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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 #361803
Title: Mapping maize water stress based on UAV multispectral remote sensing

Author
item ZHANG, LIYUAN - Northwest A&f University
item Zhang, Huihui
item NIU, YAXIAO - Northwest A&f University
item HAN, WENTING - Northwest A&f University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/9/2019
Publication Date: 3/13/2019
Citation: Zhang, L., Zhang, H., Niu, Y., Han, W. 2019. Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing. 11(6):605. https://doi.org/10.3390/rs11060605.
DOI: https://doi.org/10.3390/rs11060605

Interpretive Summary: Mapping maize water status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping maize water stress. The study was carried out in a 1.13-ha maize research field located in Ordos, Inner Mongolia, China. The experimental field was designed as five treatments with different depths of irrigation at different growth stags. Canopy temperature, air temperature and relative humidity were used to establish Crop Water Stress Index (CWSI) model. Nine vegetation indices (VIs) were derived from the multispectral imagery and used to establish VI-CWSI regression model. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stage, but the non-transpiring baseline did not change significantly. Specific non-water stress lines were found for well-irrigated maize in the reproductive and maturation stages. Compared to the CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models showed the ability to assess the field variability of soil and crop.

Technical Abstract: Mapping maize water status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping maize water stress. The study was carried out in a 1.13-ha maize research field located in Ordos, Inner Mongolia, China. The applied water depth of maize at the late vegetative, reproductive and maturation growth stages was the percentages of the control treatment region. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish Crop Water Stress Index (CWSI) empirical model. Nine vegetation indices (VIs) related to crop water stress were derived from the multispectral imagery and used to establish CWSI inversion model. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1°C, however, the non-transpiring baseline did not change significantly. Specific non-water stress lines were found for well-irrigated maize in the reproductive and maturation stages, corresponding to average CWSI of 0.12 and 0.03, respectively. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI) and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlation with CWSI. Compared to the CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study were relatively reasonable, due to the ability to assess the field variability of soil and crop. This study demonstrates the potentiality of using UAV-based high-resolution multispectral imagery to map maize water stress.