Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring

Liyuan Zhang; Yaxiao Niu; Huihui Zhang; Wenting Han; Guang Li; Jiandong Tang; Xingshuo Peng

doi:10.3389/fpls.2019.01270

Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring

Front Plant Sci. 2019 Oct 9:10:1270. doi: 10.3389/fpls.2019.01270. eCollection 2019.

Authors

Liyuan Zhang^{1

2}, Yaxiao Niu^{1

2}, Huihui Zhang³, Wenting Han^{1

4}, Guang Li^{1

2}, Jiandong Tang⁵, Xingshuo Peng^{1

2}

Affiliations

¹ College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China.
² Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling, China.
³ Water Management and Systems Research Unit, USDA-ARS, Fort Collins, CO, United States.
⁴ Institute of Soil and Water Conservation, Northwest A&F University, Yangling, China.
⁵ College of Resources and Architectural Engineering, Northwest A&F University, Yangling, China.

Abstract

To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red-green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 (n = 15) and root mean square error value of 0.7°C. The RGRI-Otsu method was most accurate for estimating temperatures around 32.9°C, but the magnitude of residuals increased above and below this value. This phenomenon may be attributable to changes in canopy cover (leaf curling) under water stress, resulting in changes in the proportion of exposed sunlit soil in UAV thermal orthophotographs. Therefore, to improve the accuracy of maize canopy detection and extraction, optimal methods and better strategies for eliminating mixed pixels are needed. This study demonstrates the potential of using high-resolution UAV RGB images to supplement UAV thermal images for the accurate extraction of maize Tc.

Keywords: Otsu algorithm; leaf area index; nearest neighbor algorithm; red-green ratio index; soil water content; stomatal conductance.