Impact of Planet Fusion Surface Reflectance Data on Crop Biomass and Carbon Budget Estimates Within the AgriCarbon-EO Processing Chain.
Saved in:
| Title: | Impact of Planet Fusion Surface Reflectance Data on Crop Biomass and Carbon Budget Estimates Within the AgriCarbon-EO Processing Chain. |
|---|---|
| Authors: | Geraud, Andréa1,2 (AUTHOR) andrea.geraud@netcarbon.fr, Al Bitar, Ahmad1,2 (AUTHOR), Pique, Gaétan2,3 (AUTHOR), Houborg, Rasmus3,4 (AUTHOR), Wijmer, Taeken1,4 (AUTHOR), Ceschia, Eric1,2 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 4, p581. 26p. |
| Subjects: | Plant biomass, Spectral sensitivity, Reflectance, Artificial satellites, Carbon sequestration, Geographic information systems, Remote-sensing images, Agricultural remote sensing |
| Geographic Terms: | France |
| Abstract: | Highlights: What are the main findings? We obtain a reduction in uncertainty and bias on estimates of maize and cover crop biomass by assimilating both Sentinel-2 and PlanetFusion data in Agricarbon-EO, compared to assimilating Sentinel-2 data alone The combined use of Sentinel-2 and PlanetFusion data in AgriCarbon-EO did not improve CO2 flux estimates for winter wheat The accuracy of the biomass estimates was more strongly affected by the spectral resolution of the satellite data than by their availability What is the implication of the main finding? Potentially more accurate biomass estimates in regions with important cloud cover and enhanced operationality of the approach by assimilating both PlanetFusion data with Sentinel-2 data in AgriCarbon-EO Remote sensing is commonly employed in agriculture for crop monitoring and environmental studies. However, the accuracy of satellite-based products is impacted by image frequency, spectral sensibility and density, and cloud cover. This study evaluates the impact of assimilating Planet Fusion (PF) data into the AgriCarbon-EO (ACEO) crop modeling chain, alone or combined with Sentinel-2 (S2) data, compared to assimilating (S2) data only. Parallel experiments were conducted using PF data alone, or PF data in addition to S2 data in the simulations. Satellite data are used to estimate the Green Leaf Area Index (GLAI), CO2 flux dynamics, biomass and yields of winter wheat, maize and cover crops in south-west France. We analyzed the data availability and spectral resolution impact when using PF alone, a combination of PF and S2 data, and S2 alone. We demonstrate that PF's lack of red-edge spectral information can lead to GLAI overestimation during vegetation growth. This is mitigated by applying a statistical correction derived from S2-based GLAI. Assimilating both S2 and PF reduces the Root Mean Square Error (RMSE) and bias for biomass estimates by 19.75 g.m−2 and 117.78 g.m−2, respectively, in maize and by 2.15 g.m2 and 13.19 g.m−2 in cover crops, compared to using S2 data alone. However, it did not improve wheat CO2 flux components estimates. We show that the crop biomass accuracy is more strongly affected by the spectral resolution than by image frequency. The synergistic use of S2 and PF data demonstrates potential for improving biomass accuracy, particularly in cloud-prone regions. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? We obtain a reduction in uncertainty and bias on estimates of maize and cover crop biomass by assimilating both Sentinel-2 and PlanetFusion data in Agricarbon-EO, compared to assimilating Sentinel-2 data alone The combined use of Sentinel-2 and PlanetFusion data in AgriCarbon-EO did not improve CO2 flux estimates for winter wheat The accuracy of the biomass estimates was more strongly affected by the spectral resolution of the satellite data than by their availability What is the implication of the main finding? Potentially more accurate biomass estimates in regions with important cloud cover and enhanced operationality of the approach by assimilating both PlanetFusion data with Sentinel-2 data in AgriCarbon-EO Remote sensing is commonly employed in agriculture for crop monitoring and environmental studies. However, the accuracy of satellite-based products is impacted by image frequency, spectral sensibility and density, and cloud cover. This study evaluates the impact of assimilating Planet Fusion (PF) data into the AgriCarbon-EO (ACEO) crop modeling chain, alone or combined with Sentinel-2 (S2) data, compared to assimilating (S2) data only. Parallel experiments were conducted using PF data alone, or PF data in addition to S2 data in the simulations. Satellite data are used to estimate the Green Leaf Area Index (GLAI), CO2 flux dynamics, biomass and yields of winter wheat, maize and cover crops in south-west France. We analyzed the data availability and spectral resolution impact when using PF alone, a combination of PF and S2 data, and S2 alone. We demonstrate that PF's lack of red-edge spectral information can lead to GLAI overestimation during vegetation growth. This is mitigated by applying a statistical correction derived from S2-based GLAI. Assimilating both S2 and PF reduces the Root Mean Square Error (RMSE) and bias for biomass estimates by 19.75 g.m−2 and 117.78 g.m−2, respectively, in maize and by 2.15 g.m2 and 13.19 g.m−2 in cover crops, compared to using S2 data alone. However, it did not improve wheat CO2 flux components estimates. We show that the crop biomass accuracy is more strongly affected by the spectral resolution than by image frequency. The synergistic use of S2 and PF data demonstrates potential for improving biomass accuracy, particularly in cloud-prone regions. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18040581 |
