Use of laboratory hyperspectral reflectance data of soils for predicting their diurnal albedo dynamics accomodating their roughness

Jerzy Cierniewski, Jakub Ceglarek, Arnon Karnieli, Slawomir Krolewicz, Cezary Kazmierowski, Bogdan Zagajewski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and its albedo, measured under various roughness conditions. 108 soil surfaces measurements were conducted in Poland and Israel. Each surface was characterized by its diurnal albedo variation in the field as well as its reflectance spectra that was obtained in the laboratory. The best fit to the model was achieved by postprocessing manipulation of the spectra, namely second derivate transformation. Using stepwise elimination process, four spectral wavelengths, as well as roughness index, were selected for modeling. The resulted models allow predicting the albedo of a soil at specific roughness for any solar zenithal angle, provided that hyperspectral reflectance data is available.

Original languageEnglish
Title of host publication2016 8th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2016
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781509006083
DOIs
StatePublished - 28 Jun 2016
Event8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 - Los Angeles, United States
Duration: 21 Aug 201624 Aug 2016

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume0
ISSN (Print)2158-6276

Conference

Conference8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Country/TerritoryUnited States
CityLos Angeles
Period21/08/1624/08/16

Keywords

  • Albedo
  • Bare soil
  • Hyperspectral reflectance
  • Roughness

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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