Understanding the patterns and relationships between land surface productivity and the climatic condition is essential to predict the impact of climate change. This study aims to understand spatial temporal variability and relationships of land surface productivity using Normalized Difference Vegetation Index (NDVI) and drought indices, mainly Standard Precipitation Index (SPI) and Standard Precipitation Evaporation Index (SPEI) across four ecoregions: Mojave, Sonoran, Apache Highlands and Chihuahua of the Southwest United States. Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and land cover data, and Parameter Regression on Independent Slopes Model (PRISM) precipitation and temperature data were used for analysis. Using Mann-Kendall, I calculated the trends in annual and seasonal NDVI, SPI and SPEI datasets. I used the Pearson Correlation Coefficients to examine the response of integrated and monthly NDVI values to SPI and SPEI values. The positive and negative trends were found during the annual and monsoon seasons whereas only negative trends were found during the spring season for NDVI, SPI and SPEI values. The relationship between NDVI and coincident and antecedent SPEI values changed significantly by area and season for each of the ecoregions across the east-west seasonal precipitation gradient.