BRDF Correction

Zhuosen Wang; Crystal Schaaf

doi:10.1016/B978-0-443-13220-9.00057-3

BRDF Correction

Zhuosen Wang
, Crystal Schaaf

SFE - Dean's Office

University of Maryland, College Park
NASA Goddard Space Flight Center

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The land surface is not Lambertian and instead exhibits strong anisotropic characteristics. Therefore, the Bidirectional Reflectance Distribution Function (BRDF) correction is crucial to ensure accurate, consistent, and comparable reflectance measurements. A directional correction leads to improved classification, time-series analysis, and multi-sensor data integration. In this chapter, we describe the characterization of BRDF. BRDF models, including empirical, physical, semi-empirical, and machine learning methods, are introduced. We present several BRDF correction applications based on BRDF models.

Original language	English
Title of host publication	Comprehensive Remote Sensing-
Publisher	Elsevier
Pages	V2:29-V2:37
ISBN (Electronic)	9780443132209
ISBN (Print)	9780443239496
DOIs	https://doi.org/10.1016/B978-0-443-13220-9.00057-3
State	Published - Jan 1 2025

ASJC Scopus Subject Areas

General Engineering
General Environmental Science

Keywords

Anisotropic reflectance
BRDF
Canopy structure
Hotspot
Land cover
Landsat
Machine learning
MODIS
NBAR
Phenology
Time-series analysis
Vegetation index
VIIRS

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10.1016/B978-0-443-13220-9.00057-3

Cite this

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abstract = "The land surface is not Lambertian and instead exhibits strong anisotropic characteristics. Therefore, the Bidirectional Reflectance Distribution Function (BRDF) correction is crucial to ensure accurate, consistent, and comparable reflectance measurements. A directional correction leads to improved classification, time-series analysis, and multi-sensor data integration. In this chapter, we describe the characterization of BRDF. BRDF models, including empirical, physical, semi-empirical, and machine learning methods, are introduced. We present several BRDF correction applications based on BRDF models.",

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AU - Wang, Zhuosen

AU - Schaaf, Crystal

PY - 2025/1/1

Y1 - 2025/1/1

N2 - The land surface is not Lambertian and instead exhibits strong anisotropic characteristics. Therefore, the Bidirectional Reflectance Distribution Function (BRDF) correction is crucial to ensure accurate, consistent, and comparable reflectance measurements. A directional correction leads to improved classification, time-series analysis, and multi-sensor data integration. In this chapter, we describe the characterization of BRDF. BRDF models, including empirical, physical, semi-empirical, and machine learning methods, are introduced. We present several BRDF correction applications based on BRDF models.

AB - The land surface is not Lambertian and instead exhibits strong anisotropic characteristics. Therefore, the Bidirectional Reflectance Distribution Function (BRDF) correction is crucial to ensure accurate, consistent, and comparable reflectance measurements. A directional correction leads to improved classification, time-series analysis, and multi-sensor data integration. In this chapter, we describe the characterization of BRDF. BRDF models, including empirical, physical, semi-empirical, and machine learning methods, are introduced. We present several BRDF correction applications based on BRDF models.

KW - Anisotropic reflectance

KW - BRDF

KW - Canopy structure

KW - Hotspot

KW - Land cover

KW - Landsat

KW - Machine learning

KW - MODIS

KW - NBAR

KW - Phenology

KW - Time-series analysis

KW - Vegetation index

KW - VIIRS

UR - https://www.scopus.com/pages/publications/105028828027

UR - https://www.scopus.com/pages/publications/105028828027#tab=citedBy

U2 - 10.1016/B978-0-443-13220-9.00057-3

DO - 10.1016/B978-0-443-13220-9.00057-3

M3 - Chapter

AN - SCOPUS:105028828027

SN - 9780443239496

SP - V2:29-V2:37

BT - Comprehensive Remote Sensing-

PB - Elsevier

ER -

BRDF Correction

Abstract

ASJC Scopus Subject Areas

Keywords

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