The Use of Remotely Sensed Data for Estimating of Rice Yield Considering Soil Characteristics

Chiharu Hongo, Gunardi Sigit, Ryohei Shikata, Katsuhisa Niwa and Eisaku Tamura

Journal of Agricultural Science, 2014, vol. 6, issue 7, 172

Abstract: In Indonesia, the food shortage resulting from a rapid increase in population and also the stability of food production largely affected by climate conditions have been serious problems, and it is necessary to improve a cultivation management system aiming at increasing rice yield and also the stability. The purposes of this study are to estimate the rice yield using remote sensing data and field investigation data considering soil characteristics, and also to grasp spatial distribution of the rice yield in wide area. In this study, at first, the relationship between rice yields of different soil types was examined. The soil type of each field could be classified into 3 types, according to the definition of USA soil taxonomy, which are Inceptisol, Ultisol and Entisol. Evaluation on the difference in yield among the soil types was made with the result that there was significant difference between the two- Entiso and Inceptisol. Then, the correlation between the yield and each satellite band and index was examinded about each soil type and the whole soil. As a result, both of Entisol and the whole soil showed significant correlation on all satellite bands and indexes except short-wavelength infrared, but other soil types had little correlation. This suggests that it would be better to use the whole soil without considering soil types for creating the yield estimation formula from SPOT satellite data and the ground investigated yield data. Finally, multiple linear regression analysis was conducted to develop a yield estimation formula, using the investigated rice yield data and also the reflection density of each band, NDVI, GNDVI, NDWI from SPOT satellite data of harvesting season. As a result, the estimation formula was created using NDVI and middle infrared as predictors (r2=0.428, p<0.01, RMSE=0.77t/ha).

Date: 2014
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