Crop water stress maps for an entire growing season from visible and thermal UAV imagery

Abstract

Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red-green-blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green-red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

“…Visible light VIs, such as the NGRDI, are often used to characterize vegetation if NIR information is lacking (Pérez et al 2000; Meyer and Neto 2008; Raymond et al 2005). Due to their low costs and low weight, consumer-grade true colour (RGB) digital cameras are particularly suitable for assessing green vegetation using UAS-based imaging systems (Torres-Sánchez et al 2014; Saberioon et al 2014; Hoffmann et al 2016a; Goodbody et al 2017; Jannoura et al 2015). Rasmussen et al (2016) evaluated the reliability of four VIs (ExG, NGRDI, NDVI, ENDVI) derived from consumer-grade RGB as well as CIR (colour-infrared) cameras mounted on UAS.…”

“…Even though CIR cameras are sometimes recommended rather than RGB cameras, they found no clear advantage of CIR images and concluded that RGB cameras are powerful tools for assessing green vegetation. Hoffmann et al (2016a) used the NGRDI based on UAS imagery to assess surface greenness of barley fields for the detection of crop water stress. In their study, they found a medium–strong correlation between the NGRDI and the NDVI and concluded that their results bode well for the use of the NGRDI as a greenness index.…”

Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods

“…The UAV instrumentation has previously been tested to derive drought stress in agricultural crops such as barley [54], olive [4,5], and fruit species [16]. UAV was also applied to study vegetation index linkages to water stress in grapevines [55] but not yet to investigate CWSI in a grapevine crop, as done in the present research and other studies [14,15].…”

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

“…Finally, it should be remembered that while correlated, our definition of vigour is fundamentally different from the definition of the NDVI detected by the GreenSeeker sensor. This difference may also be a contributing factor to its lower correlation with image-based vigour measurement by both MGP and UAV imaging systems [29].…”

“…In this study, GRVI has been used to represent and proportionally quantify plant vigour. The index normalizes for variations in light intensities, has been a tested indicator of chlorophyll content in several crops and is shown to be positively correlated with traits such as biomass [28] and leaf area index [29], a quantity related to plant vigour. In this study, images of plant canopies are captured in the RGB channels, making an RGB-derived index suitable to represent vigour by both MGP and UAV.…”