Blind　super-resolution　(BlindSR)　has　recently　attracted　attention　in　the　field　of　remote　sensing.　Due　to　the　lack　of　paired　data,　most　works　assume　that　the　acquired　remote　sensing　images　are　high-resolution　(HR)　and　use　predefined　degradation　models　to　synthesize　low-resolution　(LR)　images　for　training　and　evaluation.　However,　these　acquired　remote　sensing　images　are　often　degraded　by　various　factors,　which　still　require　super-resolution　(SR)　reconstruction　to　meet　practical　needs.　Using　them　as　ground-truth　(GT)　images　will　limit　the　model＇s　ability　to　restore　fine　details,　resulting　in　blurry　and　noisy　reconstructions.　To　overcome　these　limitations,　we　propose　an　unsupervised　degradation-aware　network　which　transforms　natural　images　into　the　degraded　domain　as　real-world　remote　sensing　images.　It　uses　natural　images　containing　rich　texture　information　as　a　reference　for　fine-grained　restoration　of　the　network,　enabling　the　network　to　produce　clearer　reconstructions.　Furthermore,　we　discovered　the　remarkable　capability　of　the　patchwise　discriminator　to　perceive　the　degradation　type　of　different　regions　within　the　acquired　remote　sensing　image.　Inspired　by　this　finding,　we　design　a　novel　degradation　representation　module　(DRM)　that　can　estimate　the　degradation　information　from　LR　images　and　guide　the　network　to　perform　adaptive　restoration.　Comprehensive　experimental　results　demonstrate　that　our　proposed　unsupervised　blind　super-resolution　framework　achieves　state-of-the-art　(SOTA)　restoration　performance.　Our　code　and　pretrained　models　have　been　uploaded　to　GitHub　(https://github.com/55Dupup/UDASR)　for　validation.