Pemilihan Kanal yang Optimal untuk Model Prediksi Kandungan Air Daun Padi dengan Data Field-Spectrometer dan Airborne-Hyperspectral
Agus Wibowo1,2, Bangun Muljo Sukojo2, Teguh Harianto2, Yusuf S. Djajadihardja1
1Pusat Teknologi Inventarisasi Sumberdaya Alam, Badan Pengkajian Penerapan Teknologi,
Jl. MH. Thamrin No 8 Jakarta 10340, E-mail: email@example.com
2Teknik Geomatika Fakultas Teknik Sipil dan Perencanaan ITS, Kampus ITS Sukolilo, Surabaya 60111
Canopy water content (CWC) monitoring for rice field is important to understand the water status on the plant. CWC information also can be used as one input source of rice yield prediction using hyperspectral data. Canopy spectral of paddy rice is measured by field spectrometer and Hyperspectral Mapper (HyMap) sensors that onboard of Cessna airplane in rice field covered Indramayu District, West Java province. Meanwhile, destructive sampling is undertaken in the same time to obtain biophysical parameter such as wet biomass weight and dry biomass weight. Field spectrometer measurement is undertake at distance of 10 cm (FS10) and 50 cm (FS50) above the paddy canopy. Those data is processed using multi linear regression (MLR) and spectral index such as Ratio Spectral Index (RSI), Normalized Difference Spectral Index (NDSI), Soil Adjusted Spectral Index (SASI), and Renormalized Difference Spectral Index (RDSI). The purpose is finding the band which have strong correlation with CWC and will be used for CWC predicting using hyperspectral data. The results of MLR technique show that more band used also resulted higher R2 as well. The result of MLR for FS10 data is 2 bands combination with R2 = 0.704, b1=0,8209 µm, and b2=2,2613 µm; while FS 50 data results 3 bands combination with R2=0.817, b1=0,8067 µm, b2=1.7957 µm, b3=0,8632 µm. While using spectral index shows that RSI technique resulted best coefficient correlation R2. The results of RSI are a) FS10: R2 = 0.8024, b1=1.3070 µm, b2=1.3211 µm; and b) FS50: R2=0.7911, b1=1.5159 µm, b2=2.1725 µm.
Kata kunci: Remote Sensing, Hyperspectral, Canopy Water Content, Field Spectrometer, HyMap, Padi, Indramayu