the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global near real-time 500 m 10-day FPAR dataset from MODIS and VIIRS for operational agricultural monitoring and crop yield forecasting
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).
- Preprint
(13916 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on essd-2025-287', Hongliang Fang, 24 Jun 2025
The work presents a NRT global dataset of FPAR generated from existing MODIS and VIIRS datasets. The new dataset combines MODIS-FPAR for 2000 to 2023, and the intercalibrated VIIRS-FPAR for 2018 onward. Detailed procedures to smooth and filter the original datasets were introduced, together with fine quality assessment and intercalibration studies. The dataset is openly available and should be valuable for crop monitoring studies. I recommend its publication in ESSD.
L73-75 I think JRC once generated a FPAR product from AVHRR. Please check it
https://www.mdpi.com/2072-4292/11/24/3055
L146. For the MCD12Q1 biome map, I think a better reference would be Friedl and Sulla-Menash (2019), as that cited for Fig. B1. Indeed, the MODIS LAI C6.1 (Myneni, 2020) has used the MCD12Q1 biome map.
Citation: https://doi.org/10.5194/essd-2025-287-RC1 -
RC2: 'Reply on RC1', Hongliang Fang, 25 Jun 2025
For L73-75, there are a few FPAR products existing (doi: 10.1109/TGRS.2018.2818929). For example:
1. GEOV2-FPAR product
Verger, A., Baret , F., Weiss, M., 2020. Algorithm Theoretical Basis Document - GEOV2/AVHRR: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Fraction of green Vegetation Cover (FCOVER) from LTDR AVHRR (Issue I2.50), Report for THEIA-SP-44-0207-CREAF, https://www.theia-land.fr/wp-content/uploads/2022/03/THEIA-SP-44-0207-CREAF_I2.50.pdf, p. 51.
2. FPAR3g from Boston University (http://www.mdpi.com/2072-4292/5/2/927)
3. JRC-TIP FAPAR (https://doi.org/10.3390/rs11243055)
Citation: https://doi.org/10.5194/essd-2025-287-RC2
-
RC2: 'Reply on RC1', Hongliang Fang, 25 Jun 2025
-
RC3: 'Comment on essd-2025-287', Kai Yan, 23 Jul 2025
This paper presents a comprehensive methodology for generating a global near real-time (NRT) FPAR dataset at 500m resolution with 10-day temporal resolution, combining MODIS and VIIRS satellite data for agricultural monitoring and crop yield forecasting applications. The authors employ a Whittaker smoother-based filtering approach with quality-based weighting and constraint mechanisms to handle sparse observations due to cloud cover. The dataset provides multiple consolidation stages (C0 to CF) representing increasing data quality over time, and includes intercalibrated VIIRS-FPAR data to ensure continuity beyond the MODIS mission lifetime. The work addresses an important operational need for consistent, gap-filled vegetation monitoring data for early warning systems and crop yield forecasting. Nevertheless, there are still improvements for this manuscript. My detailed comments are as follows:
Major Comments
- This work presents a valuable operational implementation of established techniques, specifically for agricultural monitoring. I'm curious about the specific adaptations made to the Whittaker smoother approach compared to previous implementations. Could the authors provide more detail on what aspects of their constraint mechanisms or gap-filling procedures are novel or represent improvements over existing methods? This would help readers better understand the technical contributions.
- The quality layers provide valuable information about processing conditions, but I'm curious about how measurement uncertainties from the original MODIS and VIIRS products affect the final filtered results. This information would be particularly valuable for users needing to understand confidence levels in their applications.
- The evaluation metrics are appropriate. I wonder if seasonal or regional performance variations might provide additional insights into the method's behavior under different conditions.
Minor Comments
- The paper is generally well-written with clear explanations of the methodology. There are a few minor issues (e.g., "reangeland" should be "rangeland" in several places) that could be addressed during revision.
- More justification for the λ=3000 parameter selection would be helpful
- I note that "hindacsting" in line 215 should be corrected to "hindcasting" and "poral resolution" in line 90 to "temporal resolution". Please check for similar cases.
Citation: https://doi.org/10.5194/essd-2025-287-RC3
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
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
298 | 96 | 25 | 419 | 9 | 29 |
- HTML: 298
- PDF: 96
- XML: 25
- Total: 419
- BibTeX: 9
- EndNote: 29
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1