Remote sensing image super-resolution requires local detail and wider structural context. The mixture-of-experts (MoE) designs compared here select or combine expert outputs without explicitly passing behavior statistics to subsequent spatial–frequency restoration. We propose EBSFFormer, which aggregates routing preferences and response-weighted proportions within restoration groups and converts them into channel gains for later spatial and frequency branches. Heterogeneous experts and a content–cumulative-change router supply these statistics. We train on AID and evaluate on AID, DIOR, DOTA, and NWPU-RESISC45 under bicubic $\times 4$ degradation. Across three AID training seeds, EBSFFormer obtains 31.2639 ± 0.0009 dB peak signal-to-noise ratio (PSNR), with mean improvements of 0.0121 dB over MambaIRv2 and 0.1037 dB over SwinIR. Single-checkpoint evaluation on NWPU without fine-tuning gives the highest Y-channel PSNR and structural similarity index (SSIM) among the compared methods and the third-lowest learned perceptual image patch similarity (LPIPS). Component ablations, red–green–blue (RGB) metrics, and ground-truth-referenced visual comparisons characterize the reconstruction gains. EBSFFormer has fewer parameters and lower mean latency than MambaIRv2, but the highest peak allocated memory in the runtime comparison.