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Dear Colleagues,

Surface albedo is a key parameter in the surface energy balance and has been identified as a primary essential climate variable (ECV) that can be used as a diagnostic tool for local climate change, land cover change, etc. With the rapid development of spaceborne satellite remotely sensed sensors and various airborne and near-ground measurement platforms in recent years, there has been great improvement in the spatial resolution of remotely sensed data, which provides an opportunity to investigate subtle surface albedo change in natural and artificial objects in high-resolution ranging from centimeters to tens of meters. The traditional estimation method of surface albedo usually relies on a bidirectional reflectance distribution function (BRDF) reconstructed from multi-angular reflectance, and a direct estimation method based on prior information has also been developed and widely used. However, previous studies mostly dealt with medium-resolution sensors that can capture multi-angular observations, and high-resolution albedo estimation still meets the challenge of lacking multi-angular measurements. To solve this problem, the new algorithms for multi-angular reflectance and albedo determination using remote sensing data are needed. In addition, unmanned aerial vehicle (UAV) and large-scale radiative transfer simulation models are newly developed practical tools to obtain remotely sensed data at multiple scales, which can also assist theory and algorithm development. This Special Issue aims to bring together research on remote sensing of surface BRDF and albedo regarding algorithms, measurements, simulations, variance analysis, and applications. Original research as well as review articles and short communications with a particular focus on remote sensing of BRDF and albedo of various surfaces including vegetation, soil, snow, ice and oceanic surface are welcome for submission.

Dr. Alexander Kokhanovsky
Dr. Xiaoning Zhang
Prof. Dr. Ziti Jiao
Dr. Hu Zhang
Prof. Dr. Tao He
Dr. Anxin Ding
Guest Editors

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Keywords

BRDF
albedo
multi-angle remote sensing
high resolution
estimation
radiative transfer simulation
UAV
measurement
application

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21 pages, 13549 KiB

Open AccessArticle

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

by Zhuo Wang, Haiwei Li, Shuang Wang, Liyao Song and Junyu Chen

Remote Sens. 2024, 16(3), 543; https://doi.org/10.3390/rs16030543 - 31 Jan 2024

Viewed by 694

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing of Surface BRDF and Albedo)

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