A Dual-Factor-Driven Temporal Network for Pixel-Level NDVI Prediction in the Hulunbuir Grassland

Abstract

Normalized Difference Vegetation Index (NDVI) is an important remote sensing indicator for characterizing vegetation growth status and ecosystem changes. Improving NDVI prediction accuracy is of great significance for regional ecological monitoring and conservation. However, existing prediction methods often rely on single meteorological drivers or numerous explanatory variables that are difficult to obtain for future periods, limiting their applicability to pixel-level NDVI forecasting over spatially distributed areas. In this study, a Dual-Factor-Driven Temporal Network (DfT-Net) was proposed for pixel-level NDVI prediction in the Hulunbuir grassland based on MODIS NDVI data and ERA5-Land temperature and precipitation data from 2013 to 2024. The model was independently applied to valid 1000 m grassland pixels, with the same model parameters shared across pixels. This pixel-wise prediction strategy enables the model to learn common meteorological–vegetation response patterns across different pixels while generating spatially distributed NDVI predictions. For temporal feature modeling, Time Series Decomposition (TSD) was first applied to the temperature and precipitation sequences to decompose them into trend, seasonal, and residual components, thereby characterizing their multi-scale temporal variations and recurrent seasonal patterns. Subsequently, one-dimensional convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) were employed to extract local temporal patterns and long-term temporal dependencies, respectively. Experimental results showed that DfT-Net achieved an RMSE of 0.100, an MAE of 0.074, and an R2 of 0.828 on the independent temporal test set (2022–2024). Under the same experimental setting, DfT-Net achieved the lowest RMSE and MAE and the highest R2 among the evaluated models, including LSTM, CNN, TSD-CNN, TSD-LSTM, and CNN-LSTM. These results indicate that DfT-Net effectively integrates dual-factor meteorological driving, multi-scale temporal feature representation, and pixel-level prediction, providing a useful framework for spatially distributed grassland NDVI forecasting and ecological monitoring.