{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T23:10:52Z","timestamp":1770160252217,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"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 GENERAL PROGRAM","doi-asserted-by":"publisher","award":["42376193"],"award-info":[{"award-number":["42376193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The unique geographical isolation and climate sensitivity of island ecosystems make them valuable for ecological research. The Normalized Difference Vegetation Index (NDVI) is an important indicator when monitoring and evaluating these systems, and its prediction has become a key research focus. However, island NDVI prediction remains uncertain due to a limited understanding of vegetation growth and insufficient high-quality data. Deterministic models fail to capture or quantify such uncertainty, often leading to overfitting. To address this issue, this study proposes an uncertainty prediction model for the island NDVI within a coding\u2013prediction\u2013decoding framework, referred to as a Convolutional Autoencoder\u2013Regularized Bayesian Neural Network (CAE-RBNN). The model integrates a convolutional autoencoder with feature regularization to extract latent NDVI features, aiming to reconcile spatial scale disparities with environmental data, while a Bayesian Neural Network (BNN) quantifies uncertainty arising from limited samples and an incomplete understanding of the process. Finally, Monte Carlo sampling and SHAP analysis evaluate model performance, quantify predictive uncertainty, and enhance interpretability. Experiments on six islands in the Xisha archipelago demonstrate that CAE-RBNN outperforms the Convolutional Neural Network\u2013Recurrent Neural Network (CNN-RNN), the Convolutional Recurrent Neural Network (ConvRNN), Convolutional Long Short-Term Memory (ConvLSTM), and Random Forest (RF). Among them, CAE-RBNN reduces the MAE and MSE of the single-time-step prediction task by 8.40% and 10.69%, respectively, compared with the suboptimal model and decreases them by 16.31% and 22.57%, respectively, in the continuous prediction task. More importantly, it effectively quantifies the uncertainty of different driving forces, thereby improving the reliability of island NDVI predictions influenced by the environment.<\/jats:p>","DOI":"10.3390\/ijgi15020065","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T10:03:29Z","timestamp":1770113009000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CAE-RBNN: An Uncertainty-Aware Model of Island NDVI Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Zheng","family":"Xiang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-6578","authenticated-orcid":false,"given":"Cunjin","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyue","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingrui","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9054-3759","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"China Siwei Surveying and Mapping Technology Co., Ltd., Beijing 100086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"key":"ref_1","first-page":"273","article-title":"Remote Sensing Analysis of Fractional Vegetation Cover Change Triggered by Island Construction","volume":"19","author":"Wen","year":"2017","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10113-022-01887-2","article-title":"Small islands and climate change: Analysis of adaptation policy in the Cayman Islands","volume":"22","author":"Johnston","year":"2022","journal-title":"Reg. Environ. Change"},{"key":"ref_3","first-page":"1917","article-title":"Ecological Status, Protection and Development Strategies of South China Sea Islands and Reefs","volume":"44","author":"Cui","year":"2023","journal-title":"Chin. J. Trop. Crops"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10113-024-02205-8","article-title":"Place attachment, storms, and climate change in the Faroe Islands","volume":"24","author":"Kongsager","year":"2024","journal-title":"Reg. Environ. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101552","DOI":"10.1016\/j.ecoinf.2022.101552","article-title":"Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review","volume":"68","author":"Aya","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100174","DOI":"10.1016\/j.acags.2024.100174","article-title":"Global Normalized Difference Vegetation Index forecasting from air temperature, soil moisture and precipitation using a deep neural network","volume":"23","author":"Fathollahi","year":"2024","journal-title":"Appl. Comput. Geosci."},{"key":"ref_7","first-page":"1664","article-title":"Spatiotemporal pattern and prediction model of NDVI in Qiangtang grassland based on random forest algorithm","volume":"43","author":"Li","year":"2024","journal-title":"Chin. J. Ecol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, X., Yuan, W., and Dong, W. (2021). A Machine Learning Method for Predicting Vegetation Indices in China. Remote Sens., 13.","DOI":"10.3390\/rs13061147"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zuo, L.Y., Gao, J.B., Liu, Q., and Liu, L.L. (2021). Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning. Atmosphere, 12.","DOI":"10.3390\/atmos12101341"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nguyen, K.A., Seeboonruang, U., and Chen, W. (2023). Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach. Environments, 10.","DOI":"10.3390\/environments10120204"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s10661-023-12006-x","article-title":"Machine learning models for predicting vegetation conditions in Mahanadi River basin","volume":"195","author":"Raj","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vasilakos, C., Tsekouras, G.E., and Kavroudakis, D. (2022). LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data. Land, 11.","DOI":"10.3390\/land11060923"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1007\/s10661-024-13168-y","article-title":"Correlation change analysis and NDVI prediction in the Yellow River Basin of China using complex networks and GRNN-PSRLSTM","volume":"196","author":"Meng","year":"2024","journal-title":"Environ. Monit. Assess."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, Y., Lao, D., Ruan, Y.J., Huang, C., and Xin, Q.C. (2023). A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data. Sustainability, 15.","DOI":"10.3390\/su15086632"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6957","DOI":"10.1109\/JSTARS.2022.3200521","article-title":"Spatiotemporal Prediction of Alpine Vegetation Dynamic Change Based on a ConvGRU Neural Network Model: A Case Study of the Upper Heihe River Basin in Northwest China","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhao, F., Yang, G.J., Yang, H., Zhu, Y.H., and Bu, X.L. (2021). Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW\u2013LSTM Combination Method and MODIS Time Series Data. Remote Sens., 13.","DOI":"10.3390\/rs13224660"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1109\/JSTARS.2024.3350053","article-title":"Monthly NDVI Prediction Using Spatial Autocorrelation and Nonlocal Attention Networks","volume":"17","author":"Xu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cai, R.N., Xu, L., Lv, Y., Wu, T.T., and Li, X.C. (2024). Geographically Weighted Convolutional Long Short-Term Memory Neural Networks: A Geospatial Deep Learning Model for Monthly NDVI Prediction. IEEE Trans. Geosci. Remote Sens., 62.","DOI":"10.1109\/TGRS.2024.3487259"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.2166\/hydro.2003.0019","article-title":"Handling uncertainty in the hydroinformatic process","volume":"5","author":"Hall","year":"2003","journal-title":"J. Hydroinfor."},{"key":"ref_20","first-page":"4","article-title":"An Introduction to Variational Autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_21","first-page":"1501","article-title":"Advances and Trends in Bayesian Spatio-Temporal Statistical Methods and Applications","volume":"27","author":"Li","year":"2025","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_22","unstructured":"Nichol, A., and Dhariwal, P. (2021, January 18\u201324). Improved denoising diffusion probabilistic models. Proceedings of the International Conference on Machine Learning, San Diego, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"W07405","DOI":"10.1029\/2006WR005351","article-title":"Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment","volume":"43","author":"Ghosh","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_25","first-page":"104563","article-title":"Calibration and uncertainty quantification for deep learning-based drought detection","volume":"140","author":"Zhang","year":"2025","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"015002","DOI":"10.1088\/2632-2153\/aba6f3","article-title":"Deeply uncertain: Comparing methods of uncertainty quantification in deep learning algorithms","volume":"2","author":"Caldeira","year":"2021","journal-title":"Mach. Learn. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, S., Chen, H., Zhang, X.L., Xiao, P.F., and Bai, L. (2025). VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting. IEEE Trans. Geosci. Remote Sens., 63.","DOI":"10.1109\/TGRS.2025.3564317"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110176","DOI":"10.1016\/j.asoc.2023.110176","article-title":"A comprehensive survey on design and application of autoencoder in deep learning","volume":"138","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11773","DOI":"10.1007\/s10462-023-10443-1","article-title":"Bayesian learning for neural networks: An algorithmic survey","volume":"56","author":"Magris","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_30","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). A Unified Approach to Interpreting Model Predictions, Curran Associates, Inc.. Advances in Neural Information Processing Systems 30."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/2\/65\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T10:29:06Z","timestamp":1770114546000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/2\/65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,3]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["ijgi15020065"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15020065","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,3]]}}}