Li et al. (2025) NRT-GSF: A novel near-real-time ground-satellite fusion algorithm to retrieve daily green area index at field scale

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-11-25
• Authors: Wenjuan Li, Marie Weiss, Samuel Buis, Aleixandre Verger, Sylvain Jay, Zihan Ren, WU Wenbin, Jingyi Jiang, Alexis Comar, Benoît de Solan
• DOI: 10.1016/j.rse.2025.115160

Research Groups

• State Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
• National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
• UMR EMMAH 1114, INRAE, Avignon Universit´e, Avignon, France
• CIDE, CSIC-UV-GVA, Spain
• The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, China
• HIPHEN SAS, Avignon, France
• Arvalis, Institut du Vegetal, Avignon, France

Short Summary

This study developed the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, integrating Sentinel-2 imagery with IoTA system data to generate daily 10-meter Green Area Index (GAI) products for precision agriculture. The algorithm demonstrated enhanced spatiotemporal completeness and accuracy, offering a robust solution for near-real-time, high-resolution crop GAI mapping.

Objective

• To develop a novel near-real-time ground-satellite fusion algorithm (NRT-GSF) that integrates Sentinel-2 imagery with continuous Internet of Things for Agriculture (IoTA) measurements to retrieve daily Green Area Index (GAI) at the field scale, thereby overcoming limitations in spatial and temporal resolution for precision agriculture.

Study Configuration

• Spatial Scale: Field scale, 10-meter resolution, implemented over French wheat fields.
• Temporal Scale: Daily, near-real-time (NRT) mode, continuous measurements, using Sentinel-2 time series from 2019.

Methodology and Data

• Models used: Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, based on a Bayesian dynamic linear model and Kalman filtering. The algorithm was compared against the Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm.
• Data sources: Sentinel-2 imagery, continuous measurements from 34 Internet of Things for Agriculture (IoTA) systems, and ground validation data from handheld RGB cameras.

Main Results

• The NRT-GSF algorithm effectively enhanced spatiotemporal completeness and accuracy of daily GAI products at the field scale.
• Evaluation against Sentinel-2 data showed high accuracy with a correlation coefficient (R) ranging from 0.75 to 0.98 and a Root Mean Square Error (RMSE) between 0.1 and 0.49.
• Ground validation using handheld RGB cameras confirmed the accuracy of the GAI products with an RMSE of 0.5.
• A comprehensive leave-one-out Sentinel-2 evaluation demonstrated the NRT-GSF algorithm's superiority over the current CACAO algorithm.
• The algorithm's recursive framework supports both forward prediction in NRT mode following satellite overpasses and backward updating to refine historical GAI profiles.

Contributions

• Development of a novel, robust, and operationally viable near-real-time ground-satellite fusion algorithm (NRT-GSF) for daily, high-resolution crop GAI mapping.
• Unique integration of Sentinel-2 imagery with continuous IoTA system measurements to address the trade-off between spatial and temporal resolution in crop monitoring.
• Introduction of a recursive framework that enables both forward prediction for near-real-time applications and backward updating for historical data refinement.
• Demonstrated superiority over existing spatiotemporal fusion techniques (e.g., CACAO) in terms of NRT capability and accuracy.
• Provides a foundational framework extendable to other crop traits or applications requiring near-real-time, high-resolution monitoring.

Funding

The provided paper text does not contain specific funding information.

Citation

@article{Li2025NRTGSF,
  author = {Li, Wenjuan and Weiss, Marie and Buis, Samuel and Verger, Aleixandre and Jay, Sylvain and Ren, Zihan and Wenbin, WU and Jiang, Jingyi and Comar, Alexis and Solan, Benoît de},
  title = {NRT-GSF: A novel near-real-time ground-satellite fusion algorithm to retrieve daily green area index at field scale},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115160},
  url = {https://doi.org/10.1016/j.rse.2025.115160}
}