The estimation of Leaf area index (LAI) becomes important as LAI is one of parameters in the analysis of crop growth model. The crop growth has different characteristics and its strongly influenced by environmental conditions and factors. The growth tends to occur in a short period of time and covers a large area. Therefore, an approach to analyze the pattern of changes in crop growth based on LAI spatially is needed. Remote sensing offers an effective and efficient approach in monitoring crop growth characteristic, which can be done in a time series with a wide area coverage by detecting and monitoring the physical characteristics of crop. The most famous and commonly used parameters to estimate LAI are vegetation indices which are usually calculated based on the ratio of the red and NIR wavelength, known as spectral signature. The objectives of the research are to examine the spatio-temporal correlation between LAI of three rice cultivars Sentinel-2A based vegetation indices, and to select the most optimum vegetation index in estimating LAI. A synchronization process of the LAI for each plot with the pixel of Sentinel-2A based vegetation index value was carried out. The results of the analysis show that the vegetation index has a strong correlation with LAI. The Comparison of the four calculated vegetation indices in estimating LAI was performed using linear regression model and followed by comparing R-squared, RMSE and Correctness. The EVI2 vegetation index provides the most optimum representation in capturing crop growth patterns based on LAI.
Serntinel 2A provide Normalized Difference Vegetation Index to be used as an estimate of soil fertility, plant varieties and productivity. The weakness of satellite data is that the data obtained is often inaccurate due to cloud cover, especially in tropical countries with high rainfall such as Indonesia. The use of unmanned aerial vehicle as an alternative data have limitation as it captured RGB imagery. The research was conducted from July to September 2020 at Pasir Kaliki Village, District of Rawamerta, Karawang Regency, West Java province. The study has discovered that NDVI showed higher number in result of vegetation index compared to NGRDI with correlation coefficient is 0.944625. The regression model resulted as y=4.7722x+0.3845 and MAPE value expresses as 26.74%, where the regression model with Pearson’s correlation coefficient value is 0.877885. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice vegetation map reveal a high mapping accuracy with the corresponding R2 being as high as 0.7429; however, the mapped rice vegetation accuracy might influenced by other physical factors such as water reflectant, sunlight and the RGB camera limitation itself. Nonetheless, the highest values of NGRDI only reach 0.2 while NDVI can attain at 0.9 at the peak of vegetative phase of rice growth stage. This means that Green Band have limitation in detecting vegetation index. In relation to the different approaches performed, it is noted that the average trend line on both NDVI and NGRDI shown the similarity tendency in all growth stage.
Indonesia has a potency for planting Nikomaru, a japonica rice cultivar that has a capability for tolerating a high air temperature due to a chance for international trading, mainly to Japan. Developing a crop model to know the potency of Nikomaru in Indonesia based on the climate condition is an easier step than doing direct planting. A Decision Support System (DSS) was expected to help Indonesian farmers to decide their plantation. A field experiment was needed to develop and evaluate a crop model for predicting rice production. A web-based DSS developed for simulating some scenarios to know the potency of Nikomaru in West Java Province, Indonesia. Bogor Regency and Bandung Regency were selected area due to a higher rice production than the other places. Both of them would face dry periods. Bandung Regency will face the worst dry period in the first scenario.
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