Hyperspectral reflectance measurements from UAS under intermittent clouds: Correcting irradiance measurements for sensor tilt
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Hyperspectral reflectance measurements from UAS under intermittent clouds: Correcting irradiance measurements for sensor tilt
Authors
Keywords
Downwelling irradiance, Unmanned Aerial Systems (UAS), Hyperspectral remote sensing, Data correction, Sensor tilt, Calibration, Fluctuating light
Journal
REMOTE SENSING OF ENVIRONMENT
Volume 267, Issue -, Pages 112719
Publisher
Elsevier BV
Online
2021-10-05
DOI
10.1016/j.rse.2021.112719
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Leonardo spaceborne infrared payloads for Earth observation: SLSTRs for Copernicus Sentinel 3 and PRISMA hyperspectral camera for PRISMA satellite
- (2020) Peter Coppo et al. APPLIED OPTICS
- Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline
- (2019) P.J. Zarco-Tejada et al. REMOTE SENSING OF ENVIRONMENT
- Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition
- (2019) Sheng Wang et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application
- (2018) Helge Aasen et al. REMOTE SENSING OF ENVIRONMENT
- Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization
- (2018) Teemu Hakala et al. SENSORS
- Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows
- (2018) Helge Aasen et al. Remote Sensing
- Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models
- (2018) Jingjing Cao et al. Remote Sensing
- On the Use of Unmanned Aerial Systems for Environmental Monitoring
- (2018) Salvatore Manfreda et al. Remote Sensing
- A Novel Tilt Correction Technique for Irradiance Sensors and Spectrometers On-Board Unmanned Aerial Vehicles
- (2018) Juha Suomalainen et al. Remote Sensing
- Juvenile tree classification based on hyperspectral image acquired from an unmanned aerial vehicle
- (2016) Jin Huang et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data
- (2016) Haiying Liu et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities
- (2015) Christine M. Lee et al. REMOTE SENSING OF ENVIRONMENT
- The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands—Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements
- (2015) Mitsunori Ishihara et al. Remote Sensing
- HyperUAS-Imaging Spectroscopy from a Multirotor Unmanned Aircraft System
- (2014) Arko Lucieer et al. Journal of Field Robotics
- On the vertical structure of wind gusts
- (2014) I. Suomi et al. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
- Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)
- (2013) P.J. Zarco-Tejada et al. AGRICULTURAL AND FOREST METEOROLOGY
- A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy
- (2013) Andreas Burkart et al. IEEE SENSORS JOURNAL
- Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission
- (2009) L. Guanter et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Turbulence structure above a vegetation canopy
- (2009) JOHN J. FINNIGAN et al. JOURNAL OF FLUID MECHANICS
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started