Abstract
Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.
Highlights
Remote sensing has proved to be game changing for the agricultural sector, as it is one of the backbones of precision in agriculture [1]
Bowen Ratio areare installed in the in order to measure the different climate parameters which are necessary for irrigation management, for measure the different climate parameters which are necessary for irrigation management, for mapping mapping actual evapotranspiration, and for estimating crop yield 1)
It is proved in this research that crop yield estimation can be improved if reliable model exists to help in overcoming all obstacles which hinder crop yield estimation processes
Summary
Remote sensing has proved to be game changing for the agricultural sector, as it is one of the backbones of precision in agriculture [1]. Multispectral and hyperspectral satellite images play a major role in crop management; their ability to represent crop growth conditions on the spatial and temporal scale is remarkable. These images can describe the crop development, photosynthetic active radiation (PAR), biomass accumulation (Bio), leaf area index (LAI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (ENDVI), Perpendicular. Many approaches have been developed to translate remote sensing data into crop yields, and several reviews of such approaches exist [6,7,8]. Most of the time climatic conditions and low temporal resolution are the main obstacles that prevent decision makers from using remote sensing data to map crops and to estimate crop yields
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