Remote Sensing of Evapotranspiration Using SEBAL and Metric Energy Balance Models for Enhanced Precision Agriculture Cotton Irrigation Scheduling

<p>Irrigation scheduling is one of the main factors that affect the crop ability to resist stress symptoms in addition to affecting directly the final yield. In the last decade, many remote sensing methods have been developed to help in scheduling irrigation with higher precision. Some of these methods estimate irrigation needs indirectly such as those using normalized difference vegetation index (NDVI) or crop coefficient curve, and other methods that directly calculate Evapotranspiration (ET) through satellite images. Cotton SmartIrrigation App (Cotton App) is one of the recent applications that have been developed to help farmers in scheduling irrigation during the growing season. The App is based on an interactive ET-based soil water balance model. In this study, remote sensing of Evapotranspiration has been used to detect and map crop water requirements in order to enhance the Cotton App predictions for irrigation schedule during the growing season. Two remote sensing ET models based on thermal infrared (TIR), The surface energy balance algorithm for land (SEBAL) and Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), were used to derive ET over cotton. Results showed higher values of actual Evapotranspiration calculated by both SEBAL and METRIC models during the first 45 days of the growing season compared to the calculated values of ETa from crop coefficient. This is expected to be due to the higher evaporation fraction from bare soil since the plant cover is still very low and accordingly the plant transpiration too. However, later in the second growing stage, the models showed that the crop coefficient calculated ETa (ETa- Calculated) has overestimated the plant Evapotranspiration giving higher values compared to the values from the models. These results indicate that, the use of remote sensing techniques along with the ET-models will increase the app efficiency in giving more precise irrigation scheduling.</p>