Reconstructing NDVI time series in cloud-prone regions: A fusion-and-fit approach with deep learning residual constraint

Abstract

The time series data of Normalized Difference Vegetation Index (NDVI) is crucial for monitoring changes in terrestrial vegetation. Existing reconstruction methods encounter challenges in areas prone to clouds, primarily due to inadequate utilization of spatial, temporal, periodic, and multi-sensor information, as well as a lack of physical interpretations. This frequently results in limited model performance or the omission of spatial details when predicting scenarios involving land cover changes. In this study, we propose a novel approach named Residual (Re) Constraints (Co) fusion-and-fit (ReCoff), consisting of two steps: ReCoF fusion (F) and Savitzky-Golay (SG) fit. This approach addresses the challenges of reconstructing 30 m Landsat NDVI time series data in cloudy regions. The fusion-fit process captures land cover changes and maps them from MODIS to Landsat using a deep learning model with residual constraints, while simultaneously integrating multi-dimensional, multi-sensor, and long time-series information. ReCoff offers three distinct advantages. First, the fusion results are more robust to land cover change scenarios and contain richer spatial details (RMSE of 0.091 vs. 0.101, 0.164, and 0.188 for ReCoF vs. STFGAN, FSDAF, and ESTARFM). Second, ReCoff improves the effectiveness of reconstructing dense time-series data (2016–2020, 16-day interval) in cloudy areas, whereas other methods are more susceptible to the impact of prolonged data gaps. ReCoff achieves a correlation coefficient of 0.84 with the MODIS reference series, outperforming SG (0.28), HANTS (0.32), and GF-SG (0.48). Third, with the help of the GEE platform, ReCoff can be applied over large areas (771 km × 634 km) and long-time scales (bimonthly intervals from 2000 to 2020) in cloudy regions. ReCoff demonstrates potential for accurately reconstructing time-series data in cloudy areas.