Estimating actual crop evapotranspiration using deep stochastic configuration networks model and UAV-based crop coefficients in a pomegranate orchard
Haoyu Niu, Dong Wang, YangQuan Chen
Abstract
Evapotranspiration (<i>ET</i>) estimation is important agricultural research in many regions because of the water scarcity, growing population, and climate change. <i>ET</i> can be analyzed as the sum of evaporation from the soil and transpiration from the crops to the atmosphere. The accurate estimation and mapping of <i>ET</i> are necessary for crop water management. One traditional method is to use the crop coefficient (<i>K<sub>c</sub></i>) and reference <i>ET</i> (<i>ET<sub>o</sub></i>) to estimate actual <i>ET</i>. With the advent of satellite technology, remote sensing images can provide spatially distributed measurements. Satellite images are used to calculate the Normalized Difference Vegetation Index (NDVI). The relation between NDVI and <i>K<sub>c</sub></i> is used to generate a new <i>K<sub>c</sub></i>. The spatial resolution of multispectral satellite images, however, is in the range of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Moreover, the frequency of satellite overpasses is not high enough to meet the research or water management needs. The Unmanned Aerial Vehicles (UAVs) can help mitigate these spatial and temporal challenges. Compared with satellite imagery, the spatial resolution of UAV images can be as high as centimeter-level. In this study, a regression model was developed using the Deep Stochastic Configuration Networks (DeepSCNs). Actual evapotranspiration was estimated and compared with lysimeter data in an experimental pomegranate orchard. The UAV imagery provided a spatial and tree-by-tree view of <i>ET</i> distribution.