ASSESSING THEHYPERSPECTRAL REMOTE SENSING DATA TO DIAGNOSIS CROP VARIABLES MODEL IN TROPICAL IRRIGATED WETLAND RICE - Muhamad Evri; Muhamad Sadly; Arief Darmawan (Faculty of Geography, Universitas Gadjah Mada, 2008)
Canopy spectral measureme t using ground-based hyperspectral r;leviceand rice crop variables such as leaf area index (LAI), leaf dry weight (LDW) andSPAD values were done periodically during growth season with involving threerice cultivars (Pandanwangi, Ciherang and IR Jumbo) ahd four nitrogenapplication levels (NO,N80, N92 and NI03 kg/ha). Thestudy is directed to exploreall possible waveband combinations tested in reflectance of vegetation indices(VIs) and to develop a predictive model of relation between hyperspectral-basedvegetation indiceswith rice crop variables. .Analysis of all possible two-pair waveband combinations used in VIs wasinvestigated with 6,786 combinations to gain optimal waveband attributed to cropvariables. To develop.efficient and accurate model, various multivariate regressionmodels were examined with ten-fold cross validations. Accuracy validation ofpredicted model was performed using reflectance and FDR, NDVI, RVI, RDVI andSA VI data. Validation of predictive model using flJR implied better accuracy toestimate LAI using whole season data (R2=0.856). Meanwhile, the model usingSA VI denoted highest values (R2=0.852)for predicting LAI While the validation ofpredictive model using RVI implied the highest values (K=O. 797) for predictingLDW. Moreover, the test of predictive model using SAVI indicated the highestvalue (R2=0.658) for predicting SPAD values. According to overall validationusing VIs, it seems that RVI has the best accuracy to validate the predictive modelof LAI than those of LDW or SPAD values. Meanwhile, the most significant of K tovalidate the predictive model was obtained on FDR data with R2=0.859for LAl