Remote sensing is a promising technology that provides information about the crop's physiological and phenological status. This information is based on the spectral absorption and scattering features of the plants. Many different vegetation indices (VI) have been developed, and are in use to estimate quantitatively the relationship between multi and hyper-spectral reflectance and effective crop physiological parameters, i.e. nitrogen (N) content, biomass, leaf area index (LAI). The CropCircle and the RapidScan, the simple radiometers, offer an option retrieving red (R), red edge (RE) and near infrared (NIR) plant reflectance enabling calculate the VI such as NDVI, NDRE, and other based on the important RE region. The objectives of this study were: (i) to test relationships between wheat biophysical characteristics such as biomass and nitrogen and remote sensing data; (ii) to evaluate accuracy of remote sensing biomass and nitrogen estimation; (iii) to explore the potential and limitations of using active remote sensing techniques. The study was carried out during the growing seasons of 2012–2013 on 16 commercial spring wheat fields of kibbutz Saad and 8 experiment fields at the Gilat Research Center, located in the Northern Negev region of Israel. Data have been collected by CropCircle (mounted on a car) in both seasons by passing over fields, while RapidScan data have been collected by hand in 2013. The data set includes fields at different growth stages, from 3 leaves (Zadoks 13) till stem elongation (Zadoks 35), reflecting large differences in canopy height (10 through 50 cm) and vegetation cover. The variation in wheat samples represents biomass of 1 to 880 g m^-2, N concentration 9.7 to 54 g kg, and canopy N content 65 to 16500 mg N m^-2. High correlations have been found between the different VI, as they were calculated from the same 2 or 3 bands. Very close relationships have been found between many VI and wheat biomass. For CropCircle, indices have been calculated by the closest point and by an average of the area near the sampling point. Most VI had similar correlation with wheat parameters independent of the representing area. Canopy nitrogen concentration estimation was problematic and most indices exhibit very low correlation, the best VI was MSRre that reached r = 0.47 (mREP 0.57 for RapidScan). Despite that low correlation, for most indices the correlation with canopy nitrogen content reached higher or similar correlation as with biomass. The best VI were WDRVI, MSR and WICI2 (r = 0.90) while the correlation with biomass was a little bit lower (r = 0.88). The NDVI and NDRE had lower correlations, of 0.41, 0.40; 0.86, 0.87; 0.84, 0.85 for N concentration, canopy N content and biomass, respectively, with little NDRE advantage. The RE band data can improve correlation, when it is included in newer VI. However, correlation accuracy must be regarded with caution as there was low repeatability in different data bases. The difference between CropCircle and RapidScan relationships as well as variation between fields accounted for the correlation much more than the within field variation. The potential and limitations of using active remote sensing as a tool for growers and/or scientists are discussed.