Coupling multiscale remote and proximal sensors for the estimation of crop water requirements

Abstract

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The present study aims to estimate the actual water requirements of crop fields based on the Crop Water Stress Index, combining multiple and multiscale data, such as infrared canopy temperature, air temperature, air relative humidity, near-infrared and thermal infrared image data, taken above the crop field using an innovative aerial micrometeorological station (AMMS), and two more compatible and advanced cameras, a multispectral and a thermal mounted in an Unmanned Aerial Vehicle (UAV), along with satellite-derived thermal data. Moreover, ground micrometeorological stations (GMMS) were installed in each crop. The study area was situated in Trifilia (Peloponnese, Greece) and the experimentation was conducted on two different crops, potato, and watermelon, which are representative cultivations of the area. The analysis of the results showed, in the case of the potato field, that the amount of irrigation water supplied in the rhizosphere far exceeds the maximum crop needs reaching values of about 394% more water than the maximum required amount needed by the crop. Finally, the correlation of the different remote and proximal sensors proved to be sufficiently high while the correlation with the satellite data was moderate. The overall conclusion of this research is that proper irrigation water management is extremely necessary and the only solution for agricultural sustainability in the future. The increasing demand for freshwater, mainly for irrigation purposes, will inevitably lead to groundwater overexploitation and deterioration of the area's already affected and semi-brackish coastal aquifers.