Studies of urban ecological systems can be greatly enhanced by combining ecosystem modelling and remote sensing which often requires establishing statistical relationships between field and remote sensing data. At the Central Arizona–Phoenix Long‐Term Ecological Research (CAPLTER) site in the south‐western USA, we estimated vegetation abundance from Landsat ETM+ acquired at three dates by computing vegetation indices (NDVI and SAVI) and conducting linear spectral mixture analysis (SMA). Our analyses were stratified by three major land use/land covers—urban, agricultural, and desert. SMA, which provides direct measures of vegetation end member fraction for each pixel, was directly compared with field data and with the independent accuracy assessment dataset constructed from air photos. Vegetation index images with highest correlation with field data were used to construct regression models whose predictions were validated with the accuracy assessment dataset. We also investigated alternative regression methods, recognizing the inadequacy of traditional Ordinary Least Squares (OLS) in biophysical remote sensing. Symmetrical regressions—reduced major axis (RMA) and bisector ordinary least squares (OLS_bisector)—were evaluated and compared with OLS. Our results indicated that SMA was a more accurate approach to vegetation quantification in urban and agricultural land uses, but had a poor accuracy when applied to desert vegetation. Potential sources of errors and some improvement recommendations are discussed.