Rice Area Inter Annual Variation through a Remote Sensing Based Mapping Algorithm
- 1School of Sciences and Engineering, Environmental Engineering, The American University in Cairo, New Cairo 11835,Egypt
- 2Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University, Orman, Giza, Egypt
Keywords: Remote Sensing, Rice, mapping, MODIS, Multi Sensor, Vegetation Indices, Inter annual variation
Abstract. Rice is the main water-consuming crop planted in Egypt Delta. Constrained with the limited water resources, mapping rice is essential for any better water resources management. Xiao (2005) developed an algorithm for rice mapping by studying the dynamics of three vegetation indices the normalized difference vegetation index (NDVI), the Enhanced Vegetation Index (EVI) and the Land surface water index (LSWI). Rice main differentiating feature is being planted in flooded land. Thus moisture sensitive index like LSWI will temporally exceed the EVI or the NDVI signalling rice transplanting. Xiao (2005) utilized MODIS free satellite imagery (500 m spatial resolution). However its coarse resolution combined with the Egyptian complex landscape raised the need for the algorithm modification. In this piece of work a low – cost rice mapping algorithm was developed. The multi resolution (MODIS 250 m red and near infrared bands) and (MODIS 500 m – shortwave infrared and blue bands) were utilized. The arable land was mapped through the utilization of the NDVI and applying it on MODIS 250 m (fine spatial resolution) scenes. The MODIS fine temporal resolution (MOD09A1 product) was utilized to study the LSWI, NDVI and EVI dynamics throughout the rice planting season. The non-arable land from MODIS 250 m was then used to refine the rice area calculated from the MODIS 500 m imagery. The algorithm was applied on the Egypt delta region in years 2008, 2009, and 2010. The mapped rice areas were enhanced from the MODIS 250 m arable mapping module and the results of the algorithm were validated against annual areas reports. There was good agreement between the estimated areas from the algorithm and the reports. Inter annual variation in rice areas was successfully mapped. In addition, the rice area and probable transplanting dates conforms to local planting practices. The findings of this study indicate that the algorithm can be used for rice mapping on a timely and frequent manner.