A Study toward the Evaluation of ALOS Images for LAI Estimation in Rice Fields
For expanding and managing agricultural sources, satellite data have a key role in determining required information about different factors in plants Including Leaf Area Index (LAI).This paper has studied the potential of spectral indices in estimating rice canopy LAI in Amol city as one of the main sources of rice production in Iran. Due to its importance in provision of food and calorie of a major portion of population, rice product was chosen for study. A field campaign was conducted when rice was in the max growth stage (late of June). Also, two satellite images from ALOS-AVNIR-2 were used (simultaneous with conducted field works) to extract and determine vegetation indices. Then the Regression between measured data and vegetation indices, derived from combination of different bands, was evaluated and after that suitable vegetation indices were realized. Finally, statistics and calculations for introduction of a suitable model were presented. After examination of models, the results showed that RDVI and SAVI2, by determination coefficient and RMSE of 0.12–0.59 and 0.24–0.62, have more accuracy in LAI estimation. Results of present study demonstrated the potential of ALOS images, for LAI estimation and their significant role in monitoring and managing the rice plant.