Search

The objective of the current work is to generate statistical empirical rice yield estimation models under the local conditions of the Egyptian Nile delta. The methodology is based on regressing measured yield with satellite derived spectral information or leaf area index (LAI). LAI field measurements and spectral information from SPOT data collected during two crop seasons are examined against measured yield to generate the yield models. Near-infrared and red bands, six vegetation indices and LAI of 100 points are used as the main inputs for the modeling process while 20 points of the same are used for validation process. Nine models are generated and tested against the observed yield. Comparing the generated models show relatively higher superiority of (LAI-yield) and (infrared-yield) models over the rest of the models with (0.061) and (0.090) as a standard error of estimate and (0.945) and (0.883) as coefficient of determinations between modeled and observed yield. The models are applicable a month before harvest for similar regions with same conditions.

A research project was conducted as collaboration between the National Authority for Remote Sensing and Space Sciences (NARSS) in Egypt and the Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences. The objective of this study is to generate normalized difference vegetation index (NDVI)–leaf area index (LAI) statistical inversion models for three rice varieties planted in Egypt (Giza-178, Sakha-102, and Sakha-104) using the data of two rice growing seasons. Field observations were carried out to collect LAI field measurements during 2008 and 2009 rice seasons. The SPOT4 satellite data acquired in rice season of 2008 and 2009 conjunction with field observations dates were used to calculate the vegetation indices values. Statistical analyses were performed to confirm the assumptions of inversion modeling for plant variables and to get reliable models that fit the inversion relationship between LAI and NDVI. The inversion process resulted in three NDVI–LAI models adequate to predict LAI with 95% confidence for the three different rice varieties. The accuracy of the generated models ranged between 50% in the case of Sakha-104 and 82% in the case of Giza-178. LAI maps were produced from NDVI imageries based on the generated models.