Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression

Abstract

Diagnosis of rice growth and nutrient status is critical for prediction of rice yield and grain quality and prescription of nitrogen topdressing at panicle initiation stage. Two experiments, one in 2000 and one in 2003 were conducted to construct a partial least square model for assessing rice leaf growth and nitrogen status non-destructively at the Experimental Farm (37°16′N, 126°59′E) of Seoul National University, Suwon, Korea. The experiment included two cultivars (Hwasungbyeo and Dasanbyeo) and four levels of nitrogen (N) application in year 2000 and four rice cultivars (Hwasungbyeo, SNU-SG1, Juanbyeo, and Surabyeo) and 10 N treatments in year 2003. Hyperspectral canopy reflectance (300–1100 nm) data recorded at various growth stages before heading were used for partial least square regression (PLS) model to predict four crop variables: leaf area index, leaf dry weight, leaf N concentration, and leaf N density. Three hundred and forty two observations from two experiments were randomly split for model calibration (75%) and validation (25%). Coefficient of determination ( R 2), root mean square error in prediction (RMSEP) and relative error of prediction (REP) of model calibration and validation were calculated for the model quality evaluation. The results revealed that PLS model using hyperspectral canopy reflectance data to predict four plant variables produced an acceptable model precision and accuracy. The model R 2 and REP ranged from 0.84 to 0.87 and 10.0 to 23.8% for calibration and 0.79 to 0.84 and 11.1 to 25.6% for validation, respectively. The most important reflectance as judged by factor loading in PLS model for rice leaf characterization was at various bands such as near-infrared (>760 nm) and visible (355, 420, 524–534, 583 and 687 nm) and red edge (707 nm) region.