A variety of remote sensing indices have been used to infer crop nitrogen (N) status for field-scale nutrient management. However, such indices may indicate incorrect N status if there is a decrease in crop canopy density influenced by other growth retardation factors, such as water stress. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that has been proposed for inferring cotton N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content. The primary objective of this study was to evaluate the CCCI for detecting the N status for cotton, broccoli and wheat during the growing season without being affected by water stress. Remote sensing data were collected during cotton (1998 and 1999), broccoli (2001), and wheat (2004 and 2005) experiments. Experiments included treatments of optimal and low levels of N and water. They were carried out at The University of Arizona's Maricopa Agricultural Center (MAC) located approximately 40 km south of Phoenix, AZ, USA. The primary results indicated that the CCCI is significantly correlated with the measured parameters of nitrogen status, including petiole NO3-N, SPAD chlorophyll, and leaf total nitrogen. The CCCI was found to be highly sensitive to nitrogen, but mostly insensitive to water stress, especially at full cover. The CCCI can be used as a successful management tool for differentiating between the effects of nitrogen and water stress in wheat. However, CCCI was not very reliable with wheat or broccoli at times of severe water stress.