Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images

• Published in: Agricultural water management Vol. 264; p. 107506
• Main Authors: Zhang, Liyuan, Zhang, Huihui, Han, Wenting, Niu, Yaxiao, Chávez, José L., Ma, Weitong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30.04.2022
• Subjects: Canopy temperature; Crop water stress index; Deficit irrigation; Inter-field variability; UAV RGB images; Crop water stress index; Deficit irrigation; Inter-field variability; UAV RGB images; Canopy temperature
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2022.107506

To further explore the performance of the newly proposed crop water stress indicator - the Mean Value of Gaussian Distribution of Excess Green index (MGDEXG) for maize canopy in RGB imagery, studies were conducted in two maize fields with different irrigation levels during the 2015 and 2019 growing seasons. Specifically, the effects of spatial resolution of RGB images collected by both ground and UAV platforms, and segmentation scale of UAV RGB orthophoto on the performance of MGDEXG were investigated, and MGDEXG maps were derived based on UAV RGB orthophoto to monitor maize water status and its inter-field variability. The results show that when the spatial resolution of ground RGB images (2.4 mm) was resized by bilinear interpolation algorithm to 4.8, 9.6, 19.2, 38.4, and 76.8 mm, similar water estimation performances of MGDEXG were observed when compared to the crop water stress index (CWSI), with R2 values ranging 0.80–0.83. However, the processing time per RGB image with 2.4 mm spatial resolution was greatly reduced from 232.26 s to 0.32 s when the resolution was reduced by 32 times, providing a better opportunity to obtain MGDEXG in real-time. When UAV RGB images with two spatial resolutions of 2.7 mm and 14.7 mm were adopted, a poor water stress estimation performance was observed for the lower resolution with R2 of 0.62 and RMSE of 0.12 mol·m-2·s-1 for the maize stomatal conductance. The possible reason could be the errors introduced during the mosaicking process. When segmentation scales of 2 m x 2 m, 4 m x 4 m, 6 m x 6 m, 8 m x 8 m, 10 m x 10 m, and 12 m x 12 m were adopted to crop UAV RGB orthophoto, similar results were also observed. Finally, MGDEXG maps were derived from UAV RGB orthophoto. Overall, this study demonstrated that the water stress estimation performance of MGDEXG index was not affected by image spatial resolution and statistical scale, and MGDEXG maps could be successfully acquired by a UAV RGB remote sensing platform with the advantages of low cost and easy to be adopted by users. •The performance of MGDEXG was further investigated in two maize growing seasons.•The image spatial resolution does not affect the performance of MGDEXG.•The high processing efficiency provides the opportunity to obtain real-time MGDEXG.•The MGDEXG map could be successfully derived from UAV RGB images.•The MGDEXG is much cheaper and easier to be adopted and implemented by users.