UAV-based multi-angular measurements for improved crop parameter retrieval

Research output: Thesisinternal PhD, WU

Abstract

Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Wageningen University
Supervisors/Advisors
  • Herold, Martin, Promotor
  • Clevers, Jan, Co-promotor
  • Bartholomeus, Harm, Co-promotor
Award date17 Nov 2017
Place of PublicationWageningen
Publisher
Print ISBNs9789463436717
DOIs
Publication statusPublished - 2017

Fingerprint

reflectance
crop
anisotropy
radiative transfer
sensor
remote sensing
geometry
parameter
vehicle

Keywords

  • reflectance
  • anisotropy
  • crops
  • soil water content
  • drones
  • remote sensing

Cite this

@phdthesis{9b1c6c17bdc24b718f403d5e3e7b69ea,
title = "UAV-based multi-angular measurements for improved crop parameter retrieval",
abstract = "Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.",
keywords = "reflectance, anisotropy, crops, soil water content, drones, remote sensing, reflectiefactor, anisotropie, gewassen, bodemwatergehalte, drones, remote sensing",
author = "Roosjen, {Peter P.J.}",
note = "WU thesis 6809 Includes bibliographical references. - With summary in English",
year = "2017",
doi = "10.18174/421562",
language = "English",
isbn = "9789463436717",
publisher = "Wageningen University",
school = "Wageningen University",

}

UAV-based multi-angular measurements for improved crop parameter retrieval. / Roosjen, Peter P.J.

Wageningen : Wageningen University, 2017. 133 p.

Research output: Thesisinternal PhD, WU

TY - THES

T1 - UAV-based multi-angular measurements for improved crop parameter retrieval

AU - Roosjen, Peter P.J.

N1 - WU thesis 6809 Includes bibliographical references. - With summary in English

PY - 2017

Y1 - 2017

N2 - Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

AB - Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

KW - reflectance

KW - anisotropy

KW - crops

KW - soil water content

KW - drones

KW - remote sensing

KW - reflectiefactor

KW - anisotropie

KW - gewassen

KW - bodemwatergehalte

KW - drones

KW - remote sensing

U2 - 10.18174/421562

DO - 10.18174/421562

M3 - internal PhD, WU

SN - 9789463436717

PB - Wageningen University

CY - Wageningen

ER -