Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

Abstract

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.