Rice yield forecasting models using satellite imagery in Egypt

• DOI: 10.1016/j.ejrs.2013.04.005
• Publication Date: 2013-05-18T21:30:57Z
• Authors: N.A. Noureldin; M.A. Aboelghar; H.S. Saudy; A.M. Ali
• Keywords: Spectral bands; Enhanced vegetation index

Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different ratios form vegetation indices (VI) with leaf area index (LAI) were used generate remotely sensed pre-harvest empirical rice models. LAI measurements, spectral data derived from two SPOT acquired on August 24, 2008 23, 2009 observed as main inputs for modeling. Each factor was separately combination the The results showed that green band, middle infra-red (GVI) did not show sufficient capability estimators while other such red near infrared are algebraic these bands when or combined produced high accurate estimation validation process carried out statistical tests; standard error estimate correlation coefficient between modeled predicted yield. indicated normalized difference (NDVI) model highest accuracy stability during seasons. generated models applicable 90 days after planting any similar environmental conditions practices.