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The primary goal of this dissertation was to leverage the capabilities of remote sensing technology for capturing detailed spatial information at different spatial resolutions to monitor agricultural crops and generate accurate input datasets for water resources models. This dissertation is divided into three different research studies. In the first study, a remote sensing classification method was developed for classifying irrigated and non-irrigated fields that integrates Vegetation indices with surface energy balance fluxes. The method was applied in the COHYST2010 hydrological model region with wide climate variation and to multiple growing seasons with results that were 92.1% accurate and explained 97% variation in National Agricultural Statistics Service (NASS) county irrigation statistics. In the second study, a new method was developed (referred to as "footprint method") of re-projecting Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that preserves the geometric orientation and size of satellite sensor pixels. It properly represents satellite sensor pixel orientations in fields, and eliminates artifacts introduced by conventional processing methods. Statistical results of field comparison in AmeriFlux experimental fields US-Ne1 and US-Ne2 based on Leaf Area Index (LAI) equation of Myneni eat al. showed improvement in LAI estimation when the footprint method was applied with reduced RMSE by 16.05%, ubRMSE by 26.25%, and nRMSE by 16.1% in average. On the contrary, the results of statistical analysis of MODIS Green LAI estimates based on Green LAI equation of Viña et al. does not support this conclusion. A third study explored the potential opportunities and benefits of utilizing gridded precipitation data, which is the combination of remotely sensed and weather stations precipitation data with more detailed spatial variability in water resources models. This study explored differences in spatial patterns between precipitation and rechar