Comparison of water stress coefficient using three alternative canopy temperature-based indices

Authors: Zhang, Huihui; ZHANG, LIYUAN - Northwest A&f University; NIU, YAXIAO - Northwest A&f University; HAN, MING - University Of Waterloo; Yemoto, Kevin

Submitted to: International Journal of Precision Agricultural Aviation (IJPAA)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/26/2020
Publication Date: 6/28/2020
Citation: Zhang, H., Zhang, L., Niu, Y., Han, M., Yemoto, K.K. 2020. Comparison of water stress coefficient using three alternative canopy temperature-based indices. International Journal of Precision Agricultural Aviation (IJPAA). 3(2):28-34. https://doi.org//10.33440/j.ijpaa.20200302.78.
DOI: https://doi.org/10.33440/j.ijpaa.20200302.78

Interpretive Summary: Three crop canopy temperature-based water stress indices, standard deviation of the distribution of canopy temperature (CTSD), the ratio of canopy temperature of non-stressed to stressed canopy (Tc-ratio) and Degrees Above Non-Stressed (DANS), were tested as the substitute of water stress coefficient (Ks) for maize crop water use estimation. Ground-based thermal imagery was taken from corn under various levels of deficit irrigation in 2015 and 2016 growing seasons. CTSD, Tc-ratio and DANS were calculated from the image derived canopy temperature and converted to water stress coefficient denoted as Ks-CTSD, Tc-ratio, and Ks-DANS. Crop transpiration estimated using three Ks were compared with sap flow measurements in 2015. The results further confirmed that CTSD responded well to irrigation events and was significantly correlated to leaf water potential and soil water deficit, especially when stress level was above moderate. Ks-CTSD was more sensitive to soil water deficit than Tc-ratio and Ks-DANS. Crop transpiration estimated using Ks-CTSD preformed the best among all methods when compared with sap flow measurements. Statistical analysis indicates the performance of the prediction model is satisfactory. CTSD is easy to acquire from high resolution thermal imagery from remote sensing platforms, such as ground and unmanned aerial vehicles; and it doesn't require measurements from non-stressed crop as a reference.

Technical Abstract: In this study three crop canopy temperature-based water stress indices, standard deviation of the distribution of canopy temperature (CTSD), the ratio of canopy temperature of non-stressed to stressed canopy (Tc-ratio) and Degrees Above Non-Stressed (DANS), were tested as the substitute of water stress coefficient (Ks) for maize crop water use estimation. Thermal imagery was taken from maize under various levels of deficit irrigation at different crop growth stages in 2015 and 2016 growing seasons. The Expectation-Maximization algorithm was used to estimate the canopy temperature distribution from thermal imagery under a range of crop coverage and water stress conditions. CTSD, Tc-ratio and DANS were calculated from the extract canopy temperature and converted to water stress coefficient denoted as Ks-CTSD, Tc-ratio, and Ks-DANS. Crop transpiration estimated using three water stress coefficients were compared with sap flow measurements in 2015. The results further confirmed that CTSD responded well to irrigation events (timing and depth) on crops with water stress and was significantly correlated to leaf water potential and soil water deficit, especially when stress level was above moderate. Ks-CTSD was more sensitive to soil water deficit than Tc-ratio and Ks-DANS. Crop transpiration estimated using Ks-CTSD preformed the best among all methods when compared with sap flow measurements (R2_adj =0.58, relative absolute error =0.63, and root mean square error =0.87 mm day-1). Nash-Sutcliffe coefficient of 0.61 indicates the performance of the prediction model is sufficient and satisfactory. The canopy temperature-based index, CTSD, is easy to acquire from high resolution thermal imagery from remote sensing platforms, such as ground and unmanned aerial vehicles. It has strong application potential to improve crop water stress detection and crop water use estimation for irrigation scheduling.