Not Available

Not AvailableField experiments were conducted with four levels of irrigation and nitrogen on wheat for 2 years (2009–2010 and 2010–2011) to quantify and predict the crop water status using hyperspectral remote sensing. Hyperspectral reflectance in 350–2500 nm range was recorded at five growth stages. Based on highest correlation between relative leaf water content (RLWC) and reflectance in five water bands, the booting stage was identified as the most suitable stage for water stress evaluation. Ten hyperspectral water indices were calculated using the first year booting stage reflectance data and prediction models for RLWC and equivalent water thickness (EWT) based on these ten indices were developed. The prediction models for RLWC based on moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index 1640 (NDWI) and normalized multiband drought index (NMDI) were identified as the most precise and accurate models as indicated by different validation statistics. The models developed for EWT based on water band index1640 (WBI), MSI, NDWI1640and NMDI were found to be most suitable and accurate. These indices were found to be insensitive to N stress treatments indicating their ability to detect water deficiency as the cause of plant stress. Thus, the study identified four hyperspectral water indices to assess the wheat crop water status at booting stage and developed their respective predictive models.Not Availabl