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Identification of Optimal Hyperspectral Bands for Estimation of Rice Biophysical Parameters
Author(s) -
Wang FuMin,
Huang JingFeng,
Wang XiuZhen
Publication year - 2008
Publication title -
journal of integrative plant biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.734
H-Index - 83
eISSN - 1744-7909
pISSN - 1672-9072
DOI - 10.1111/j.1744-7909.2007.00619.x
Subject(s) - hyperspectral imaging , biomass (ecology) , red edge , environmental science , remote sensing , leaf area index , stepwise regression , agronomy , mathematics , biology , statistics , geology
The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground‐level hyperspectral reflectance measurements for five rice species at three levels of nitrogen fertilization during the growing period. Reflectance was measured in discrete narrow bands between 350 and 2 500 nm. Observed rice biophysical parameters included leaf area index (LAI), wet biomass and dry biomass. The stepwise regression method was applied to identify the optimal bands for rice biophysical parameter estimation. This research indicated that combinations of four narrow bands in stepwise regression models explained 69% to 83% variability for LAI, 56% to 73% for aboveground wet biomass and 70% to 83% for leaf wet biomass. An overwhelming proportion of rice information was in a particular portion of near infrared (NIR) (1 100–1 150 nm), red‐edge (700–750 nm), and a longer portion of green (550–600 nm). These were followed by the moisture‐sensitive NIR (950–1 000 nm), the intermediate portion of shortwave infrared (SWIR) (1 650–1 700 nm), and another portion of NIR (1 000–1 050 nm).