Leaf equivalent water thickness (LEWT) is an important parameter in ecological and environmental monitoring. Our study proposes two new indices, the normalized differential projection index (NDPI) and the distance projection ratio index (DPRI), by considering the angle of βSWIR1 and the length, projection, and relationships among the random forest feature bands at 721, 1466, and 2061 nm, corresponding to NIR, SWIR1, and SWIR2 wavebands, respectively. Single-factor analysis of NDPI and DPRI and multifactor analysis of other vegetation indices are used to build a LEWT linear model whose effectiveness is evaluated using 10-fold cross validation. Despite their simple structure, the NDPI and DPRI can explain the majority of the variation in LEWT. After single-factor analysis, results from DPRI are superior to those from previous vegetation indices or equations with a higher coefficient of determination (R²  =  0.79) and lower relative root-mean-square error (rRMSE  =  9.79  %  ). Multifactor analysis shows that the model (R²  =  0.81, rRMSE  =  9.45  %  ) built using DPRI and the water index is the most accurate. The proposed use of NDPI and DPRI as parameter bands to build such models provides a method for further study of the vegetation water content inversion.