Combining ground-based data and MODIS data for rice crop estimation in Indonesia

In this study, ground based data from spectroradiometer International Light type ILT900 combined with remotely sensed data from MODIS (Moderate Resolution Imaging Spectrometer) sensor of experimental farmland of the Ministry of Agriculture Republic of Indonesia in Sukamandi, Subang, West Java were used as input data for rice crop estimation using regression analysis. We chose four spectral bands (1-4) of MODIS data and four spectral bands of spectroradiometer data with same (the most similar) wavelength with chosen MODIS data. In addition to the spectral reflectance measurements, we also measured rice production data from several 7 × 20 plot areas that contain different rice varieties and different fertilizer compositions. The data from spectroradiometer then used for estimating regression model based on two approaches, Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). The evaluation on ground-based data shows that PCR and PLSR give good accuracy with r2 = 0.968 and 0.984 respectively.