Chickpea (Cicer arietinum) is an important grain legume in semi-arid regions and water-stress is a major constraint to its productivity. Area under chickpea cultivation is growing but higher precipitation instability risks yields. The ability to assess water potential can support irrigation decisions and promote more efficient irrigation. The current study aims to assess leaf water potential (LWP) and leaf area index (LAI) by spaceborne and ground spectral sensors. Field experiments were conducted in locations representing different climatic conditions in Israel. Six irrigation regimes were applied, from 0%, 50%, 75%, 100%, 120% and 140% of Penman-Monteith evapotranspiration were implemented at the Gilat and Neve Yaar research stations as well as in commercial fields. Plants were characterized weekly for morpho-physiological traits and grain yield data was obtained at the final harvest. Canopy reflectance was acquired with a MicroSatellite VENµS (11 spectral bands, 420-910 nm) and a field spectrometer dual-field of view system at ground level (ASD, 350-2500 nm). The VENµS and ground level hyperspectral data were divided to calibration and validation data sets. Partial least squares regression and artificial neural networks were used to quantitatively estimate the morpho-physiological traits. The coefficient of determination values of independent validation models for the LWP and LAI estimated by VENµS were 0.60 and 0.67 and hyperspectral 0.66 and 0.80 with root mean square error (RMSE) of 0.24 Mpa, 0.95, 0.23 Mpa, and 0.83, respectively. The VENµS and hyperspectral ground level data are useful for evaluation of morpho-physiological chickpea plant traits.