Abstract
Vegetation, though central to terrestrial ecosystems, remains highly vulnerable to fluctuations in climate and human-induced activities. Such combined influence on vegetation health dynamics necessitates the application of robust remote sensing-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). The latter enables the detection of subtle structural and functional changes, offering an early warning of plant stress before visible symptoms appear. It is therefore crucial to predict vegetation activities by modelling NDVI that is able to detect and attribute the climate change impacts on vegetation growth through its temporal and spatial variations. For this reason, in this paper we introduce an advanced combined deep learning method (bidirectional long short-term memory and convolutional neural network model, BiLSTM-CNN) for temporal-spatial modelling of NDVI informed by meteorological and soil moisture data. BiLSTM-CNN is a composite progressive processing model that can investigate potential trends of vegetation alterations that may be abrupt and barely obvious, localized and extensive, happening over short or long-time scales. Our proposed BiLSTM-CNN forecasting method has been evaluated and compared with state-of-the-art techniques, and the experimental results have shown clearly that our proposed method is competitive with the existing relevant NDVI deep learning predicting models.
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Acknowledgments
This work has been soported by Junta de Andalucia (grant QUALIFICA_00010), Spanish Government (grant PID2022-142181OB-I00 -LearnFDT) and Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101139172 (6G-PATH) and 101192633 (6G-VERSUS).
Funding
Open access funding provided by Institute for Software Engineering and Software Technology of University of Malaga (ITIS Software).
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Soula, A., Zayas, A.D., Ksantini, R. et al. An effective BiLSTM-CNN model for predicting large-scale temporal-spatial dynamics of normalized difference vegetation index.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-55191-8
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DOI: https://doi.org/10.1038/s41598-026-55191-8
Keywords
- Deep learning
- Normalized difference vegetation index
- Meteorological data
- Remote sensing data
- Bidirectional long short-term memory (BiLSTM)
- Convolutional neural network (CNN)
Source: Ecology - nature.com
