The analysis of hydrological basins requires information that is often not available or non-existent when the study areas are far from large urban centers. In the case of Bustillos Lagoon in the Mexican state of Chihuahua, hydrological information is limited, and government agencies do not share data with interested persons and research institutions. Given this barrier, this research contributes to filling information gaps concerning the geometry of the Bustillos Lagoon, evaporation, and morphometric parameters through the use of current technology in remote sensors, geographic information systems, and programming techniques that are used to extract, transform and process information. Chapter 2 deals with a new methodology that generates a 3D model of the bottom of the lagoon, which uses high-precision GPS surveys, bathymetry, regional digital terrain models, and satellite image time series. The analysis using the Kappa coefficient demonstrates that the overall performance of the 3D model is more significant than 0.89, which means that the model has a very high level of agreement. The analysis also showed that at greater depth, the agreement between the coverage of the water surface of the model and the images is relatively low (0.89), and this is due to the spatial resolution of the satellite images and strip banding errors of Landsat ETM +. On the other hand, on the upper level, there is an agreement close to 0.99 of the Kappa coefficients. Chapter 3 presents a performance comparison between the Regional Evapotranspiration Estimate Model (REEM) and the Earth Engine Evapotranspiration Flux (EEFlux) model, which are evapotranspiration models based on energy balances. These models can estimate the evaporation of water bodies. After applying statistical analysis, REEM performed better than EEFlux in quantifying the evaporation of the Bustillos Lagoon. Chapter 4 proposes an iterative algorithm to calculate morphometric variables (volume-area-height) using 3D models of water bodies. The implementation of the algorithm in the Python programming language showed that it is not necessary to develop complex equations that interrelate the morphometric variables, which by their nature, lead to more considerable uncertainty from the data source for their construction. This research document highlights the importance of cumulative multi-faceted knowledge to support and respond to regional water issues.