In order to aid federal agencies and private companies in the ever-growing problem of invasive species target detection, an investigation has been done on classification accuracy data cubes for use in the determination of spectral, spatial, and temporal sensor resolution requirements. The data cube is the result of a developed automated target recognition system that begins with ?ideal? hyperspectral data, and then reduces and combines spectral and spatial resolutions. The reduced data is subjected to testing methods using the Best Spectral Bands (BSB) and the All Spectral Bands (ASB) approaches and classification methods using nearest mean (NM), nearest neighbor (NN), and maximum likelihood (ML) classifiers. The effectiveness of the system is tested via two target-nontarget case studies, namely, terrestrial Cogongrass (Imperata cylindrica)-Johnsongrass (Sorghum halepense), and aquatic Water Hyacinth (Eichhornia crassipes)-American Lotus (Nelumbo lutea). Results reveal the effects, or trade-offs, of spectral-spatial-temporal resolution combinations on the ability of an ATR system to accurately detect the target invasive species. For example, in the aquatic vegetation case study, overall classification accuracies of around 90% or higher can be obtained during the month of August for spectral resolutions of 80 ? 1000nm FWHM for target abundances of 70 ? 100% per pixel. Furthermore, the ATR system demonstrates the use of resolution cubes that can be readily used to design or select cost-effective sensors for use in invasive species target detection, since lower resolution combinations may be acceptable in order to gain satisfactory classification accuracy results.