Derived land use/land cover (LULC) by remote sensing serves as a key data source for subsequent geospatially integrated analysis and modeling efforts using cell-by-cell calculations. While the sensitivity of algorithms and comparisons of performance in the context of users’, producers’ and overall accuracies are well-studied by the remote sensing community, the spatial sensitivity of classification algorithms (i.e. the locations of LULC classes change with different algorithms) is a less-studied phenomenon that impacts modeling results. Hydrologists are often unaware of the spatial sensitivity of image processing algorithms, and hence they may incorporate LULC maps in models without critically evaluating the spatial accuracy of the data. Recent significant increases of machine learning algorithms (ML), such as Artificial Neural Networks (ANN) and support vector machines (SVM) for LULC classifications, including deep learning, can benefit from theoretical development of uncertainty and sensitivity analysis associated with various algorithms using spatial thinking and spatial reasoning. Recent graduates and young professionals can benefit from a simple demonstration of spatial sensitivity of ML algorithms and their consequences in water budget calculations using an integrated geospatial approach. Further, one of the common use of LULC data includes hydrologic model for long-term simulations, which require the use of historical data. Historical data are often characterized by poor spectral and spatial resolution. However, effective analysis of historical data is needed to gather information about baseline and for long-term analysis. Therefore, we need to understand the spatial sensitivity of historical data such as Landsat5 to ML-based classification algorithms such as SVM and ANN before using an integrated geospatial framework to integrate LULC data hydrologic models.