Spectral reconstruction (SR) techniques can generate hyperspectral images (HSIs) from multispectral images (MSIs) with the same spatial resolution, thus alleviating the problem of limited availability and low spatial resolution of satellite HSIs. However, in scenarios where both HSIs and MSIs can be acquired simultaneously, spectral mapping relationship (SMR) among real images may not align with the sensor’s spectral response function (SRF), due to factors such as sensor noise and calibration errors. This mismatch can result in discrepancies in reflectivity between the reconstructed HSIs and the real HSIs. To solve the above problems, this article proposes a semi-supervised transfer learning SR (SSTSR) model based on gradient direction constraints. Through semi-supervised learning, SSTSR acquires precise SMRs in overlapping regions and extracts spectral trend information of ground objects from historical models in nonoverlapping regions. Experiments on two datasets demonstrate that the reconstructed HSIs closely resemble real HSIs, leading to impressive classification performance when employing a real HSI classifier.