Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis–NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis–NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis–NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30–40, 50–60, 30–35, and 25–35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM.

.Plateau pika (Ochotona curzoniae) is a key species of alpine meadow ecosystem in Qinghai–Tibet Plateau. In this study, an observation and classification system of plateau pika disturbance intensity was constructed by multidimensional stereoscopic surveying, visual interpretation, geostatistics, principal component analysis, and clustering analysis, which can monitor and evaluate the disturbance of plateau pika activities comprehensively, scientifically, and efficiently in the head-water region of the Yellow River. The results show that (1) digital elevation model, digital orthophoto model, and three-dimensional real scene model can extract the surface morphology information changed by pika activities such as the number of pika holes and molehills, the area and volume of molehills, the elevation variation coefficient, the surface relief, and roughness; (2) The main load factors of the plateau pika disturbance intensity index (PPDII) are the surface roughness, the area and number of the molehills, and the number of mousehole; (3) The kappa value of PPDII clustering verification was 0.866, and the clustering analysis results were consistent with reality characteristics of plateau pika activity and distribution; (4) The disturbance in Laji Mountain is mainly slight and mild, accounting for 47.6% of sampling area, and the disturbance in Henan County is mainly medium and severe, accounting for 42.6% of sampling area. The results can provide reference data for the restoration and management of alpine meadow degradation in the head-water region of the Yellow River.