Spatial distribution models are increasingly used in ecological studies, but are limited by the poor accuracy of remote sensing (RS) for mapping microhabitat (< 0.1 ha) features. Mapping accuracy can be improved by combining advanced RS image-processing techniques with microhabitat data expressed as a structural complexity index (SCI). To test this idea, we used principal components analysis (PCA) and an additive SCI method developed for forest ecology (calculated by re-scaling and summing representative structural variables) to summarize 13 microhabitat-scale (0.04 ha) vegetation structure attributes describing the rare mountain bongo antelope's (Tragelaphus eurycerus isaaci) habitat in Kenya's Aberdare mountains. Microhabitat data were collected in 127 plots: 37 related to bongo habitat use, 90 from 1 km-spaced grid points representing overall habitat availability and bongo non-presence. We then assessed each SCI's effectiveness for discerning microhabitat variability and bongo habitat selection, using Wilcoxon Rank Sum tests for differences in mean SCI scores among plots divided into 4 vegetation classes, and the Area Under the Curve (AUC) of receiver operating characteristics from logistic regressions. We also examined the accuracy of predicted SCI scores resulting from regression models based on variables derived from a) ASTER imagery processed with spectral mixture and texture analysis, b) an SRTM DEM and c) rainfall data, using the 90 grid plots for model training and the bongo plots as an independent test dataset. Of the five SCIs derived, two performed best: the PCA-derived Canopy Structure Index (CSI) and an additive index summarizing 8 structural variables (AI8). CSI and AI8 showed significant differences between 5 of 6 vegetation class pairs, strong abilities to distinguish bongo-selected from available habitat (AUCs = 0.71 (CSI); 0.70 (AI8)), and predicted scores 60–110% more accurate than reported by other studies using RS to quantify individual microhabitat structural attributes (CSI model R2 = 0.51, RMSE = 0.19 (training) and 0.21 (test); AI8 model R2 = 0.46, RMSE = 0.17 (training) and 0.19 (test)). Repeating the Wilcoxon tests and logistic regressions with RS-predicted SCI values showed that AI8 most effectively preserved the patterns found with the observed SCIs. These results demonstrate that SCIs effectively characterize microhabitat structure and selection, and boost microhabitat mapping accuracy when combined with enhanced RS image-processing techniques. This approach can improve distribution models and broaden their applicability, makes RS more relevant to applied ecology, and shows that processing field data to be more compatible with RS can improve RS-based habitat mapping accuracy.