Crop water stress maps for entire growing seasons 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 the late growing season are included to investigate whether the WDI index 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 index 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 index is assessed. Final WDI maps had a spatial resolution of 0.25 m. It was found that the UAV-based WDI index determines accurate crop water status. Further, the WDI index 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 index accounts for areas of ripe crops that no longer have the same need of 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 seasons.

Plant phenotyping relevance

UAVの可視・熱画像からWDIを算出し、作物の水分状態・水ストレスをマッピングする取得・解析手法が研究の中心であり、精度と適用性も評価している。

Code and data availability

The paper's UAV thermal/RGB imagery, NGRDI and WDI maps, and validation data are paper-specific phenotyping assets, but the article states they are only available upon request from the corresponding author; no public repository, code deposit, or authors' public URL is provided. The allowed URLs in the reference list (d

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