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

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dc.contributor.authorPanigrahi, Niranjan
dc.contributor.authorDas, Bhabani Sankar
dc.date.accessioned2023-05-23T10:16:03Z
dc.date.available2023-05-23T10:16:03Z
dc.date.issued2021-08-11
dc.description.abstractOptical 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 algorithms (partial-least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), locally-weighted-PLSR (PLSRLW) and PLSR with feature selection (PLSRFS)) for rapid assessment of leaf area index (LAI) and canopy water content (CWC) for rice canopies using canopy reflectance spectra over visible to short-wave infrared region. Two pooled datasets of LAI (600) and CWC (480) were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season. The performance of each algorithm was evaluated using coefficient of determination (R2). Results showed that PLSRLW performed more accurately than other algorithms with R2 values 0.77 and 0.66 for LAI and CWC, respectively. We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model. The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples, which has similar canopy structure as the test sample. Subsetting of the canopy spectral data results large error values in test dataset, therefore the use of entire season canopy spectral data should be used for model calibration.en_UK
dc.identifier.citationPanigrahi N, Das BS. (2021) Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance. Information Processing in Agriculture, Volume 8, Issue 2, June 2021, pp. 284-298en_UK
dc.identifier.issn2214-3173
dc.identifier.urihttps://doi.org/10.1016/j.inpa.2020.06.002
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19700
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRice canopyen_UK
dc.subjectReflectance spectroscopyen_UK
dc.subjectCalibrationen_UK
dc.subjectRetrieval algorithmsen_UK
dc.subjectPartial-least-squares regressionen_UK
dc.titleEvaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectanceen_UK
dc.typeArticleen_UK

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