Reducing BRDF Effects on the Estimation of Leaf Biochemical Parameters Using the Nonpolarized Reflectance Factor in the Hemispheric Space

Yao, Ce; Sun, Zhongqiu; Lu, Shan

doi:10.1109/tgrs.2022.3175721

Cited by 4 publications

(3 citation statements)

References 83 publications

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“…During the period from 4 January 2022 to 11 June 2023, a total of six periods of multispectral imagery were collected using the P4M UAV, including five rainy seasons and one dry season, as shown in Table S1. To minimize the effects of water surface specular reflections [38] and sky clouds [35] on multispectral data, six UAV flight missions were conducted under clear-sky and low-wind-speed (<5 m/s) conditions between the local time of 9:00-11:00 and 14:00-16:00, with altitudes around 100 m [39]. In addition, to meet the requirements of multispectral image splicing afterward, the course overlap and the side overlap were set at 80% and 70%, respectively.…”

Section: Methods 221 Multispectral Data Collection and Preprocessingmentioning

confidence: 99%

Section: Multispectral Data Collection and Preprocessingmentioning

confidence: 99%

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An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

Lei,

Jiang,

Deng

et al. 2024

Remote Sensing

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Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including CODMn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA’s superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, CODMn, and TP, with an R2 of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and CODMn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management.

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Section: Methods 221 Multispectral Data Collection and Preprocessingmentioning

confidence: 99%

Section: Multispectral Data Collection and Preprocessingmentioning

confidence: 99%

An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

Lei,

Jiang,

Deng

et al. 2024

Remote Sensing

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“…The Bidirectional Reflectance Distribution Function (BRDF) is defined as the ratio of the irradiance in a given outgoing direction to the irradiance in a given incoming direction on that surface element, and this function can be used to characterize the surface reflectance anisotropy [1,2]. Currently, it is mainly applied to radiometric correction of satellite-borne instruments [1], radiometric correction of low-altitude images [3], light utilization, albedo inversion [4,5], crop mapping [6], crop element estimation [7], and estimation of metal surface roughness [8]. The modeling accuracy of the BRDF has directly impacted the application of quantitative remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Methodology and Modeling of UAV Push-Broom Hyperspectral BRDF Observation Considering Illumination Correction

Wang,

Li,

Wang

et al. 2024

Remote Sensing

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The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV push-broom hyperspectral imaging is limited by the push-broom imaging method, and the multi-angle information is often difficult to obtain. In addition, the random variation of solar illumination during UAV low-altitude flight makes the irradiance between different push-broom hyperspectral rows and different airstrips inconsistent, which significantly affects the radiometric consistency of BRDF modeling and results in the difficulty of accurately portraying the three-dimensional spatial reflectance distribution in the UAV model. These problems largely impede the application of outdoor BRDF. Based on this, this paper proposes a fast multi-angle information acquisition scheme with a high-accuracy BRDF modeling method considering illumination variations, which mainly involves a lightweight system for BRDF acquisition and three improved BRDF models considering illumination corrections. We adopt multi-rectangular nested flight paths for multi-gray level targets, use multi-mode equipment to acquire spatial illumination changes and multi-angle reflectivity information in real-time, and introduce the illumination correction factor K through data coupling to improve the kernel, Hapke, and RPV models, and, overall, the accuracy of the improved model is increased by 20.83%, 11.11%, and 31.48%, respectively. The results show that our proposed method can acquire multi-angle information quickly and accurately using push-broom hyperspectral imaging, and the improved model eliminates the negative effect of illumination on BRDF modeling. This work is vital for expanding the multi-angle information acquisition pathway and high-efficiency and high-precision outdoor BRDF modeling.

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A New Method to Estimate the Leaf Chlorophyll Content From Multiangular Measurements: Anisotropy Index

Sun

Omasa

2023

IEEE Trans. Geosci. Remote Sensing

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Reducing BRDF Effects on the Estimation of Leaf Biochemical Parameters Using the Nonpolarized Reflectance Factor in the Hemispheric Space

Cited by 4 publications

References 83 publications

An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery

Methodology and Modeling of UAV Push-Broom Hyperspectral BRDF Observation Considering Illumination Correction

A New Method to Estimate the Leaf Chlorophyll Content From Multiangular Measurements: Anisotropy Index

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