Claim · model-written summary
For FAPAR, 71% of pixels met uncertainty requirements relative to GEOV2-CLMS.
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirection
Verbatim
e0“71 % (34 %) within ±max(10 %, 0.05) FAPAR uncertainty requirements”
Title
Aleixandre Verger; Marie Weiss; Frédéric Baret Remote Sensing of Environment 2025. doi:10.1016/j.rse.2025.115029
Other claims from this paper (15)
- Method / resource GEOV2-AVHRR provides vegetation products for July 1981 to December 2022.
- Method / resource LAI accuracy error ranged from −0.2 to 0.3.
- Method / resource FAPAR accuracy error was about −0.03.
- Method / resource LAI uncertainty was below one.
- Method / resource FAPAR uncertainty was about 0.10–0.15.
- Method / resource For LAI, 92% of pixels met uncertainty requirements relative to GEOV2-CLMS.
- Method / resource Gap filling and filters improved completeness, leaving 1% missing data.
- Method / resource Gap filling and filters improved temporal precision of LAI and FAPAR.
- Method / resource Intra-annual consistency agreed with GEOV2-CLMS.
- Association Inter-annual consistency was sensitive to input reflectance consistency.
- Method / resource GEOV2-AVHRR showed high stability and improved GEOV2-CLMS stability.
- Observation Residual inter-annual inconsistencies occurred at sensor transitions.
- Method / resource GEOV2-AVHRR indicates greening in about 70% of land pixels.
- Method / resource About 50% of pixels show significant greening.
- Method / resource Trend magnitude and spatial pattern agreed with GIMMS.