Work

Reconstructing satellite temporal series data under cloudy conditions: Application in predicting rice growth phases

Year: 2025

Type: article

Abstract: Vegetation indices derived from satellite observations, particularly the NDVI, are widely used for monitoring crop development and estimating yields. In tropical regions such as Indonesia, however, persistent cloud cover causes large gaps, undermining the reliability of NDVI time series and limiting their agricultural utility. This study proposes a two-stage spatiotemporal reconstruction framework to enhance NDVI time series under such conditions. The approach combines spatiotemporal filling with multistep temporal smoothing through an interval-based strategy that accommodates diverse planting schedules and performs effectively on shorter time series. Experiments with simulated datasets demonstrated that the method consistently outperformed benchmark techniques, achieving the lowest reconstruction errors compared to the Savitzky–Golay filter and cubic spline smoothing. Applied to Sentinel-2 NDVI from 2,460 rice field sites across three Indonesian regions, the method enhanced rice growth phase classification with a random forest model, improving precision by up to 19%, recall by 24%, and F1-score by 21%. These results demonstrate the framework’s robustness in handling large cloud-induced gaps and its potential for near-real-time agricultural monitoring.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101378

Authors: B. Suseno, G. Brunel, H. Wijayanto, K. Sadik, F.M. Afendi, B. Tisseyre

Institutions: IPB University, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Technologies & méthodes pour les agricultures de demain

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Zero hunger

Open Access status: gold

Estimated APC paid: $1,770