Abstract: Red edge parameters were used to estimate chlorophyll content of spring wheat in irrigated land and dry land regions. The study analyzed the determination processes of canopy spectral, chlorophyll content, leaf width and seedling height of spring wheat in different regions, irrigated land and dry land regions. Then the strongest correlation between red edge position and chlorophyll content of spring wheat in different regions was used to establish optimally efficient models for simulating chlorophyll content in irrigated and dry lands. The results showed that red edge position of irrigated land was highest, followed by dry land in shady slope, then dry land in half shady/sunny slope and dry land in sunny slope. The red edge positions of spring wheat grown at different lands were deviated to long wave from setting to flowering stages, and to short wave from flowering to milking stages. The peak value of red edge of spring wheat in irrigated land and dry land in shady slope followed a "unimodal" curve at setting and milking stages, and a "dimodal" curve at other stages. The peak value of red edge of spring wheat grown at dry land in half shady/sunny slope also followed a "dimodal" curve at heading and flowering stages, and "unimodal" curve at setting, joining and milking stages. "Unimodal" curve was outstandingly a unique feature of dry land in sunny slope for all the growth stages of spring wheat. The red edge area of spring wheat was largest at irrigated land, and smallest at dry land in sunny slope. Irrespective of the stage, a significant correlation was noted between chlorophyll content and red edge position of spring wheat. From setting stage to milking stage, the estimated determination coefficient of regression (R²) for irrigated land exceeded 0.82 with a root mean square error (RMSE) of less than 1.26. Then R² for dry land spring wheat exceeded 0.81 (all correlation coefficients highly positive) with RMSE of less than 1.70. Thus it was possible to use red edge position to estimate chlorophyll content of spring wheat. The results provided new insights into the methods to using hyper-spectral information to monitor wheat growth and yield.