information with the Reflection Model

Cited by 3 publications

References 9 publications

Model and analysis of six-parameter polarized BRDF for cement board based on genetic algorithm method

Model and analysis of six-parameter polarized BRDF for cement board based on genetic algorithm method

Hyperspectral Inversion of Soil Moisture Content Based on SOILSPECT Model

Study on the Effect of Surface Roughness on the Spectral Unmixing of Mixed Pixels

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