Moisture stress assessment in rabi maize through UAV-mounted multispectral sensor

Abstract

Introduction: Early detection of moisture stress in maize is vital for sustainable irrigation management, improved water-use efficiency, and stable yields under water-limited conditions. This study aimed to (i) evaluate the sensitivity of vegetation indices for detecting water stress, (ii) analyze their relationship with kernel yield, and (iii) assess their correlation with the Crop Water Stress Index (CWSI). Methods: A field experiment was conducted during the 2021–22 rabi season at the University of Agricultural Sciences, Dharwad, under nine irrigation regimes in a randomized complete block design (RCBD). Multispectral imagery was acquired using a MicaSense RedEdge multispectral sensor mounted on an Unmanned Aerial Vehicle (UAV) to derive the Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil-Adjusted Vegetation Index (SAVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption in Reflectance Index (TCARI). Results: Irrigation limited to the knee-high stage or omitted at tasseling and silking significantly reduced vegetation indices and kernel yield (ANOVA, p ≤ 0.05). NDVI, RDVI, SAVI, and OSAVI exhibited strong positive correlations with yield, confirming their effectiveness in capturing reductions in canopy vigour, chlorophyll activity, and photosynthetic capacity under stress. By contrast, TCARI increased with stress, reflecting its sensitivity to pigment degradation. The Crop Water Stress Index (CWSI), derived from canopy temperature, supported these spectral responses and highlighted the high vulnerability of reproductive stages to water deficit. Discussion: UAV-based multispectral indices, when combined with ground-based CWSI, provide a robust framework for early stress detection and precision irrigation in maize. These results demonstrate the potential of remote sensing technologies to improve water-use efficiency and advance climate-smart agricultural practices in water-scarce regions.