Abstract: Accurate quantification of moisture content is critically important for the efficient industrial utilization of wheat straw, including its application as a bioenergy feedstock, animal feed, and industrial raw material, as well as for ensuring safe storage and transportation. Near-infrared (NIR) spectroscopy has emerged as a promising technique for moisture determination owing to its rapid response, non-destructive nature, and suitability for online implementation. However, the strong sensitivity of water-related NIR absorption features to temperature variations poses a significant challenge to the robustness and reliability of NIR-based moisture prediction models under practical operating conditions. Therefore, a systematic investigation of temperature-induced spectral variations and the development of effective correction strategies are essential for advancing the application of NIR spectroscopy in real-world scenarios. Extensive literature evidence and experimental observations indicate that temperature interference in NIR spectra primarily originates from its influence on the microstructural and physicochemical states of water molecules. Temperature variations alter the dynamic equilibrium of hydrogen bonding within the water molecular network, leading to changes in the degree of molecular association. As temperature increases, partial disruption of hydrogen bonds occurs, promoting the transition of water molecules from highly associated clusters to less ordered states such as monomers or small oligomers. These structural transformations directly affect the vibrational behavior of O–H bonds, including both stretching and bending modes. Consequently, NIR spectra exhibit characteristic changes, such as absorption band shifts, asymmetric peak broadening, and variations in band intensity. From a modeling perspective, these temperature-induced spectral distortions result in a pronounced deterioration of prediction accuracy when calibration models developed under isothermal conditions are applied to samples measured across varying temperatures, thereby limiting the practical applicability of conventional NIR moisture models. To assess the intrinsic capability of NIR spectroscopy for moisture determination in wheat straw under non-isothermal conditions, a stepwise modeling strategy was initially adopted. Local calibration models were constructed at discrete and stable temperature points to minimize temperature-induced variability. The optimal local model exhibited excellent predictive performance, achieving a cross-validated coefficient of determination (Rcv²) of 0.990 and a relative standard deviation (RSD) of 4.911%. These results demonstrate that, under controlled temperature conditions, NIR spectroscopy can accurately capture moisture-related information in wheat straw and thus serves as a reliable benchmark for further model development. To address the challenge of arbitrary temperature fluctuations encountered in industrial environments, the External Parameter Orthogonalization (EPO) algorithm was subsequently introduced. EPO is a multivariate correction technique designed to separate systematic variations associated with external factors from chemically relevant information. By projecting the original spectral data onto orthogonal subspaces that are either sensitive or insensitive to temperature, EPO effectively suppresses temperature-related spectral contributions while preserving moisture-dependent features. Using this approach, a global calibration model was established over a broad temperature range of 25–60 °C. Although the predictive performance of the EPO-corrected model was lower than that of the local isothermal model, it still achieved satisfactory results, with an Rcv² of 0.883 and an RSD of 13.144%, indicating enhanced robustness and stability across variable temperature conditions. In conclusion, this study elucidates the mechanistic origin of temperature effects on the NIR spectra of wheat straw from the perspective of water molecular structure and hydrogen bonding dynamics. More importantly, it demonstrates that EPO-based correction provides an effective strategy for mitigating temperature interference in NIR-based moisture determination. The proposed approach offers a solid theoretical basis and a practical technical pathway for the development of temperature-resilient online NIR monitoring systems for agricultural straw moisture analysis.