Scholarly record
OPTIMIZATION OF CROP YIELD ESTIMATION BASED ON REMOTE SENSING AND NEW MATHEMATICAL MODEL
Abstract
Nowadays crop management seeks techniques and practices to enhance production and to optimize the use of natural resources. Most of the developing countries need reliable techniques which can help in increasing production to meet the demand for food. The techniques should be able to forecast crop yield and to detect problems ahead regardless of the technical or the natural conditions. Geospatial technologies including remote sensing play a vital role in mapping and in monitoring crops. However due to many obstacles such as climate conditions and low temporal resolution that hinder the effectiveness of these tools, a new mathematical model is created to compensate for the missing data. Based on this model a new method for crop yield estimation is implemented that includes the use of Mapping Evapotranspiration at High Resolution using Internalized Calibration (METRIC) to improve the estimation process in a large agricultural area. To verify the results of the new method, several potato farmers are interviewed for information about their crops yield. In addition, two pilot areas planted with potato are monitored using remote sensing and Bowen ratio stations from the seeding stage until harvesting. The comparison between the estimated potato crop yield and the actual production in different potato fields proves that the accuracy is between 95% and 97%.
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