Watershed Symposium 2022

Understanding the effect of cloud-based illumination on ET estimation is crucial for the wider application of drone based image collection. We tested the ET estimation under partial cloud and cloud free condition using the image collected by drone and found that the ET are overestimated on the pixel superimposed over cloud shadows.

Agriculture is the largest user of water in Utah and throughout most of the world but you can’t effectively manage what you don’t accurately measure. UAV based imagery are increasingly used for mapping Evapotranspiration (ET) from agricultural fields as they can produce flights on demand and provide higher resolution images. Numerous literature claims that their flexibility to fly on cloudy days and the possibility of capturing higher variability of ET is the added advantage over similar other remote sensing techniques. However, little is known about how cloud based illumination impacts on the ET mapping from agricultural field. Although drones can be flown under the cloud, the sensor's perception of ground data might be impacted by the varying weather conditions. Therefore, understating the effect of cloud on drone based ET estimation is critical for mapping ET. In this research, we estimated and compared the ET under cloudy and non cloudy condition using the images collected by Mica Sense Altum sensor mounted on DJI Matrice 210 Quadacopter on same day. ET was estimated using the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model over three irrigated agricultural fields alfalfa, corn, and peas. Our result shows that ET values are estimated to be higher in the areas superimposed over cloud shadows compared to cloud-free areas. This information will be precious for better and more efficient water management, including precision agriculture.