Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance

نویسندگان

چکیده

Optical remote sensing (RS) with robust algorithms is needed for accurate assessment of crop canopy features. Despite intensive studies on algorithms, their performance using RS needs to be improved. We evaluated five different (partial-least-squares regression (PLSR), support vector (SVR), random forest (RFR), locally-weighted-PLSR (PLSRLW) and PLSR feature selection (PLSRFS)) rapid leaf area index (LAI) water content (CWC) rice canopies reflectance spectra over visible short-wave infrared region. Two pooled datasets LAI (600) CWC (480) were collected from two replicated field experiments during 2014–15 2015–16 growing season. The each algorithm was coefficient determination (R2). Results showed that PLSRLW performed more accurately than other R2 values 0.77 0.66 CWC, respectively. also used a bootstrapping approach generate kernel density estimator root mean squared error model. results suggested the improvement in prediction accuracy can achieved if suitable selected by assigning higher weights calibration samples, which has similar structure as test sample. Subsetting spectral data large dataset, therefore use entire season should model calibration.

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ژورنال

عنوان ژورنال: Information Processing in Agriculture

سال: 2021

ISSN: ['2214-3173']

DOI: https://doi.org/10.1016/j.inpa.2020.06.002