The impact of vegetation on the microclimate has not been adequately considered in the analysis of temperature forecasting and modelling. To fill part of this gap, the following study was undertaken.

A daily 850-700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalised Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989-98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (r2 value) of surface maximum and minimum temperature by only the 850-700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850-700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the r2 values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March-October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.