Normalised difference moisture index in water stress assessment of maize crops

Remote sensing is a promising technique for better management of water resources in agriculture through improvement of dynamic control and operational scheduling on irrigated croplands. The main goal of this study was to identify the possibilities of application of the normalised difference moisture index (NDMI) to water stress monitoring in maize crops, and to determine the relationship between the index and soil moisture content. The study was carried out in 2019–2021 in the experimental fields of disturbed maize, cultivated on dark-chestnut soils in the Southern Ukraine at the NAAS Institute of Climate-Smart Agriculture. The crop cultivation technology was common for the conditions of the steppe zone of Ukraine. Actual soil moisture content was determined by gravimetric method in the pre-sowing and post-harvest period. The NDMI values were calculated using cloudless aerospace images from the satellites Landsat 8, Sentinel-2, and MODIS with 250 m resolution. It was revealed that the seasonal NDMI dynamics perfectly reflected the water-supply conditions of the disturbed maize, and could be used for operational monitoring and scheduling of irrigation. The parameters of the water-supply conditions were determined in 2021, which was the wettest year of the study: the cumulative seasonal NDMI reached 1.71, while the highest water stress was recorded in the driest year, 2020, – the cumulative NDMI was 0.15. Additionally, there was a moderately strong negative correlation between NDMI and soil moisture content, and the coefficient of determination was 0.62. The linear regression models, developed to predict soil moisture content in the 0–100 cm layer depending on the NDMI values, had good fitting quality and reasonable accuracy, but they required further calibration and extension of the initial dataset to provide more robust and reliable results for practical implementation. Based on the results of the study, spatial NDMI could be considered a good and reliable tool for improving irrigation water management. Further studies should focus on the practical implementation of the NDMI-based model of moisture-content estimation, as well as on the possibilities of the index usage for mapping irrigated lands.