Estimation of herbage biomass and nutritive status using band depth features with partial least squares regression in Inner Mongolia grassland, China. (20th November 2015)

• Record Type: Journal Article
• Authors: Gong, Zhe; Kawamura, Kensuke; Ishikawa, Naoto; Inaba, Mizuki; Alateng, Dalai
• Is Part Of: Grassland science. Volume 62:Number 1(2016:Mar.)
• Page Start: 45
• Page End: 54
• Publication Date: 2015-11-20
• Subjects: Herbage biomass -- hyperspectral -- nutrient status -- partial least squared regression -- steppe recovery; Grasslands -- Periodicals; Grasslands -- Management -- Periodicals; 578.74
• DOI: 10.1111/grs.12112
• Languages: English
• ISSNs: 1744-6961

Abstract

Abstract: Although herbage biomass and nutrient status are widely assessed from hyperspectral measurements, certain difficulties are encountered in semiarid and arid regions with low canopy cover. This study investigated the potential of band depth approaches using partial least squares (PLS) regression to estimate herbage biomass and the concentrations of nitrogen (N) and phosphorus (P) in the Inner Mongolia grassland. Field hyperspectral measurements and plant sampling were conducted in desert and typical steppes with different fertilizer levels. The PLS analyses of typical steppe, desert steppe and combined datasets were based on canopy reflectance and first derivative reflectance (FDR) at wavelengths of 400–1000 nm, with consideration of six band depth features extracted from the red absorption region (580–740 nm). The predictive accuracy of the standard full‐spectrum PLS (FS‐PLS) was compared with that of the iterative stepwise elimination PLS (ISE‐PLS) via the cross‐validated coefficient of determination ( R 2 cv ) and the ratio of prediction to standard deviation (RPD). In most of the datasets, the ISE‐PLS provided better predictive results than the FS‐PLS. The final models used band depth features to estimate herbage biomass ( R 2 cv = 0.624–0.952, RPD = 1.506–4.539) and pasture N ( R 2 cv = 0.437–0.888, RPD = 1.331–2.869) and reflectance and FDR to estimate pasture P ( R 2 cv = 0.686–0.815, RPD = 1.754–2.267). The models could accurately estimate most of the grass parameters (RPD >1.5), with the exception of pasture N concentrations in the desert steppe dataset due to a range of variation that was too small. The band depth approach with ISE‐PLS improved the predictive ability of the method for estimating herbage biomass and the nutrient contents of grasses in sparse grasslands.