Abstract
Mapping maize water stress 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 water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. 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 a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. 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 with an increase of 0.1 °C. 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 correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.
Highlights
Water resource scarcity is one of the most important challenges for agricultural water management, especially in arid and semi-arid areas all over the world
This study investigated whether a unmanned aerial vehicle (UAV)-based multispectral remote sensing system could map the water stress status of maize at a farm scale, as a supplement to ground-based empirical crop water stress index (CWSI) model
The local weather condition and management practices can greatly influence the applicability of the empirical CWSI model, changes of the non-water stress baseline (NWSB) and the non-transpiring baseline (NTB) were analyzed
Summary
Water resource scarcity is one of the most important challenges for agricultural water management, especially in arid and semi-arid areas all over the world. Due to drought associated with climate change, agricultural water resources will be reduced, it is necessary to achieve maximum production per unit of applied irrigation water. To achieve a delicate balance between yield and irrigation water, effective monitoring methods for crop water stress are necessary [5,6,7]. Crop water stress can be detected based on soil moisture content, crop physiological characteristics (e.g., stomatal conductance, leaf water potential) and remote-sensing technology [8]. On-site measurements of soil water content and crop physiological characteristics are time-consuming, laborious and costly, and do not take into account the spatial variability of soil and crops [9,10]. Measurements of canopy temperature or canopy reflectance based on remote-sensing technology have the advantages of being easy, non-destructive and of low labor intensity [8,11]
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