{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T03:56:52Z","timestamp":1772251012198,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,2]],"date-time":"2023-04-02T00:00:00Z","timestamp":1680393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42161068"],"award-info":[{"award-number":["42161068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021-ZJ-743"],"award-info":[{"award-number":["2021-ZJ-743"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation in Qinghai of China","award":["42161068"],"award-info":[{"award-number":["42161068"]}]},{"name":"Natural Science Foundation in Qinghai of China","award":["2021-ZJ-743"],"award-info":[{"award-number":["2021-ZJ-743"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis\u2013NIR 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\u2013NIR 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\u201340, 50\u201360, 30\u201335, and 25\u201335 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.<\/jats:p>","DOI":"10.3390\/s23073686","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T02:32:27Z","timestamp":1680489147000},"page":"3686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Vis\u2013NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai\u2013Tibet Plateau"],"prefix":"10.3390","volume":"23","author":[{"given":"Chuanli","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Geologic Engineering, Qinghai University, Xining 810016, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5817-886X","authenticated-orcid":false,"given":"Jianyun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Geologic Engineering, Qinghai University, Xining 810016, China"},{"name":"Key Lab of Cenozoic Resource & Environment in North Margin of the Tibetan Plateau, Xining 810016, China"}]},{"given":"Yuanyuan","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Geologic Engineering, Qinghai University, Xining 810016, China"}]},{"given":"Guorong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geologic Engineering, Qinghai University, Xining 810016, China"},{"name":"Key Lab of Cenozoic Resource & Environment in North Margin of the Tibetan Plateau, Xining 810016, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1038\/s43017-020-0080-8","article-title":"The concept and future prospects of soil health","volume":"1","author":"Lehmann","year":"2020","journal-title":"Nat. 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