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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #385515
Research Project: Improving the Sustainability of Irrigated Farming Systems in Semi-Arid Regions

Location: Water Management and Systems Research

Title: 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

Author
item ZHANG, LIYUAN - Northwest A&f University
item Zhang, Huihui
item HAN, WENTING - Northwest A&f University
item NIU, YAXIAO - Northwest A&f University
item CHÁVEZ, JOSÉ - Colorado State University
item MA, WEITONG - Northwest A&f University

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/22/2022
Publication Date: 4/30/2022
Citation: Zhang, L., Zhang, H., Han, W., Niu, Y., Chávez, J.L., Ma, W. 2022. 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. Agricultural Water Management. 264. Article e107506. https://doi.org/10.1016/j.agwat.2022.107506.
DOI: https://doi.org/10.1016/j.agwat.2022.107506

Interpretive Summary: We conducted studies in two maize fields with different irrigation levels during the 2015 and 2019 growing seasons to evaluate the Mean Value of Gaussian Distribution of Excess Green index (MGDEXG) of RGB imagery for estimating crop water stress. The effects of spatial resolution and segmentation scale of UAV RGB orthophoto on the performance of MGDEXG were investigated, and MGDEXG maps were derived to monitor crop water status and its inter-field variability. The results show that when the spatial resolution of RGB images (2.4 mm) was resized to 4.8, 9.6, 19.2, 38.4, and 76.8 mm, similar performance of MGDEXG was observed when compared to the crop water stress index (CWSI). However, the processing time per RGB image with 2.4-mm spatial resolution was greatly reduced from 232.26s to 0.32s when the resolution was reduced by 32 times. When UAV RGB imagery 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. The possible reason could be the errors introduced during the mosaicking process. When segmentation scales of 2 to 12 m were used to crop UAV RGB orthophoto, similar results were also observed. Overall, this study demonstrated that the 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.

Technical Abstract: To further explore the performance of the newly proposed crop water stress indicator - the Mean Value of Gaussian Distribution of Excess Green index (MGDEXG) of maize crop in RGB imagery, we conducted studies in two maize fields with different irrigation levels during the 2015 and 2019 growing seasons. Specifically, the effects of spatial resolution 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 crop water status and its inter-field variability. The results show that when the spatial resolution of 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 232.26s to 0.32s when the resolution was reduced by 32 times, providing a better opportunity to obtain MGDEXG in real-time. When UAV RGB imagery 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 mmol·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 x 2 m, 4 x 4 m, 6 x 6 m, 8 x 8 m, 10 x 10 m, and 12 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.