Abstract
Long-term time series of global leaf area index (LAI) and fraction of absorbed photosynthetic active radiation (FAPAR) are required for characterizing vegetation dynamics in global change studies. The recently developed Copernicus Land Monitoring Service GEOV2-CLMS products were demonstrated to outperform other existing LAI and FAPAR products in terms of completeness, temporal smoothness, consistency across variables and accuracy. However, these GEOV2-CLMS products are derived from the SPOT/VGT and PROBA-V constellation with temporal coverage from 1999 to 2020 which limits its applicability for global change studies. We present here an adaptation of the GEOV2-CLMS algorithm to AVHRR to extend these time series and generate long-term global vegetation products from July 1981 to December 2022. The GEOV2-AVHRR algorithm was specifically designed to maximize the temporal consistency over the successive AVHRR sensors on board NOAA and MetOp-B satellites while keeping high agreement with GEOV2-CLMS products. Neural networks first transform AVHRR surface reflectance into LAI and FAPAR values at the daily time step. The daily estimates are then filtered, smoothed, gap filled and composited every 10-day. GEOV2-AVHRR showed accuracy error between −0.2 and 0.3 LAI and ∼ −0.03 FAPAR and uncertainty <1 LAI and ∼ 0.10–0.15 for woody and non-woody sites of both DIRECT2.1 and GBOV V3 datasets. GEOV2-AVHRR agreed well with GEOV2-CLMS (MODIS): 92 % (76 %) of land pixels are within ±max(20 %, 0.5) LAI and 71 % (34 %) within ±max(10 %, 0.05) FAPAR uncertainty requirements. The gap filling and temporal filters applied in GEOV2-AVHRR proved effective in improving the completeness (only 1 % of missing data) and temporal precision (smoothness) of LAI and FAPAR time series as compared to MODIS. The intra-annual consistency of GEOV2-AVHRR highly agree with GEOV2-CLMS, indicating it is mostly driven by the algorithm. On the contrary, the inter-annual consistency of LAI and FAPAR datasets appears to be very sensitive to the consistency of the input surface reflectance. GEOV2-AVHRR showed high stability as evaluated with MODIS LAI/FAPAR and improves the stability of GEOV2-CLMS. Some residual inter-annual inconsistencies from the transition to sensors are observed for GEOV2-AVHRR as well as for other long term AVHRR datasets (i.e. GIMMS, GLASS and C3S). GEOV2-AVHRR shows overall greening trends in ∼70 % (∼50 % significant at p < 0.05) of land pixels, and the magnitude and spatial pattern of trends highly agree with those of GIMMS.
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Claims 16
Claim · model-written summary
GEOV2-AVHRR provides vegetation products for July 1981 to December 2022.
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e0“generate long-term global vegetation products from July 1981 to December 2022”
Claim · model-written summary
LAI accuracy error ranged from −0.2 to 0.3.
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e0“accuracy error between −0.2 and 0.3 LAI”
Claim · model-written summary
FAPAR accuracy error was about −0.03.
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Verbatim
e0“∼ −0.03 FAPAR”
Claim · model-written summary
LAI uncertainty was below one.
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Verbatim
e0“uncertainty <1 LAI”
Claim · model-written summary
FAPAR uncertainty was about 0.10–0.15.
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e0“∼ 0.10–0.15 for woody and non-woody sites”
Claim · model-written summary
For LAI, 92% of pixels met uncertainty requirements relative to GEOV2-CLMS.
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Verbatim
e0“92 % (76 %) of land pixels are within ±max(20 %, 0.5) LAI”
Claim · model-written summary
For FAPAR, 71% of pixels met uncertainty requirements relative to GEOV2-CLMS.
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Verbatim
e0“71 % (34 %) within ±max(10 %, 0.05) FAPAR uncertainty requirements”
Claim · model-written summary
Gap filling and filters improved completeness, leaving 1% missing data.
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Verbatim
e0“improving the completeness (only 1 % of missing data)”
Claim · model-written summary
Gap filling and filters improved temporal precision of LAI and FAPAR.
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Verbatim
e0“temporal precision (smoothness) of LAI and FAPAR time series as compared to MODIS”
Claim · model-written summary
Intra-annual consistency agreed with GEOV2-CLMS.
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Verbatim
e0“The intra-annual consistency of GEOV2-AVHRR highly agree with GEOV2-CLMS”
Claim · model-written summary
Inter-annual consistency was sensitive to input reflectance consistency.
- Outcome
- inter-annual consistency of LAI and FAPAR datasetse0
- Qualifier
- consistency of the input surface reflectancee0
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Verbatim
e0“inter-annual consistency of LAI and FAPAR datasets appears to be very sensitive to the consistency of the input surface reflectance”
Claim · model-written summary
GEOV2-AVHRR showed high stability and improved GEOV2-CLMS stability.
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Verbatim
e0“GEOV2-AVHRR showed high stability as evaluated with MODIS LAI/FAPAR and improves the stability of GEOV2-CLMS”
Claim · model-written summary
Residual inter-annual inconsistencies occurred at sensor transitions.
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Verbatim
e0“Some residual inter-annual inconsistencies from the transition to sensors are observed for GEOV2-AVHRR”
Claim · model-written summary
GEOV2-AVHRR indicates greening in about 70% of land pixels.
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Verbatim
e0“overall greening trends in ∼70 % (∼50 % significant at p < 0.05) of land pixels”
Claim · model-written summary
About 50% of pixels show significant greening.
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Verbatim
e0“∼50 % significant at p < 0.05”
Claim · model-written summary
Trend magnitude and spatial pattern agreed with GIMMS.
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Verbatim
e0“magnitude and spatial pattern of trends highly agree with those of GIMMS”