Preprints
https://doi.org/10.5194/essd-2025-287
https://doi.org/10.5194/essd-2025-287
03 Jun 2025
 | 03 Jun 2025
Status: this preprint is currently under review for the journal ESSD.

Global near real-time 500 m 10-day FPAR dataset from MODIS and VIIRS for operational agricultural monitoring and crop yield forecasting

Lorenzo Seguini, Anja Klisch, Michele Meroni, Anton Vrieling, Giacinto Manfron, Clement Atzberger, and Felix Rembold

Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10-day filtered timeseries: 1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and 2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: a inital estimate immediately after the 10-day reference period, four subsequent estimates every 10 days using new observations, and a final consolidated estimate 90 days later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Share
Lorenzo Seguini, Anja Klisch, Michele Meroni, Anton Vrieling, Giacinto Manfron, Clement Atzberger, and Felix Rembold

Status: open (until 10 Jul 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Lorenzo Seguini, Anja Klisch, Michele Meroni, Anton Vrieling, Giacinto Manfron, Clement Atzberger, and Felix Rembold

Data sets

A global near real-time filtered 500m 10-day FPAR dataset from MODIS and VIIRS instruments, suited for operational agricultural monitoring and crop yield forecasting Lorenzo Seguini, Anja Klisch, Michele Meroni, Clement Atzberger, Anton Vrieling, Giacinto Manfron, Felix Rembold https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50

Lorenzo Seguini, Anja Klisch, Michele Meroni, Anton Vrieling, Giacinto Manfron, Clement Atzberger, and Felix Rembold

Viewed

Total article views: 31 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
27 4 0 31 0 0
  • HTML: 27
  • PDF: 4
  • XML: 0
  • Total: 31
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 03 Jun 2025)
Cumulative views and downloads (calculated since 03 Jun 2025)

Viewed (geographical distribution)

Total article views: 31 (including HTML, PDF, and XML) Thereof 31 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Jun 2025
Download
Short summary
We released a consistent MODIS-VIIRS FPAR timeseries of global FPAR data at 500 m resolution, updated every 10 days since 2000. This dataset is filtered and optimized for agricultural applications and meets the operational needs of the systems for early warning, and crop yield forecasting. We have generated a VIIRS-FPAR timeseries, corrected over the MODIS-FPAR data, to guaranty timeseries continuity beyond 2030, thanks to the future VIIRS missions. Data are freely available.
Share
Altmetrics