Highlights What are the main findings? ECO-DEAU significantly outperforms traditional linear and unconstrained deep learning models, achieving a maximum overall of 0.749 in heterogeneous zones and effectively decoupling spectrally similar classes like impervious surfaces and bare soil. What are the implications of the main findings? Embedding ecological priors into deep autoencoders effectively overcomes local optima limitations of traditional unmixing methods, ensuring both high accuracy and biophysical interpretability.Highlights What are the main findings? ECO-DEAU significantly outperforms traditional linear and unconstrained deep learning models, achieving a maximum overall of 0.749 in heterogeneous zones and effectively decoupling spectrally similar classes like impervious surfaces and bare soil. What are the implications of the main findings? Embedding ecological priors into deep autoencoders effectively overcomes local optima limitations of traditional unmixing methods, ensuring both high accuracy and biophysical interpretability.Abstract Arid and semi-arid regions are critical to terrestrial ecosystems and regional carbon cycle regulation, directly contributing to peak carbon and carbon neutrality goals. However, the fragmented landscapes in these regions pose significant challenges to conventional pixel-based classification, which often struggles with mixed pixel issues and lacks biophysical interpretability. To address these limitations, this study develops an Ecologically Constrained Deep Learning Autoencoder (ECO-DEAU) framework for sub-pixel land cover mapping by integrating biophysical constraints. Specifically, ECO-DEAU employs spectral indices to extract standard spectral signatures for five primary land cover types, which serve as initial weights to guide the autoencoder in estimating fractional abundances. The model was trained across ten representative landscape zones in the Inner Mongolia section of the Yellow River Basin and validated against high-resolution Gaofen-2 data. Results demonstrated that ECO-DEAU yielded an average R2 of 0.687, reaching a maximum R2 of 0.749 in spatially heterogeneous transition zones, representing a substantial improvement over the baseline unconstrained Deep Autoencoder (DEAU). By effectively resolving the blind source separation problem and improving decomposition accuracy, ECO-DEAU serves as a robust tool for addressing mixed pixel challenges in heterogeneous environments, thereby facilitating large-scale, high-resolution carbon sink monitoring.