NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Peiqiang Gao, Wenfeng Du (), Qingwen Lei, Juezhi Li, Shuaiji Zhang and Ning Li
Additional contact information
Peiqiang Gao: China University of Mining and Technology-Beijing
Wenfeng Du: China University of Mining and Technology-Beijing
Qingwen Lei: Hebei University of Engineering
Juezhi Li: China University of Mining and Technology-Beijing
Shuaiji Zhang: China University of Mining and Technology-Beijing
Ning Li: China University of Mining and Technology-Beijing

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 4, No 2, 1497 pages

Abstract: Abstract Normalized difference vegetation index (NDVI) is the most widely used factor in the growth status of vegetation, and improving the prediction of NDVI is crucial to the advancement of regional ecology. In this study, a novel NDVI forecasting model was developed by combining time series decomposition (TSD), convolutional neural networks (CNN) and long short-term memory (LSTM). Two forecasting models of climatic factors and four NDVI forecasting models were developed to validate the performance of the TSD-CNN-LSTM model and investigate the NDVI's response to climatic factors. Results indicate that the TSD-CNN-LSTM model has the best prediction performance across all series, with the RMSE, NSE and MAE of NDVI prediction being 0.0573, 0.9617 and 0.0447, respectively. Furthermore, the TP-N (Temperature & Precipitation-NDVI) model has a greater effect than the T-N (Temperature-NDVI) and P-N (Precipitation-NDVI) models, according to the climatic factors-based NDVI forecasting model. Based on the results of the correlation analysis, it can be concluded that changes in NDVI are driven by a combination of temperature and precipitation, with temperature playing the most significant role. The preceding findings serve as a helpful reference and guide for studying vegetation growth in response to climate changes.

Keywords: Normalized difference vegetation index; Climatic factors; Time series analysis; Prediction models; TSD-CNN-LSTM (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03419-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:37:y:2023:i:4:d:10.1007_s11269-022-03419-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-022-03419-3

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().