Abstract
Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model.
Highlights
Laos is among the major rice (Oryza sativa L.) consuming countries in South-East Asia [1]
We evaluated the feasibility of using canopy HS data for in-season grain yield evaluations at the reproductive phase of rice
We investigated the performance of the iterative stepwise elimination partial least squares (ISE-Partial least squares (PLS)) model by comparing it with the standard full-spectrum PLS (FS-PLS) model and discussed the importance of the selected wavebands for ISE-PLS
Summary
Laos is among the major rice (Oryza sativa L.) consuming countries in South-East Asia [1]. Rice yield can be predicted by crop growth simulation models whose inputs are related to a wide range of environmental variables (e.g., air temperature and solar radiation) [3], and rice yield is generally related to crop growth and the nitrogen (N) status before the heading stage [4,5]. Indicators related to growth and the N status before the heading stage have been applied in various models to predict yield variation among cultivars [6,7]. In Laos and other developing countries, these data may not be readily available because gathering and accumulating such environmental data is difficult
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