Estimating biomass of rice in farmers' fields by red-edge indices

Martin Leon Gnyp, Kang Yu, Yuxin Miao, Georg Bareth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biomass is an important parameter that has a decisive influence on the final yield. Destructive measurements of biomass are time-consuming and labor-intensive. Proximal sensing methods using field spectrometers offer indirect observation and estimation of biomass. For this purpose, farmers' fields were investigated in a two-year growing season of rice and canopy reflectance was measured by spectrometers. Several vegetation indices (VIs) and multiple linear regression (MLR) models based on bands around the red-edge domain (680-760 nm) were tested. Published rededge VIs were generally prone to saturation, whereas MLR models and the Ratio of Reflectance Difference Index in the red-edge (RRDIre) were less influenced by saturation. The linearly tested MLR (based on VIs) and the RRDIre models provided the best performance for biomass estimation in model validation using an independent dataset.

Original languageEnglish (US)
Title of host publication2014 6th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2014
PublisherIEEE Computer Society
ISBN (Electronic)9781467390125
DOIs
StatePublished - Jun 28 2014
Event6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 - Lausanne, Switzerland
Duration: Jun 24 2014Jun 27 2014

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2014-June
ISSN (Print)2158-6276

Other

Other6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
CountrySwitzerland
CityLausanne
Period6/24/146/27/14

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