ASSESSING THEHYPERSPECTRAL REMOTE SENSING DATA TO DIAGNOSIS CROP VARIABLES MODEL IN TROPICAL IRRIGATED WETLAND RICE

Abstract

Canopy spectral measureme~t using ground-based hyperspectral r;levice
and rice crop variables such as leaf area index (LAI), leaf dry weight (LDW) and
SPAD values were done periodically during growth season with involving three
rice cultivars (Pandanwangi, Ciherang and IR Jumbo) ahd four nitrogen
application levels (NO,N80, N92 and NI03 kg/ha). Thestudy is directed to explore
all possible waveband combinations tested in reflectance of vegetation indices
(VIs) and to develop a predictive model of relation between hyperspectral-based
vegetation indiceswith rice crop variables. .
Analysis of all possible two-pair waveband combinations used in VIs was
investigated with 6,786 combinations to gain optimal waveband attributed to crop
variables. To develop.efficient and accurate model, various multivariate regression
models were examined with ten-fold cross validations. Accuracy validation of
predicted model was performed using reflectance and FDR, NDVI, RVI, RDVI and
SA VI data. Validation of predictive model using flJR implied better accuracy to
estimate LAI using whole season data (R2=0.856). Meanwhile, the model using
SA VI denoted highest values (R2=0.852)for predicting LAI While the validation of
predictive model using RVI implied the highest values (K=O. 797) for predicting
LDW. Moreover, the test of predictive model using SAVI indicated the highest
value (R2=0.658) for predicting SPAD values. According to overall validation
using VIs, it seems that RVI has the best accuracy to validate the predictive model
of LAI than those of LDW or SPAD values. Meanwhile, the most significant of K to
validate the predictive model was obtained on FDR data with R2=0.859for LAl