# GEOV2-AVHRR: Continuous and consistent time series of global leaf area index and fraction absorbed PAR from 1981 to 2022

Aleixandre Verger, Marie Weiss, Frédéric Baret

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Aleixandre Verger, Marie Weiss, Frédéric Baret. GEOV2-AVHRR: Continuous and consistent time series of global leaf area index and fraction absorbed PAR from 1981 to 2022. Remote Sensing of Environment, 2025, 331, pp.115029. 10.1016/j.rse.2025.115029. hal-05327986

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# GEOV2-AVHRR: Continuous and consistent time series of global leaf area index and fraction absorbed PAR from 1981 to 2022

Aleixandre Verger<sup>a,b,*</sup>, Marie Weiss<sup>c</sup>, Frédéric Baret<sup>c</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

Editor: Jing M. Chen

**Keywords:**

GEOV2

AVHRR

Long-term global vegetation monitoring

LAI

FAPAR

Essential climate variables

Validation

## 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.

## 1. Introduction

Continuous, consistent and validated long time series of vegetation biophysical variables at the global scale are essential to describe the vegetation dynamics and understand the interaction with the climate system. Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are recognized by the Global Climate Observing System (GCOS) as Essential Climate Variables (GCOS, 2011,

2022).

Since 2000, spatial agencies have deployed multiple efforts to derive LAI and FAPAR products from Earth Observation systems specifically designed for monitoring the vegetation, including MODIS (Myneni et al., 2002), SPOT/VGT (Baret et al., 2007), PROBA-V (Baret et al., 2013) or VIIRS (Yan et al., 2018). However, the different validation exercises (Brown et al., 2020; Fang et al., 2012; Fuster et al., 2020; Weiss et al., 2007; Yan et al., 2016) showed inequal performances depending on

<footnote><sup>a</sup> CIDE, CSIC-UV-GVA, 46113 València, Spain</footnote>

<footnote><sup>b</sup> CREAF, 08193 Cerdanyola del Vallès, Catalonia, Spain</footnote>

<footnote><sup>c</sup> INRAE UMR114 EMMAH, Domaine Saint-Paul, Site Agroparc, 84914 Avignon, France</footnote>

<footnote>* Corresponding author at: CIDE, CSIC-UV-GVA, 46113 València, Spain.
E-mail address: verger@csic.es (A. Verger).</footnote>
biome types and sites because of the differences between sensor char-
acteristics and the choice made when designing the pre-processing and
product derivation algorithms. Therefore, GEOV2-CLMS products
(Verger et al., 2023), e.g. version 2 of Copernicus Global Land Service 1
km LAI, FAPAR products derived from SPOT/VGT and PROBA-V for the
period from January 1999 to June 2020, were designed to balance the
known pros and cons of already existing CYCLOPES (Baret et al., 2007),
MODIS (Myneni et al., 2002) and GEOV1-CLMS (Baret et al., 2013)
products. While GEOV2-CLMS have the same level of accuracy as the
previous version GEOV1-CLMS, its completeness, as well as the
smoothness and consistency of the seasonal trajectories significantly
improved GEOV1-CLMS and other products including MODIS ones,
especially for high latitude canopies and tropical forests (Verger et al.,
2023). However, despite these good performances, the temporal
coverage of this dataset limits its applicability for long-term vegetation
studies.

The series of Advanced Very High-Resolution Radiometer (AVHRR)
sensors abord NOAA and MetOp satellites provide the longest and
continuous time series of global observations from which several LAI/
FAPAR products have been already derived including Global Land
Surface Satellite (GLASS) products (Xiao et al., 2015; Xiao et al., 2016),
the third generation Global Inventory Monitoring and Modelling System
(GIMMS LAI3g and FAPAR3g) (Zhu et al., 2013), the terrestrial climate
data record (TCDR) (Claverie et al., 2016), the Long-term Global Map-
ping (GLOBMAP LAI) (Liu et al., 2012), and the Copernicus Climate
Change Service (C3S) (Blessing and Giering, 2021). However, like for
moderate resolution satellite products, validation studies showed in-
consistencies between the existing long-term AVHRR datasets (Tian
et al., 2015). This effect was actually enhanced by the limitations of
AVHRR instruments (e.g. broad spectral bands sensitive to atmospheric
water absorption), satellite orbital drift, sensor degradation, spectral
differences and inter-calibration issues (Santamaria-Artigas et al.,
2021). Jiang et al. (2017) compared GLASS, GLOBMAP, GIMMS3g, and
TCDR LAI products and found significant inconsistencies between them
and over time, which seriously compromises the interpretation of global
changes derived from these products. Ceccherini et al. (2018) compared
GIMMS3g, TCDR and GLOBMAP LAI products during the summer sea-
sons over the northern latitudes from 1982 to 2011 period. They eval-
uated the expected consistency with Vegetation Optical Depth (VOD)
products derived from passive microwave used as a proxy of biomass.
They found partial agreement between TCDR and GIMMS3g, while
larger discrepancies were observed with GLOBMAP. Xiao et al. (2017)
compared GLASS, GLOBMAP, GIMMS3g and TCDR LAI products over
the 1982–2011 period. They repeated the comparison exercise for the
GLASS, GIMMS3g and TCDR FAPAR products (Xiao et al., 2017). They
found differences in completeness as well as in magnitude and season-
ality mainly over tropical vegetation and northern latitudes. For LAI
products, GLASS, GIMMS3g and TCDR showed mostly a greening trend
conversely to GLOBMAP that was showing a dominant browning trend.
However, the latest version, GLOBMAP LAI v3.0, demonstrates consis-
tent greening trends compared to other products (Piao et al., 2020).

Long time series of vegetation biophysical variables have been
instrumental to identify vegetation trends, to evaluate and calibrate
Earth system models, and to quantify the productivity of natural and
cultivated vegetation (Piao et al., 2015; Zhu et al., 2016). However, the
success in such applications of global remote sensing observations rely
strongly on the quality of the time series of vegetation biophysical
products (Tian et al., 2015). For example, Wang et al. (2020b) reported a
reversed global greening since 2000 and a decline of CO₂ fertilization
effects. However, these controversial findings were found to be sensitive
to artifacts in the satellite datasets including systematic biases in AVHRR
surface reflectance and orbital drift (Frankenberg et al., 2021). More
recent studies based on updated and corrected AVHRR datasets reported
persistent global greening during the last 40 years (Chen et al., 2024;
Jeong et al., 2024).

The development of new LAI and FAPAR products able to reconcile

the limitations of existing long-term datasets is therefore highly required
in order to get a consistent and continuous long time series across the
AVHRR archive starting back from 1981 up to present. In this sense, Cao
et al. (2023) and Zhao et al. (2024) recently developed a new version of
GIMMS LAI4g and FAPAR4g product from 1982 to 2020/2022 which
outperforms GIMMS3g and GLASS and corrects the effects of satellite
orbital drift and sensor degradation (Tian et al., 2015).

Because of the good performances of GEOV2-CLMS, we aimed to
extend these LAI and FAPAR time series in a consistent way by using the
AVHRR Long Term Data Record (LTDR) which provides surface reflec-
tance data from mid-1981 to present, using up to date algorithms to
correct from atmospheric and bidirectional effects (Franch et al., 2017).

This paper aims therefore to describe the development of the so-
called GEOV2-AVHRR LAI, FAPAR biophysical products from the
AVHRR-LTDR dataset since 1981, designed to keep a high consistency
with GEOV2-CLMS. The principles used to generate the GEOV2-AVHRR
products are first presented. Then, the characteristics of the GEOV2-
AVHRR products and the associated quality indicators are described.
The accuracy of LAI and FAPAR products was assessed with the recently
updated DIRECT2.1 and GBOV V3 ground datasets. We evaluated the
consistency of GEOV2-AVHRR with GEOV2-CLMS and MODIS products
and with other AVHRR long-term datasets. Special attention is paid on
the spatio-temporal continuity and consistency across products, vari-
ables, and sensors, and the analysis of anomalies and trends. Besides the
evaluation of the performances of the GEOV2-AVHRR products against
products derived from the same sensors and more recent ones, we
address the following research questions:

(1) What is the impact of the preprocessing (including sensor choice and input data) and postprocessing (temporal smoothing and gap filling algorithms) in the intra-annual and inter-annual consistency of LAI/FAPAR long-term time series?

(2) Are the anomalies and trends extracted from the different products consistent and can we explain their differences?

## 2. Data and methods

### 2.1. AVHRR long term data records

The LTDR dataset (https://ladsweb.modaps.eosdis.nasa.gov) provides global top of canopy directionally normalized (nadir viewing and 45° solar zenith angle, SZA) reflectance from the AVHRR red (580-680 nm) and near infrared (NIR, 720-1100 nm) channels (Fig. S1) at 0.05° spatial resolution (5.6 km at equator) and daily sampling interval (Franch et al., 2017; Santamaria-Artigas et al., 2021). The quality flags associated with the AVHRR surface reflectance were used to remove invalid values contaminated by clouds or BRDF (Bidirectional Reflectance Distribution Function) correction issues. The time series from 1981 to 2022 period includes records from AVHRR-2 sensor onboard the National Oceanic and Atmospheric Administration NOAA-07, -09, -11 and -14 satellites, and AVHRR-3 sensor onboard NOAA-16, -18, -19 and the Meteorological Operational MetOp-B satellites (Table S1). Although version 6 was released in mid-2023, we used the latest available LTDR version at the time of generation of GEOV2-AVHRR time series, e.g. LTDR v4 for the GEOV2-AVHRR products derived from NOAA data before 2017 and LTDR v5 for the products derived from MetOp-B data after 2017. Since LTDR v4 data was missing from 1st January to 2nd November 2000, for this period, we derived GEOV2-AVHRR product using LTDR v3.

## 2.2. GEOV2-AVHRR algorithm principles and product description

### 2.2.1. Algorithm principles

GEOV2-AVHRR (Fig. S2) is based on the GEOV2-CLMS algorithm
(Verger et al., 2023) with some adaptations to the specificities of AVHRR
in the data preprocessing, algorithm calibration and tunning of
parameters.

The LTDR reflectance were first spectrally harmonized to correct for the differences in the spectral responses of the different NOAA-7/9/11/ 14/16/18/19 and MetOp-B AVHRR sensors (Fig. S1). A third degree polynomial correction with the NDVI (Trishchenko et al., 2002) was applied for each AVHRR spectral reflectance relative to the reflectance of NOAA-16/ AVHRR-3, which was considered as the reference sensor. The correction coefficients (Table S2) were fitted on simulations based on the PROSAIL radiative transfer model (Jacquemoud et al., 2009). The NOAA-16 was chosen as the reference for the harmonization since it covers the period common with other sensors used to design the product and does not present artifacts (like systematic biases in surface reflectance and orbital drift in NOAA-14 (Frankenberg et al., 2021; Santa-maria-Artigas et al., 2021)).

The GEOV2-AVHRR retrieval algorithm relies on neural networks trained with CYCLOPES version 3.1 (Baret et al., 2007) and MODIS Collection 5 (Myneni et al., 2002) products over the BELMANIP2.1 (Weiss et al., 2014) sites for the 2003–2007 period during which NOAA-16/AVHRR-3 surface reflectance observations were coexisting. After applying the spectral harmonization between the NOAA and MetOp-B AVHRR constellation of sensors, the input data are the AVHRR top of canopy directionally normalized daily reflectance in the red and NIR bands and the cosine of the SZA at 10:00 local solar time for FAPAR products. The outputs of the neural networks are daily LAI and FAPAR estimates (Fig. S3).

To get continuous and more robust products every 10 days, the daily LAI and FAPAR values are then filtered, smoothed, gap filled and composited using the same methodology as for GEOV2-CLMS (Verger et al., 2023) with minor tuning (Fig. S2). A second-degree polynomial was fitted over a dissymmetric temporal window with a length varying between ±30 and ±60 days depending on the number of valid available observations (at least six daily estimates in each semi-window). A filtering procedure based on the distance to the smooth temporal profiles resulting from the polynomial fitting was then applied to remove outliers (Fig. S4). For the periods with few available observations a gap filling based on the Consistent Adjustment of Climatology to Actual Observations (CACAO) method (Verger et al., 2013) was applied. Finally, to fill the residual gaps when the climatology was not available, a linear interpolation within a ±60-days window was applied. If no data exists within the 120 days, it results in a missing data. More details are provided in the Algorithm Theoretical Basis Document (Verger et al., 2024).

### 2.2.2. GEOV2-AVHRR product description and quality indicators

The GEOV2-AVHRR products (https://doi.org/10.24400/329360/GEOV2_AVHRR) are disseminated using FAIR principles by the Data Terra Research Infrastructure (Continental Surfaces Data Hub – THEIA) through the CNES GEODES portal (https://geodes.cnes.fr). They are delivered in hdf5 format. They are distributed at the original spatial resolution of 0.05° x 0.05° and aggregated at 0.5° x 0.5° latitude-longitude grid. The temporal frequency is 10-day (for dates 5th, 15th and 25th of each month). The products cover the period July 1981–December 2022.

GEOV2 LAI product corresponds to the true LAI (Verger et al., 2023). GEOV2 FAPAR product corresponds to the instantaneous black-sky FAPAR under direct illumination at 10:00 a.m. solar local time, which is a close approximation to the daily integrated FAPAR under clear-sky conditions (Baret et al., 2003; Fensholt et al., 2004). In addition to LAI and FAPAR, GEOV2-AVHRR provides green fractional vegetation cover, but not described in this paper.

Several quality indicators are associated to each product for a given pixel every 10-day. They include the land cover of the pixel (water, evergreen broadleaf forest (EBF) or bare soil (BS) issued from GEOCLIM), the number of observations, the length of the compositing window, the pathway used to fill the gaps in the time series and a proxy of the uncertainty computed as the root-mean-square error (RMSE)

between the daily LAI/FAPAR estimates and the final 10-day GEOV2-AVHRR products after composition (Table 1, Figs. S5-S6).

### 2.3. Other LAI and FAPAR products considered

We first compared GEOV2-AVHRR with GEOV2-CLMS since GEOV2-AVHRR products were specifically designed to ensure consistency with GEOV2-CLMS. We also compared GEOV2-AVHRR with MODIS dataset, which is here considered as a benchmark to evaluate the uncertainty of GEOV2-AVHRR time series and, most importantly, their stability.

Then we compared GEOV2-AVHRR with existing long-term datasets which are derived mainly from the AVHRR series and presenting a long enough and common period. We selected datasets including both LAI and FAPAR variables: the two most popular and verified global long-term datasets, GLASS (version 5) and GIMMS LAI4g and FAPAR4g (version 1), and the recent Copernicus Climate Change C3S (version 3) LAI/FAPAR dataset which was not fully validated before in previous papers.

The comparison with GEOV2-CLMS, which is derived from the same retrieval algorithm, allows to evaluate the impact of sensor choice, input data and preprocessing on the derived LAI and FAPAR time series. The comparison with GLASS, which is derived from the same LTDR input data, allows to evaluate the impact of the retrieval algorithm (i.e. the inversion approach for retrieving LAI and FAPAR from LTDR surface reflectance) and postprocessing (temporal smoothing and gap filling methods) on the LAI and FAPAR time series.

The characteristics of the used LAI and FAPAR products are described in Table 2.

#### 2.3.1. GEOV2-CLMS

The version 2 of Copernicus Global Land Service 1 km LAI/FAPAR products (GEOV2-CLMS) were delivered at 10-day temporal frequency and 1/112° spatial resolution from January 1999 to June 2020. They were derived from SPOT/VGT (1999–2013) and PROBA-V (2014–2020) data using the same principles as described in section 2.2.1 (Verger et al., 2023). The GEOV2-CLMS LAI/FAPAR products are available at https://land.copernicus.eu (accessed September 2025).

#### 2.3.2. HiQ-MODIS

The High-Quality Reprocessed MODIS LAI/FAPAR products (HiQ-MODIS, version 1) (Yan et al., 2024) are available at 8-day temporal resolution and 5 km spatial resolution from 2000 to 2022. They were derived from reprocessed MODIS LAI/FAPAR C6.1 products by the Spatio-Temporal Information Compositing Algorithm (STICA), leading better agreement with ground measurements, and better spatio-temporal continuity and stability than the original MODIS time series (Yan et al., 2024).

The HiQ-MODIS LAI and FAPAR products correspond to the true LAI accounting for possible leaf clumping, and to the instantaneous black-

<table><caption>**Table 1**
Quality indicators associated to each GEOV2-AVHRR product for a given pixel and date.</caption><thead><tr><td>Quality indicator</td><td>Description</td></tr></thead><tbody><tr><td>Land cover</td><td>Indicates whether the pixel is land or sea, evergreen broadleaf forest (EBF) or not, or bare soil (BS) or not</td></tr><tr><td>High latitude winter: HLAT</td><td>Indicates if a specific correction is applied for high northern latitudes (&gt;55°N) at extreme SZA (&gt; 70°)</td></tr><tr><td>Gap Filling</td><td>Indicates if climatology or interpolation is used to fill missing observations</td></tr><tr><td>Product status</td><td>Indicates if the product is invalid</td></tr><tr><td>Number of observations: NOBS</td><td>Number of daily estimates used to generate the 10-day composites</td></tr><tr><td>Length of the compositing period: LENGTH</td><td>Compositing period length in days (between 60 and 120 days)</td></tr><tr><td>Uncertainty: RMSE</td><td>Root-mean-square error (RMSE) between the daily estimates and the 10-day composites</td></tr></tbody></table>
<table><caption>**Table 2**

Characteristics of the LAI and FAPAR products.</caption><thead><tr><th></th><th>GEOV2-AVHRR</th><th>GEOV2-CLMS</th><th>HiQ-MODIS</th><th>GIMMS LAI4g/<br>FAPAR4g</th><th>GLASS V5</th><th>C3S V3</th></tr></thead><tbody><tr><td>Input data</td><td>AVHRR LTDR<br>Directionally<br>normalized red and NIR<br>surface reflectance<br>(NTOC-r)</td><td>VGT & PROBA-V<br>bidirectional red, NIR<br>and SWIR reflectance<br>and sun-view angles</td><td>MODIS LAI & FAPAR<br>(MOD15A2H C6.1)</td><td>PKU GIMMS NDVI<br>(1982-2004/2016),<br>MODIS LAI<br>(2004-2020) and<br>sensor independent<br>FAPAR from MODIS<br>and VIIRS<br>(2016-2022)</td><td>AVHRR LTDR<br>NTOC-r red and NIR</td><td>AVHRR (1981-2005),<br>VGT & PROBA-V<br>(2006-2020) bi-<br>hemispherical red and NIR<br>reflectance</td></tr><tr><td>Spatial<br>sampling</td><td>0.05°</td><td>1/112°</td><td>500 m/5 km</td><td>1/12°</td><td>0.05°</td><td>1/30° (AVHRR);<br>1/112° (VGT)</td></tr><tr><td>Temporal<br>sampling</td><td>10 days</td><td>10 days</td><td>8-day</td><td>15 days</td><td>8 days</td><td>10 days</td></tr><tr><td>Time period</td><td>1981/06-2022/12</td><td>1999/01-2020/06</td><td>2000/02-2022/12</td><td>1982/01-2020/12<br>(LAI4g)<br>1982/01-2022/12<br>(FAPAR4g)</td><td>1981/06-2018/12<br>(LAI)<br>1982/01-2018/12<br>(FAPAR)</td><td>1981/09-2020/06</td></tr><tr><td>LAI definition</td><td>True LAI</td><td>True LAI</td><td>True LAI</td><td>True LAI</td><td>True LAI</td><td>Effective LAI</td></tr><tr><td>Illumination<br>condition for<br>FAPAR</td><td>Black-sky at 10:30</td><td>Black-sky at 10:30</td><td>Black-sky at 10:30</td><td>Black-sky at 10:30</td><td>Blue-sky at 10:30</td><td>White-sky illumination</td></tr><tr><td>Retrieval<br>algorithm</td><td>Neural network daily<br>estimates and temporal<br>smoothing, gap filling<br>and compositing</td><td>Neural network daily<br>estimates and temporal<br>smoothing, gap filling<br>and compositing</td><td>SpatioTemporal<br>Information<br>Compositing<br>Algorithm (STICA)</td><td>Biome specific<br>neural networks &amp;<br>pixel-wise model</td><td>Biome specific<br>general regression<br>neural networks for<br>LAI. FAPAR is<br>retrieved from LAI.</td><td>Two-stream Inversion<br>Package (TIP)</td></tr><tr><td>Reference</td><td>Verger et al. (2024)</td><td>Verger et al. (2023)</td><td>Yan et al. (2024)</td><td>Cao et al. (2023);<br>Zhu et al. (2013)</td><td>Xiao et al. (2015);<br>Xiao et al. (2016)</td><td>Blessing and Giering<br>(2021); Pinty et al. (2006)</td></tr></tbody></table>

sky FAPAR at 10:30 a.m. local time.

The HiQ-MODIS LAI products are available at https://doi.org/10.5281/zenodo.8296768, and the HiQ-MODIS FAPAR at https://doi.org/10.5281/zenodo.10683549 (accessed September 2025).

### 2.3.3. GIMMS

The fourth generation GIMMS LAI4g (version 1.2) (Cao et al., 2023) and FAPAR4g (version 1.0) (Zhao et al., 2024) products have 15-day temporal frequency, 0.08° spatial resolution and span from January 1982 to December 2020 for LAI and to December 2022 for FAPAR.

The GIMMS LAI4g and FAPAR4g from 1982 to 2015 were first generated from the PKU GIMMS NDVI (Li et al., 2023) using biome specific neural networks trained using as input the NDVI data, the corresponding month of NDVI data, the latitude, longitude, as well as the NOAA satellite information, and the target variable was the fusion of Landsat and MODIS LAI for LAI4g (Cao et al., 2023) and the sensor independent climate data record of MODIS and VIIRS FAPAR (SI FAPAR CDR) (Pu et al., 2024) for FAPAR4g (Zhao et al., 2024). The GIMMS LAI4g and FAPAR4g (1982–2015) were then readjusted to the reprocessed MODIS LAI (Yuan et al., 2011) and the SI FAPAR CDR, respectively, using a pixel-wise regression model over the overlapping period 2004–2015. The GIMMS LAI4g (1982–2020) product comprises the consolidated LAI derived from AVHRR data (1982–2003) and the reprocessed MODIS LAI (2004–2020). The GIMMS FAPAR4g (1982–2022) product comprises the consolidated FAPAR derived from AVHRR data (1982–2015) and the SI FAPAR CDR (2016–2022).

The GIMMS LAI and FAPAR products correspond to the definition of the MODIS products used for their generation. It should be therefore the true LAI accounting for possible leaf clumping, and to the instantaneous black-sky FAPAR at 10:30 a.m. local time.

The GIMMS LAI products are available at https://doi.org/10.5281/zenodo.7649107, and the GIMMS FAPAR at https://doi.org/10.5281/zenodo.12517615 (accessed September 2025).

### 2.3.4. GLASS

The GLASS LAI and FAPAR (version 5) products have 8-day temporal resolution and 0.05° spatial resolution. The GLASS LAI time series span

from July 1981 to December 2018 while FAPAR products start in January 1982. They were derived from LTDR v4 AVHRR surface reflectance.

The GLASS LAI algorithm (Xiao et al., 2016) used biome specific general regression neural networks (GRNNs) trained with CYCLOPES version 3.1 (Baret et al., 2007) and MODIS Collection 5 (Myneni et al., 2002) LAI products over the BELMANIP1 (Weiss et al., 2014) sites for 2003 and 2004. MODIS and CYCLOPES products were fused based on their uncertainties. The CYCLOPES LAI defined as effective LAI was first transformed into true LAI using a biome specific clumping index map (Pisek et al., 2010) and the MODIS land cover (MOD12Q1, layer 3). GRNNs were trained for each MODIS biome type using the fused CYCLOPES and MODIS products and LTDR reflectance data pre-processed to avoid cloud contamination and fill any missing data using the time-series cloud detection algorithm (Tang et al., 2013). Unlike other algorithm using input data acquired over a restricted time period, the GRNNs ingest AVHRR LTDR reflectance data from an entire year to estimate the one-year LAI profile as a reanalysis.

The GLASS FAPAR product was derived from GLASS LAI to ensure consistency between LAI and FAPAR retrievals assuming that FAPAR is the complementary of the radiation transmitted down to the soil described by an exponential model with LAI and the clumping index (Xiao et al., 2015). The GLASS LAI product and the MODIS derived clumping index map (He et al., 2012) were used as input data to calculate GLASS FAPAR (Xiao et al., 2015).

The GLASS LAI values correspond to the true LAI. The GLASS FAPAR values correspond to the total (direct and diffuse) FAPAR (blue-sky) at 10:30 a.m. local time.

The GLASS LAI and FAPAR products are available at https://www.glass.hku.hk/download.html (accessed September 2025).

### 2.3.5. C3S

C3S LAI and FAPAR products were derived from C3S NOAA/AVHRR (September 1981 – December 2005), SPOT/VGT (January 2006 – December 2013) and PROBA-V (January 2014 – June 2020) data. They have 10-day temporal resolution and 1/30° (~4 km) spatial resolution for AVHRR products (1981–2005 period) and 1/112° (~1 km) for VGT
and PROBA-V products (2006–2020 period). The recent products in the VGT and PROBA-V period were here degraded to AVHRR resolution.

C3S LAI and FAPAR products were retrieved by applying the Two-stream Inversion Package (TIP) based on a 1D-canopy radiative transfer (Pinty et al., 2006). The model inversion is based on pre-computed look-up tables (Blessing and Giering, 2021) where the inputs are the top of canopy bi-hemispherical reflectance (BHR) in the visible and NIR domains. The BHRs are computed by fitting a BRDF model using a Kalman filter that accounts for the BRDF climatology as a function of season and location (Carrer et al., 2021). The algorithm was first calibrated for VGT-2 sensor. A spectral harmonization algorithm was then developed to get consistent input BHFs from PROBA-V, and AVHRRs sensors. The TIP algorithm assumes a random distribution of vegetation elements in the canopy and provides effective LAI values. C3S FAPAR corresponds to bi-hemispherical FAPAR (white-sky) under diffuse illumination conditions which is independent of the directions of incidence and viewing.

C3S products are available at https://cds.climate.copernicus.eu (accessed September 2025) (Copernicus Climate Change Service Climate Data Store, 2018).

## 2.4. Evaluation approach

The evaluation approach follows the guidelines defined for the validation of satellite-derived land products by the Land Product Validation (LPV) group of the Committee on Earth Observation Satellite (CEOS) (Fernandes et al., 2014) while proposing additional criterions. It includes:

### 2.4.1. Direct evaluation

A quantitative assessment of GEOV2-AVHRR estimates was achieved using the available ground-based data from DIRECT V2.1 (Camacho et al. (2024), https://calvalportal.ceos.org/lpv-direct-v2.1) and Ground-Based Observations for Validation (GBOV) V3 (https://gbov.land.copernicus.eu) datasets. DIRECT2.1 includes 172 sites (Fig. S7a) in the 1999–2021 period, and GBOV V3 provides continuous monitoring in 39 sites (Fig. S7b) in the period 2013–2022. These datasets provide ground-based maps of LAI and FAPAR derived from up-scaling in-situ ground measurements with high resolution imagery. Ground measurements were mostly acquired with hemispherical photography. For the upscaling, site specific empirical transfer functions were used in DIRECT V2.1 (Fernandes et al., 2014; Morissette et al., 2006) whilst generic physically based transfer functions for forest and non-forest classes based on radiative transfer models were used in GBOV V3 (GBOV-ATBD-LP3-LP4-LP5, 2021). Following GBOV recommendations, we used in our validation exercise only the maps where at least 50 % of the corresponding native resolution pixels were valid. The ground-based maps were then averaged over 3 km × 3 km areas. The satellite product, including GEOV2-AVHRR, were interpolated at the date of the ground-based data if two valid data exist within a maximum period of ±15 days.

The computed metrics to quantify the deviation of satellite products from ground-based data are the correlation coefficient (R²), and the Accuracy (A), Precision (P) and Uncertainty (U) (Claverie et al., 2013; Vermote and Kotchenova, 2008):

$$A = \frac{1}{N} \sum_{i=1}^{N} (y_i - x_i) \qquad (1)$$

$$P = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (y_i - x_i - A)^2} \qquad (2)$$

$$U = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (y_i - x_i)^2} \qquad (3)$$

where $y_i$ and $x_i$ are, respectively, the LAI/FAPAR estimate and the reference ground-based value for the sample i of N. The Accuracy, A, represents the mean bias of estimates, the Precision, P, the repeatability of the estimates and the Uncertainty, U (also called RMSE), the actual statistical deviation of the estimates from the truth including the accuracy and the precision errors. We also computed uncertainty agreement ratio (UAR) as the ratio of samples which meet GCOS (2011) requirements—max(20 %, 0.5) for LAI and max(10 %, 0.05) for FAPAR— in terms of absolute bias (Fernandes et al., 2023). We used GCOS (2011) uncertainty criteria rather than the more stringent GCOS (2022)—max (20 %, 0.1) for LAI and max(10 %, 0.005) for FAPAR— because the uncertainty of ground measurements in most of cases exceed these thresholds and, therefore, the conformity to the updated GCOS requirements is difficult to statistically test.

Validation good practice requires decomposing uncertainty conditional on reference value. For this purpose we modelled A, P and U as a function of LAI/FAPAR using third order polynomial weighted least regressions (Fernandes et al., 2023).

### 2.4.2. Indirect evaluation: comparison with other LAI/FAPAR satellite datasets

The spatio-temporal consistency of GEOV2-AVHRR with other satellite products was first assessed by mapping the mean product differences. We aggregated the satellite products at a spatial resolution of 0.5° if more than 70 % of pixels were valid. The 0.5° spatial resolution corresponds to the typical resolution of global models and reduces computation time. The overlapping period of the datasets was considered: 2000–2019 for the comparison with GEOV2-CLMS and HiQ-MODIS, and 1982–2018 for the comparison with AVHRR datasets. For comparison purposes, the datasets were linearly interpolated at the dates of GEOV2-AVHRR products at 10-day time step if at least two valid observations were available within ±30 days.

The correlation of GEOV2-AVHRR with the reference products and their differences as a function of LAI and FAPAR were further analyzed over the LANDVAL sites. LANDVAL includes 720 homogenous sites representative of the global distribution of the vegetation which were specifically selected for the validation of satellite products (https://calvalportal.ceos.org/sampling) (Fuster et al., 2020).

We also analyzed the distributions of LAI and FAPAR per biome type. Products were kept at their original spatial and temporal resolution. Eight biome types were considered, based on the Copernicus Global Land Cover 100 m (Buchhorn et al., 2020): Evergreen Broadleaf Forest, Deciduous Broadleaf Forest, Needleleaf Forest, Mixed Forest, Cultivated, Herbaceous, Shrublands, and Sparse and Bare Areas. The dominant biome over each product pixel was considered.

### 2.4.3. Assessment of temporal consistency and continuity

- Spatio-temporal continuity: the fraction and distribution in space and time of the missing data of satellite products mainly due to poor illumination conditions, cloud and snow cover is explored for the overlapping period 1982–2012.

- Intra-annual consistency: The temporal smoothness was used to quantify the intra-annual consistency. LAI and FAPAR variables result from incremental bio-physical processes. They are therefore expected to show relatively smooth temporal variations except in extreme situations such as flooding, fire or changes in the land-use. High variability in the temporal profiles would indicate a lack of reliability of the derived products. The smoothness of the temporal series was evaluated for each triplet i, i + 1, and i + 2 of consecutive estimates y as the absolute value of the difference between the center $y_{i+1}$ and the corresponding linear interpolation between the two extremes $y_i$ and $y_{i+2}$ within a maximum Δt period of 30 days between $t_i$ and $t_{i+2}$ corresponding to three consecutive GIMMS data (i.e. the analyzed product with the lowest frequency, Table 2) (Weiss et al., 2007):
$$ \delta = \left| y_{t+1} - y_t - \frac{y_t - y_{t+2}}{t_i - t_{i+2}} (t_i - t_{i+1}) \right| \qquad (4) $$

The smoother the temporal evolution, the smaller the $δ$ difference should be.

- Inter-annual consistency: We analyzed the latitudinal temporal patterns over the entire period of each AVHRR time series aggregated at 0.5° spatial resolution using Hovmöller diagrams (Hovmöller, 1949). It allows to identify some possible artifacts due to changes in sensors by displaying the standardized anomaly:

$$ Z = (y(t, year, lat) - \overline{y(t, lat)}) / \sigma(y(t, lat)) \qquad (5) $$

where $y(t, year, lat)$ is the estimated value of LAI/FAPAR at time $t$ in the year, for a given year and latitude $lat$, and $\overline{y(t, lat)}$ is the inter-annual average LAI/FAPAR at time $t$ in the year for latitude $lat$ computed over the years available in the time series (i.e. the climatological LAI/FAPAR value), and $\sigma(y(t, lat))$ refers to the inter-annual standard deviation of LAI/FAPAR values for that time and latitude.

- Stability: We evaluated the temporal stability, defined as the change of bias over time, using HiQ-MODIS time series as a reference. According to GCOS (2022), the threshold requirement to be meet in

terms of stability is 6 % m²m⁻²/decade for LAI and 3 %/decade for FAPAR.

- Temporal anomalies and trends: Finally, we computed the annual mean values and evaluated the temporal anomalies and trends at the global scale and per biome classes. The temporal trends were analyzed based on the non-parametric Mann Kendall trend test for the 1982–2018 overlapping period (de Jong et al., 2011). We focused on LAI products that are more sensitive to the vegetation amount than FAPAR, especially for the larger amounts.

## 3. Results

### 3.1. Direct evaluation

Confrontation with ground-based maps shows GEOV2-AVHRR LAI meets the GCOS (2011) requirements (UAR) in ~50 % of samples with accuracy error between -0.2 and 0.3 LAI and uncertainty <1 LAI for woody and non-woody sites of both DIRECT2.1 and GBOV V3 datasets (Fig. 1 and Table S3). For the FAPAR, GEOV2-AVHRR meets GCOS requirements (UAR) in ~30–40 % of samples with an accuracy error ~ -0.03 FAPAR and uncertainty of ~0.10–0.15 (Table S3). Note that

<figure><img src="image_3.png" /><figcaption>Fig. 1. Comparison of GEOV2-AVHRR products with ground-based measurements of LAI and FAPAR. Assessment over DIRECT2.1 sites in the 1999–2021 period (number of sites: 172 sites; number of samples: 279 for LAI and 128 for FAPAR), and GBOV V3 sites in the 2013–2022 period (number of sites: 39; number of samples: 6461 for LAI and 7045 for FAPAR). The different symbols correspond to the different landcover classes: stars for forest classes and circles for non-forest classes. The dotted line corresponds to the 1:1 line. The solid lines represent the GCOS (2011) accuracy criteria: max(20 %, 0.5) LAI and max(10 %, 0.05) FAPAR.</figcaption></figure>
similar (slightly degraded) performances were found for HiQ-MODIS, GIMMS and GLASS datasets (Figs. S8, S9, S10) but, in all cases, GEOV2-AVHRR shows the best accuracy, precision and uncertainty for LAI and FAPAR as evaluated both over DIRECT2.1 and GBOV V3 and it shows better internal consistency between LAI and FAPAR variables (Fig. S11).

GEOV2-AVHRR and HiQ-MODIS exhibit comparable APU statistics as a function of LAI and FAPAR (Fig. 2). GEOV2-AVHRR shows decreasing bias values from ~0.7 LAI at LAI ~ 0 to 0 for mid range LAI values (LAI ~ 3) and < -1 for LAI > 6. On the opposite, the precision error increases for the intermediate LAI values. The total uncertainty is ~0.5 for LAI < 4 and it increases with LAI levels to ~1 at LAI ~ 6.

GEOV2-AVHRR FAPAR accuracy error decreases from positive bias of ~0.2 at FAPAR~0.2, to 0 for mid-range FAPAR values (FAPAR~0.6) and ~ -0.1 for high FAPAR values (FAPAR>0.8). The precision error is low (<0.05) and it decreases with FAPAR value. This result in higher uncertainty errors for the low FAPAR values with U ~ 0.15 at FAPAR~0.2 to ~0.1 for FAPAR>0.6.

The observed positive bias of GEOV2-AVHRR as compared to GBOV V3 for low FAPAR values is partially due to a systematic underestimation of GBOV FAPAR over sparsely vegetated canopies because the automated processing of digital hemispherical photography applies a strict red-green vegetation index threshold which underestimates photosynthetically active vegetation (Camacho et al., 2025). Indeed, for low FAPAR values, all products show higher values than GBOV V3 (Fig. S9), which also shows inconsistencies in the LAI-FAPAR relationship (Fig. S11). Similarly, the substantial scattering observed in the LAI-FAPAR relationship for GBOV V3 ground-based measurements (Fig. S11) partially explains the precision error observed for the analyzed satellite products (Figs. S9-S10).

## 3.2. Consistency with GEOV2-CLMS and MODIS

GEOV2-AVHRR, aggregated at 0.5° spatial resolution, agree well with GEOV2-CLMS and HiQ-MODIS products over the common 2000–2019 period. GEOV2-AVHRR and GEOV2-CLMS differences are within GCOS (2011) requirements in 92 % and 71 % of land pixels for LAI and FAPAR, respectively, and GEOV2-AVHRR and HiQ-MODIS in 76 % and 34 % (63 % within ±0.1 FAPAR differences) (Table 3, Figs. S12-S13).

The scatterplots of the comparison at 5 km × 5 km (i.e. one GEOV2-AVHRR pixel, and 5 × 5 GEOV2-CLMS pixels) over the LANDVAL sites show overall uncertainty errors of 0.78 LAI and 0.08 FAPAR, with accuracy of −0.20 LAI and no bias (−0.001) for FAPAR, and a determination coefficient R² > 0.93 (Fig. 3). The comparison between GEOV2-AVHRR with HiQ-MODIS shows higher differences with an overall

<caption>**Table 3**</caption>

For each pair of products, percentage of pixels agreeing with GCOS requirement of max(20 %, 0.5) LAI (the upper triangle) and max(10 %, 0.05) FAPAR (the lower triangle). The color scale ranges from green (good agreement) to red (poor agreement).

<table><thead><tr><th>FAPAR</th><th>GEOV2-AVHRR</th><th>GEOV2-CLMS</th><th>HIQ-MODIS</th><th>GIMMS</th><th>GLASS</th><th>C3S</th></tr></thead><tbody><tr><td>GEOV2-AVHRR</td><td></td><td>92</td><td>76</td><td>81</td><td>84</td><td>59</td></tr><tr><td>GEOV2-CLMS</td><td>71</td><td></td><td>78</td><td>81</td><td>83</td><td>63</td></tr><tr><td>HIQ-MODIS</td><td>34</td><td>39</td><td></td><td>79</td><td>76</td><td>65</td></tr><tr><td>GIMMS</td><td>48</td><td>44</td><td>57</td><td></td><td>89</td><td>52</td></tr><tr><td>GLASS</td><td>57</td><td>59</td><td>46</td><td>44</td><td></td><td>57</td></tr><tr><td>C3S</td><td>35</td><td>34</td><td>34</td><td>25</td><td>38</td><td></td></tr></tbody></table>

uncertainty of 0.94 LAI and 0.12 FAPAR and more scattering (precision error of 0.86 LAI and 0.12 FAPAR, with R² ~ 0.88) due to the higher noise in MODIS products (cf. section 3.4.1).

The LAI differences between GEOV2-AVHRR and GEOV2-CLMS increase with the LAI value, resulting in higher A and U errors with LAI (Fig. 4). GEOV2-AVHRR LAI is lower than GEOV2-CLMS values for the highest LAI values corresponding to EBFs in tropical latitudes (cf. Figs. S5, S12a-b, 5a). Conversely, for FAPAR no bias is observed between GEOV2-AVHRR and GEOV2-CLMS for the entire range of FAPAR values and the U error is mostly driven by the P error (Fig. 4), which may be explained by the different sensors and preprocessing applied for the two products.

GEOV2-AVHRR systematically provides higher estimates than MODIS intermediate LAI values, resulting in higher APU errors for LAI values from 2 to 4 (Fig. 4). For the FAPAR, GEOV2-AVHRR and MODIS agree well for all the range of values resulting in relatively constant U and P metrics, except for the low FAPAR values where MODIS provides higher values which are translated in negative A metrics.

The differences of GEOV2-AVHRR with both GEOV2-CLMS and HiQ-MODIS are closely linked to the RMSE quality indicator associated to the GEOV2-AVHRR product (Figs. S14-S15): the higher RMSE values, the

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** LAI (left) and FAPAR (right) differences between GEOV2-AVHRR estimates and GBOV V3 ground-based maps. Lines represent the fitted accuracy, precision and uncertainty models for GEOV2-AVHRR (solid lines) and HiQ-MODIS (dashed lines).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Comparison between GEOV2-AVHRR and GEOV2-CLMS LAI and FAPAR products. Evaluation over the LANDVAL sites at 5 km × 5 km for the 1999–2019 period following a stratified random sampling by LAI/FAPAR (N = 20,000 samples).</figcaption></figure>

higher differences between estimates. This suggests that the RMSE quality layer appears as a reliable proxy of the GEOV2-AVHRR product uncertainty. The agreement between products degrades when the number of available observations in the composition period decreases (NOBS lower than ~5 observations) and the period length increases (period higher than ~80 days) (Figs. S14-S15).

### 3.3. Comparison with other LAI and FAPAR long-term products

The comparison of GEOV2-AVHRR with the long-term products at the global scale over the common overlapping period (1982–2018) shows good agreement with GIMMS and GLASS for both LAI and FAPAR (Table 3, Figs. S16-S17): ~80 % of land pixels are within LAI GCOS requirements, and 48 % and 57 % of pixels meet FAPAR requirements for the comparison with GIMMS and GLASS, respectively, which intercompare better with GEOV2-AVHRR than between them. C3S shows a poorer agreement with the three other products.

The distributions of GEOV2-AVHRR LAI values are consistent with the other LAI products (Fig. 5a) for the biomes with low vegetation amount including Herbaceous, Shrublands and Sparse and Bare Areas. However, when observing the FAPAR distributions (Fig. 5b), some differences appear: GEOV2-AVHRR agrees with GEOV2-CLMS and GLASS while GIMMS and HiQ-MODIS shows generally larger FAPAR values. This positive bias are associated to the quality of the original MODIS products used for generating both GIMMS (Zhao et al., 2024) and HiQ-MODIS (Yan et al., 2024) products. A similar offset for the low FAPAR values was observed in the validation of MODIS product (Martínez et al., 2013; McCallum et al., 2010). C3S FAPAR values are more evenly distributed when the other products show a peak around 0.1 to 0.2 FAPAR.

GEOV2-AVHRR agrees with GEOV2-CLMS, HiQ-MODIS, GIMMS and GLASS for both LAI and FAPAR products over Cultivated and Forested

biome types. Conversely, C3S shows much lower LAI values that almost never go over LAI = 3, which is not expected at least for most forested vegetation types in the lower latitudes, even for the effective LAI (cf. ground based values up to 7 in Fig. S8). Medium to large C3S FAPAR values are in better agreement with the other products except for the Needleleaf Forest showing lower FAPAR values. However, C3S has very high frequency of low FAPAR values which is not expected, particularly for the Needleleaf Forest with a peak at FAPAR = 0.

GEOV2-AVHRR and GLASS show very consistent distribution of LAI values over Evergreen Broadleaf Forests (EBFs) with a marked peak for LAI = 5 and almost no values larger than 6.0. GEOV2-CLMS shows similar distributions but with a shifted peak located at LAI = 6. HiQ-MODIS and GIMMS show more even distributions with a peak of frequency located close to LAI = 5.5 and LAI = 6, respectively, and exhibiting a notable number of values larger than LAI = 6. The observed discrepancies in EBFs are due, partly, to (i) the higher uncertainties in the satellite data: EBFs are located in tropical areas (Fig. S5) characterized by permanent cloud cover (Fig. S6e-f), (ii) intrinsic limitations: EBFs are characterized by high levels of LAI and, in these conditions, the models are close to saturation and small variations in the reflectance level may result in high variations in LAI values, and (iii) differences of the retrieval algorithms: differences in the training process, filtering, smoothing and gap filling. GEOV2-AVHRR, GEOV2-CLMS and GLASS show sharper distributions of LAI values mainly because of the more efficient filtering of cloud contamination and the algorithm assumptions forcing a very limited seasonality for EBFs. The broader distribution of HiQ-MODIS and GIMMS is partly due to the instabilities observed in these time series (section 3.4.1). C3S distribution of LAI products over EBFs shows a peak of frequency for LAI = 1 and again almost no values larger than LAI = 3 which is obviously not expected for EBFs. FAPAR distribution of GEOV2-AVHRR, GEOV2-CLMS, GIMMS and GLASS over EBFs are roughly similar, with a peak close to FAPAR = 0.9 and very
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** LAI (left) and FAPAR (right) differences of GEOV2-AVHRR with GEOV2-CLMS (top) and with HiQ-MODIS (bottom). Lines represent the fitted accuracy, precision and uncertainty models.</figcaption></figure>

little cases with FAPAR<0.6. HiQ-MODIS shows a similar peak located at ~0.9 FAPAR but a broader distribution of values, similarly as for LAI, due to a less efficient filtering of noise. Conversely, C3S shows a relatively flat distribution which, again, appears unrealistic for such a dense vegetation type.

### 3.4. Temporal consistency

#### 3.4.1. Spatio-temporal continuity: Completeness, and intra-annual consistency: Smoothness

During the 1982–2018 period, no or small fraction of missing data is observed for GLASS (0 %) and GEOV2-AVHRR (1 % of land pixels) (Fig. S18). Conversely, C3S (12 % of missing data mostly over northern mid-high latitudes and no data for the period from 1st January to 20th March 1985 and the period from 20th September 1994 to 10th February 1995), GIMMS (14 % for LAI4g and 24 % for FAPAR4g in winter for the northern high latitudes and no data for bare areas) and HiQ-MODIS (13 % of missing data with a spatial distribution similar to GIMMS LAI4g) show much larger fractions of missing data.

The histograms of $\delta$LAI and $\delta$FAPAR (Fig. 6) show that GEOV2-AVHRR, GEOV2-CLMS and GLASS products are very smooth with differences smaller than 0.1 LAI and 0.01 FAPAR in most cases. The $\delta$LAI and $\delta$FAPAR statistics for GEOV2-AVHRR and GEOV2-CLMS highly agree even if the two products are derived from different input data. This indicates that the intra-annual consistency of GEOV2 time series is mostly driven by the retrieval algorithm. On the opposite, GIMMS, C3S and HiQ-MODIS show higher frequencies of high $\delta$LAI and $\delta$FAPAR values indicating a more unstable temporal evolution (Figs. S19-S20).

The gap filling and temporal smoothing algorithms applied in GEOV2 and GLASS improve their spatio-temporal continuity (product completeness, Fig. S18) and consistency (Figs. 6, S19-S21).

GLASS uses reprocessed smoothed and gap filled AVHRR reflectance

data (Tang et al., 2013) and a rolling retrieval algorithm with input data for one-complete year period (Xiao et al., 2016) which allows to get a perfect continuity (0 % of missing data, Fig. S18) and the smoothest time series (Fig. 6). However, excessive smoothing lead to underestimation of disturbances. GLASS LAI shows apparent limitations to correctly reproduce the seasonality including the rapid changes in vegetation growth (Fig. S19) and perturbations (Fig. S20).

GEOV2 uses a Savitzky-Golay based filter (Verger et al., 2013) and the GEOCLIM climatology (Verger et al., 2015) to fill large periods of missing observations. This results in smooth time series with only 1 % of missing data at the very high northern latitudes (>70°N) where GEOCLIM climatology is not available (Fig. S18). GEOV2-AVHRR well reproduces the seasonality of LAI and FAPAR as compared to other satellite products and ground-based measurements (Fig. S19), including the abrupt changes due to perturbations such as wildfires (Fig. S20).

The temporal evolution of mean annual $\delta$LAI and $\delta$FAPAR values are very stable from year to year and over the entire period for GEOV2-AVHRR, GEOV2-CLMS, GLASS and HiQ-MODIS. On the contrary GIMMS LAI4g shows a transition to smoother time series (lower $\delta$LAI values) since the year 2005 when the product is derived from MODIS data (Table S1). GIMMS FAPAR4g shows a break in $\delta$FAPAR temporal evolution in December 2011 for the transition to NOAA-19 period as well as slightly lower values for the MODIS and VIIRS period since 2015 than for the AVHRR period (Fig. 6). More pronounced breaks are observed in the temporal evolution of C3S $\delta$LAI and $\delta$FAPAR with lower values since the year 2006 corresponding to the transition from AVHRR to VGT period.

#### 3.4.2. Inter-annual consistency: Robustness against sensor changes

The long time series of products are made of successive sensors (Table S1). It is therefore mandatory to verify the temporal consistency across sensors over the whole range of latitudes, as latitude is one of the
<figure><img src="image_7.png" /><figcaption>Fig. 5. Distribution of GEOV2-AVHRR, GIMMS, GLASS and C3S in the overlapping period 1982–2018, and GEOV2-CLMS and HiQ-MODIS in the period 2000–2018 for (a) LAI and (b) FAPAR products per biome type as sampled by the LANDVAL sites. Biome distinction is based on Global Land Cover 100 m (Buchhorn et al., 2020).</figcaption></figure>

main drivers of LAI and FAPAR variation (Figs. S12-S13, S16-S17). The Hovmöller diagrams of the LAI and FAPAR anomaly indices (Fig. 7) were analyzed with due attention to possible artifacts during the transition between two sensors.

No strong artifact is observed between N7 and N9 for all the products except C3S and to a lesser extent GIMMS for which a clear break in the magnitude of LAI and FAPAR is observed (Fig. 7d). This may be explained by the orbital drift in NOAA satellites towards later equatorial crossing time: higher SZA over the lifetime of each satellite and discontinuities in the SZA for the transition to a new satellite (Giglio and Roy, 2020; Pinzon and Tucker, 2014). Note that negative anomalies are observed at tropical latitudes for GLASS and GIMMS, and less for GEOV2-AVHRR. It corresponds to El Chichon (04/1982–12/1984) volcanic eruption due to the presence of stratospheric aerosols that were not fully corrected.

Between N9 and N11, possible problems are observed at the beginning of N11 period for both GEOV2-AVHRR and GLASS (Fig. 7a,c) due to

sub-optimal quality of the AVHRR reflectance in LTDR dataset used as inputs for both products. Tian et al. (2015) already observed inconsistencies at the beginning of N11 LTDR time series (11/1988–10/1989) due to orbital drift effects. This was successfully corrected in GIMMS NDVI time series and the derived LAI and FAPAR products show a smooth transition (Fig. 7b). C3S shows positive anomalies for the first half of the N11 period (Fig. 7d) that again may be attributed to orbital drift effects between N9 and N11. Note that negative anomalies are observed at tropical latitudes during Mount Pinatubo (06/1991–12/1993) volcanic eruption for all the products due to a substantial decrease in precipitation and associated drought conditions (Trenberth and Dai, 2007).

The transition between N11 and N14 (with a short use of N9, see Table S1) appears smooth for GIMMS LAI. However, slight (strong) positive anomalies for N14 period are observed for GEOV2-AVHRR, GLASS, (C3S) LAI/FAPAR and GIMMS FAPAR. These positive anomalies in all LAI and FAPAR products, except for GIMMS LAI, for N14 can
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Temporal evolution (left) and histogram distributions (right) of the δLAI (top) and δFAPAR (bottom) absolute difference representing temporal smoothness for GEOV2-AVHRR, GEOV2-CLMS, HiQ-MODIS, GIMMS, GLASS and C3S products. Evaluation over the LANDVAL sites for the 1982–2018 period.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Hovmöller diagram (Hovmöller, 1949) of the standardized anomalies for (a) GEOV2-AVHRR, (b) GIMMS, (c) GLASS and (d) C3S LAI (left) and FAPAR (right) products. Time is plotted along the abscissa and latitude along the ordinate. The black dashed lines indicate the date of change of sensors (Table S1).</figcaption></figure>

be explained by the inconsistencies in the magnitude of land surface reflectance from N11 to N14 (Frankenberg et al., 2021; Giglio and Roy, 2020). Santamaria-Artigas et al. (2021) reported a positive bias in the NIR surface reflectance from the transition of N11 to N14 whilst no bias was observed for the red band that was more stable.

After the N14 period, different schemes were used. GEOV2-AVHRR and GLASS use N16, N18 and N19 from the LTDR input reflectance, and GEOV2-AVHRR also uses MetOp-B LTDR since 2017 (Table S1). The

transitions appear relatively smooth for GLASS (Fig. 7c), with however a tendency of positive anomalies since 2000, which are specially pronounced in tropical and southern latitudes, possibly due to NOAA orbital drift and calibration issues in LTDR v4 data. Before 2000, the orbital drift effects were more pronounced (Frankenberg et al., 2021), particularly over tropical latitudes which suffered from larger variations in SZA (Tian et al., 2015). After 2000, AVHRR orbits and red and NIR LTDR surface reflectance records were more stable (Frankenberg et al., 2021).
The modifications of sensor design between AVHRR-2 and AVHRR-3 from single to dual-gain quantization and the differences in the spectral response functions (Fig. S1) complicate the inter-calibration and introduce inconsistencies before and after 2000 (Pinzon and Tucker, 2014). This artifact is less apparent in GEOV2-AVHRR (Fig. 7a) thanks to the reflectance harmonization process applied. GLASS FAPAR shows inconsistencies and higher FAPAR values at the end of N19 period after 2016 when orbital drifted to later crossing time: higher SZA. Note that GEOV2-AVHRR uses MetOp data in this period to avoid N19 orbital degradation effects. However, slight negative anomalies are observed during the N18 period for both LAI and FAPAR GEOV2-AVHRR and during the MetOp-B period for FAPAR in southern latitudes (Fig. 7a). GIMMS uses N16, N17, N18, N19 and MODIS-VIIRS for FAPAR with relatively smooth transitions for the AVHRR period but positive anomalies for MODIS-VIIRS period (Fig. 7b); and, N16 and MODIS for LAI which also shows pronounced positive anomalies for MODIS period. For C3S, the transitions between N14, N16 and N17 are relatively smooth, as well as in between VGT and PV (Fig. 7d). However, a clear break in the consistency of the time series is observed at the transition between N17 and VGT periods (Fig. 7d). For this reason, due to the clear artifacts and inconsistencies in their temporal evolution, C3S time series are discarded from our further evaluation.

The comparison with HiQ-MODIS in the period after 2000 (Fig. 8) reveals that GEOV2-AVHRR, GIMMS and GLASS meet GCOS stability criteria of 6 % per decade for LAI and 3 % per decade for FAPAR. In addition, GEOV2-AHRR improves GEOV2-CLMS in terms of stability even the later also meets the threshold stability criteria. Comparing stability of GEOV2-AVHRR ($S = -0.03m^2m^{-2}/decade$ for LAI and $S = -0.004/decade$ for FAPAR) and GEOV2-CLMS ($S = 0.06 m^2m^{-2}/decade$ for LAI and $S = 0.018/decade$ for FAPAR) allows quantification of dependence of stability on input data; comparing GEOV2-AVHRR and GLASS, which show identical stability performance: $S = -0.03m^2m^{-2}/decade$ for LAI and $S = -0.004/decade$ for FAPAR, allows quantifying dependence of stability on algorithm. Results indicate that stability of time series is mostly driven by the input surface reflectance, including sensors and preprocessing.

### 3.4.3. Temporal trends and anomalies

GEOV2-AVHRR, GIMMS and GLASS LAI products show a general greening trend for the 1982–2018 common period (Fig. 9) with significant ($p < 0.001$) linear changes of 0.0016, 0.0020 and 0.0026 $m^2 m^{-2} y^{-1}$, respectively, at the global scale (Fig. 10a): ~70–80 % of land pixels with greening vs ~20–30 % with browning. However, a limited fraction of the trends is statistically significant (the black dots in Fig. 9) and they are mostly positive: 45 % and 10 % of land pixels with significant ($p < 0.05$) positive and negative trends, respectively, for GEOV2-AVHRR, 50 % and 6 % for GIMMS, and 55 % and 6 % for GLASS.

The magnitude and spatial pattern of trends of GEOV2-AVHRR and GIMMS (Fig. 9) highly agree whilst large differences are observed with

GLASS LAI trends which shows smaller positive trends as compared to the other two products above 40° latitudes (red tones in the map of differences in LAI trends) and stronger positive trends below 40° latitudes (blue tones).

In the Northern hemisphere, the three products show a similar spatial pattern (Fig. 9) dominated by significant ($p < 0.001$) positive trends of 0.0017, 0.0022, 0.0024 $m^2 m^{-2} y^{-1}$ for GEOV2-AVHRR, GIMMS and GLASS, respectively (Fig. 10a). The main differences of GEOV2-AVHRR with the other two products are observed in East China where GEOV2-AVHRR shows moderate positive trends and some spots of negative trends while GLASS and GIMMS show strong positive trends (Fig. 9).

In the Southern Hemisphere, GEOV2-AVHRR trends highly agree with GIMMS except in central Africa where GEOV2-AVHRR shows stronger negative trends (cf. map of differences in Fig. 9). On the contrary, GLASS shows strong greening over the entire South hemisphere including areas where small or negative trends are observed in the other two products. This results in 0.00075 $m^2 m^{-2} y^{-1}$ ($p > 0.05$) southern trends for GEOV2-AVHRR as compared to 0.001 $m^2 m^{-2} y^{-1}$ ($p < 0.05$) for GIMMS and 0.0036 $m^2 m^{-2} y^{-1}$ ($p < 0.001$) for GLASS (Fig. 10a). The strong positive trends of GLASS LAI as compared to other products are due to the transition around the year 2000 from negative anomalies, particularly in the NOAA-9 and NOAA-11 period, to more stable positive anomalies (Fig. 10b) as reported in previous studies (Cao et al., 2023). The artifacts in the evolution of GLASS LAI anomalies are evident for the EBFs (Fig. 10b) which are expected to be stable without abrupt inter-annual changes. GEOV2-AVHRR, although derived from the same LTDR data, partially corrects these artifacts and it shows more uniform and stable temporal evolution of LAI anomalies over the entire period in the Southern Hemisphere and particularly over EBFs, with continuous positive trends which agrees with GIMMS and HiQ-MODIS.

## 4. Discussion

### 4.1. Consistency between LAI and FAPAR should be reflected in the GCOS requirements

The LAI and FAPAR products show a large degree of consistency (Fig. S11). This is expected since FAPAR could be computed from LAI values if the leaf inclination angle distribution and clumping are known (Xiao et al., 2015). GLASS FAPAR products and C3S used explicitly such relationship in their design while it is taken more implicitly into account in the radiative transfer model for MODIS and when training the neural networks with existing products for the case of GEOV2 and GIMMS (cf. sections 2.2–2.3). However, the GCOS requirements for FAPAR —max (10 %, 0.05)— appeared to be much more stringent as compared to LAI (Table 3, Table S3). If the GCOS (2011) LAI requirements —max(20 %, 0.5)— are kept as the reference, the FAPAR requirements should be defined accordingly, considering the intrinsic relationship between the

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Stability of GEOV2-AVHRR, GEOV2-CLMS, GIMMS, GLASS as evaluated as the change in annual bias with HiQ-MODIS for LAI (left) and FAPAR (right). The legend indicates slopes (S) of change in annual bias significatively different from zero at *p* < 0.01 (***), or at *p* < 0.05 (**).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Left: Spatial pattern of annual linear trends in GEOV2-AVHRR, GIMMS and GLASS LAI products for 1982–2018. Back dots highlight trends that are statistically significant (Mann–Kendall test; *p* < 0.05). Right: maps of difference in LAI trends of GEOV2-AVHRR with GIMMS and GLASS. The histograms indicate the percentage of land pixels for each interval of (differences in) LAI trends.</figcaption></figure>

two variables. Therefore, a threshold of 0.1 absolute difference value should be recommended rather than the 0.05 FAPAR value (GCOS, 2011). Contrarily, the recently updated GCOS (2022) uncertainty requirements are even more restrictive: max(20 %, 0.1) for LAI and max (10 %, 0.005) for FAPAR, and difficult to test because the uncertainty of ground measurements often exceed these absolute values.

## 4.2. Uncertainty assessment: The limit of the comparison to ground measurements

The direct validation of products is limited by the relatively small number of available validation sites and their representativeness. While DIRECT2.1 sites are distributed through Northern and Mid latitudes (Fig. S7), but where sampled only once or twice per year, generally close to the maximum peak of vegetation (Fuster et al., 2020; Garrigues et al., 2008), the 39 GBOV V3 sites, mostly located in USA, provide a very good temporal sampling of ground measurements throughout years and seasons, which is precious when considering satellite temporal series (Fig. S19). To provide a more exhaustive evaluation of the global products, we strongly encourage that future validation activities consider at least several acquisitions across the season and better account for the representativeness of the biome types, seasonality and

latitude (e. g environmental conditions reflected by climate and soil type) at the Earth's surface. This should be supported by initiatives like what is done by GBOV (Lerebourg et al., 2024) together with the NEON network (Metzger et al., 2019) or by NUBICOS (with the ICOS network, icos-cp.eu/projects/nubicos) who take benefit of existing ground measurement networks and adapt their acquisition protocols for satellite validation.

In this study, we chose to use the original reference databases (DIRECT2.1 and GBOV V3) recommended by the CEOS Land Product Validation subgroup to allow comparison with other validation studies of the product we considered. We just discarded the DIRECT2.1 ground measurement sites that were not including understory measurements as it is obvious that understory impacts the satellite signal. However, we did not consider applying other corrections suggested in the literature like applying nominal correcting factor values for the clumping (Fernandes et al., 2023) to correct from effective LAI values or for wood proportion to account for non-green elements (Brown et al., 2021a). Although interesting, the use of nominal values may lead to include some bias (e.g. these values are assigned depending on only few biome types) in the results not easy to detect and to interpret since both clumping factor and woody proportion are very variable depending on species, phenological stages, even for a specific land cover, and
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** (a) Annual LAI trends of GEOV2-AVHRR, GIMMS and GLASS over the 1982–2018 period per biome and per hemisphere. EBF: Evergreen Broadleaf Forest. DBF: Deciduous Broadleaf Forest. NF: Needleleaf Forest. CG: Crops and Grasslands. SSB: Shrublands, Savannas and Bare Areas. NH: North Hemisphere. SH: South Hemisphere. GLO: Global. Significant trends at *p* < 0.1 (*), *p* < 0.05 (**) or *p* < 0.01 (***). (b) Temporal evolution of annual LAI anomalies of GEOV2-AVHRR, GIMMS, GLASS and HiQ-MODIS.</figcaption></figure>

especially those affected by anthropic activities (Fang, 2021). Indeed, requirements and protocols should be recommended by the CEOS LPV group to associate clumping factor and woody proportion estimates with ground LAI measurements maps. While this can be easy for woody proportion of deciduous forest by making an acquisition in winter as suggested by Brown et al. (2021b), assessing the clumping factor is less simple and still a research topic (Fang et al., 2019).

We did no explicitly consider the uncertainties associated to the reference maps as it is difficult to assess them from the already existing datasets. They were roughly estimated in the literature around 1.0 LAI units for forest (Fernandes et al., 2003), 0.5 LAI for croplands (Martínez et al., 2009) and up to 0.1 for FAPAR (Gobron et al., 2008). However, these uncertainties are very variable and caused by multiple sources that depend on (i) the instrument used to measure the variable and its own definition (e.g. whether the woody elements, the senescent elements, organs other than leaves, or the clumping were taken into account or not), (ii) the method used to derive it from the actual quantity that is measured by the instrument (e.g transmittance in the blue domain,

intercepted PAR, gap fraction), (iii) the sampling protocol (spatial distribution, vertical distribution, understory contribution), and (iv) the associated upscaling method. Brown et al. (2020) proposed a basis framework to provide an end-to-end uncertainty evaluation, accounting for the last three sources of uncertainty. This quality assurance framework should be further developed within the different validation networks.

Finally, we strongly suggest that the CEOS land product validation subgroup keeps coordinating the satellite validation activities by (i) providing regular updates of the good practices for satellite validation issued from a consensus from the community, as was done by Fernandes et al. (2014). This should concern: devices, ground measurements protocols, processing, upscaling and methods for product inter-comparison. And (ii) encouraging measurement networks to adopt these practices.
### 4.3. Inconsistencies in the magnitude of LAI and FAPAR values: The importance of product definition and retrieval algorithms

The deviation of GEOV2-AVHRR LAI and FAPAR with other datasets can be partly explained by the differences in product definition as well as in the retrieval algorithms.

#### 4.3.1. Product definition

For FAPAR, the definitions are consistent between GEOV2, GIMMS and HiQ-MODIS, corresponding to the back-sky FAPAR under direct illumination at 10:00/10:30 a.m. local time. C3S corresponds to the white-sky FAPAR under diffuse radiation that is thus independent of illumination conditions, and should be systematically higher than the direct FAPAR except for SZA > 60° and LAI < 4 when the direct FAPAR increases with the penetration path length (Li and Fang, 2015). GLASS corresponds to the total (direct and diffuse) FAPAR (blue-sky) at 10:30, and should be higher than the direct FAPAR and the diffuse FAPAR. Note however that the differences in the definition of FAPAR do not fully explain the observed differences between GEOV2, GIMMS, GLASS and C3S products that are also affected by the algorithm differences and input data, nor their differences with ground data (corresponding to direct FAPAR). The differences in FAPAR definition may have a large impact for primary productivity estimation, particularly for the higher latitudes where differences due to the seasonal change of the daily sun course are very large. Ideally, the definition of the FAPAR products should be consistent with the incoming PAR products that are now available at daily temporal resolution and already provide instantaneous values at the satellite overpass time (Wang et al., 2020a; Zhang et al., 2023).

For LAI products, the difference between true and effective LAI product definitions may introduce discrepancies up to 100 % in LAI values mostly for forest biomes since the clumping index roughly varies between 0.5 (very clumped canopies) to 1.0 (randomly distributed leaves) (Pisek et al., 2010). The comparison with the ground data indicates that GEOV2, GIMMS and GLASS are closer to the true LAI definition, whereas C3S is closer to effective LAI (Fig. S8). However, because the ground data available with concurrent true and effective LAI are not large enough (n = 41 samples), this conclusion should be mitigated. Further, great care should also be brought on the way ground data have been collected and processed to derive true or effective LAI values. Future work should be carried out to clarify this very important aspect.

#### 4.3.2. Retrieval algorithms

The differences between GEOV2-AVHRR and GEOV2-CLMS in terms of the magnitude of LAI (Figs. 3, 5a, S12 a-b) for EBF pixels (Fig. S5) may be explained by the differences in the retrieval algorithms. Indeed, EBFs are characterized by high levels of LAI, for which the reflectance signal is close to saturation, and small variations in the reflectance level may induce significant variations in LAI values. Additionally, the difference in neural nets training may also induce some uncertainties since GEOV2-CLMS (Verger et al., 2023) uses dedicated neural networks for processing EBF or non EBF pixels while generic neural networks are used in GEOV2-AVHRR without distinction between biome types. Finally, AVHRR spectral bands are broader than that of VGT sensors (Fig. S1), and therefore, GEOV2-AVHRR products are more affected by residual clouds and atmospheric effects which may introduce a negative bias in LAI estimate (Cihlar, 1996).

Conversely, for FAPAR no bias is observed between GEOV2-AVHRR and GEOV2-CLMS including for the EBF pixels (Fig. 3). In this sense, FAPAR values are less sensitive to the noise in the data and to differences in the retrieval algorithms, and will not impact gross primary production estimates.

### 4.4. Intra-annual consistency and product completeness: The importance of post-processing time series of products

GEOV2 and GLASS show much higher spatial and temporal continuity (Fig. S18) as well as higher and more stable intra-annual consistency (Figs. 6, S19-S20) as compared to MODIS, GIMMS and C3S. The pre-processing of the inputs of the retrieval algorithms appears not sufficient to fill large gaps of observations and filter unexpected values. The addition of an efficient post-processing of the daily individual product estimates as done for GEOV2 and GLASS allows to fill gaps due to cloud or snow occurrence, while smoothing the non-expected high frequency variability of LAI or FAPAR products. This results in more continuous and temporally consistent time-series that are mandatory for the monitoring of vegetation phenology. In this sense, the smoothed GEOV2 products were demonstrated to provide accurate phenology metrics as compared to visual phenophases (Bórquez et al., 2020a; Verger et al., 2016), PhenoCam and Eddy covariance data (Bórquez et al., 2020b). Using continuous LAI net field measurements over maize crops, Yu et al. (2021) showed that GEOV2 correctly reproduced the magnitude and seasonality of LAI, while C3S systematically underestimated high LAI values and provided biased estimates of phenology metrics, and the over-smoothing in GLASS introduced artifacts in terms of phenology. Additional confrontation with continuous ground measurements are necessary to further assess the seasonality of the satellite time series.

### 4.5. Inter-annual consistency: The importance of pre-processing. A high degree of temporal consistency between the series of sensors is mandatory

The inter-annual consistency of LAI and FAPAR datasets appears to be very sensitive to the pre-processing and the input data ingested in the retrieval algorithm. For the recent period, GEOV2-AVHRR meets GCOS stability criteria as evaluated with MODIS LAI/FAPAR and improves the stability of GEOV2-CLMS products even the later also meets GCOS criteria (Fig. 8). However, our results indicate that the long-term temporal consistency of the four AVHRR datasets is a difficult question (Fig. 7). The changes between sensors appear to induce artifacts that can be relatively important as in the case of C3S for which most transitions between sensors are clearly visible (Fig. 7). However, the other products show also some shifts in the time-series when the sensors are changed.

The LAI and FAPAR products derived from AVHRR data present some artifacts associated to data inconsistencies from orbital drift, calibration issues, spectral differences, BRDF normalization, or atmospheric correction (Santamaria-Artigas et al., 2021). In this sense, the improvements of temporal consistency of GIMMS LAI4g and FAPAR4g for the AVHRR era (Fig. 7) may be explained by the use of PUK GIMMS NDVI as the input data since this dataset accounts for various deleterious effects in AVHRR sensors including calibration loss and orbital drift (Li et al., 2023). Note however that GIMMS LAI4g and FAPAR4g still show some inconsistencies for the transition to MODIS period (Fig. 7).

The AVHRR LTDR, from which GEOV2-AVHRR (and GLASS) products were derived, constitutes a unique dataset for the retrieval of long-term LAI and FAPAR products because it provides surface reflectance data at daily step. Enhancements of LTDR dataset include geolocation correction, calibration, cloud screening and atmospheric correction, and BRDF normalization (Franch et al., 2017). However, recent studies showed that the LTDR dataset is still affected by calibration issues between AVHRR sensors and NOAA orbital drift which results in temporal discontinuities in the transition between successive AVHRR sensors in the red and NIR surface reflectances (Frankenberg et al., 2021; Giglio and Roy, 2020) and, subsequently, in the retrieved LAI and FAPAR products (Fig. 7). Santamaria-Artigas et al. (2021) and Frankenberg et al. (2021) reported positive bias in NIR reflectance for NOAA-14. Giglio and Roy, 2020 reported progressive changes in the surface reflectance values with the increase of SZA over the lifetime of each NOAA satellite but abrupt changes between satellites, particularly for the NIR reflectance, due to the discontinuities in SZA (up to 43° for
tropical latitudes with the transition from NOAA-11 to NOAA-14).

The interannual consistency is obviously critical when exploiting long-time series to quantify trends and anomalies in vegetation. Our results indicate therefore that studies reporting long-time trends from the current products should be taken with great caution. The annual LAI anomalies and trends from GEOV2-AVHRR highly agree with those from GIMMS LAI4g and MODIS, and partially corrects some of the artifacts detected for GLASS products (also derived from the same LTDR v4), particularly in tropical latitudes and in the southern hemisphere. The preprocessing (harmonization of AVHRR sensors) and postprocessing (temporal techniques in the retrieval algorithm) in GEOV2-AVHRR accounts for the spectral response differences among sensors through radiometric correction and partially addresses anomalies caused by cloud contamination. However, GEOV2-AVHRR still shows some temporal inconsistencies inherited from the artifacts related to the transition between AVHRR sensors in the input surface reflectance data. The transition from AVHRR-2 to AVHRR-3 in November 2000 in LTDR dataset resulted in a higher number of available observations (Fig. S22) which partially impacted the LAI and FAPAR retrievals mainly for GLASS and, in a lesser extent for GEOV2-AVHRR (Figs. 7, 10). The differences in the number of available observations for compositing GEOV2-AVHRR products may also partially explain the anomalies observed for the transition from NOAA to MetOp period in January 2017 (cf. Figs. S22 and 7).

GEOV2-AVHRR products started to be produced several years ago and, due to operational constraints, were updated on an annual basis using the last available LTDR version at the time of processing: LTDR v4 for NOAA period and LTDR v5 for MetOp period. We recognize that mixing two different LTDR versions and platforms is not optimal even if it has marginal effects on the GEOV2-AVHRR LAI and FAPAR estimates (no bias and correlation $R^2$ > 0.96, Fig. S23). A reprocessing of GEOV2-AVHRR time series with the latest version of LTDR product or novel corrected surface reflectance dataset with enhanced temporal consistency is envisaged.

Addressing the remaining inconsistencies in the input AVHRR surface reflectance data appears mandatory to derive more accurate and reliable long-term LAI/FAPAR products. A rigorous radiometric calibration of the sensors is obviously mandatory, but not sufficient. The impact of the increase of the sun zenith angle with the orbital drift on the directionally normalized surface reflectance should be analyzed more in detail due to the known limitations of the kernel-driven BRDF models at extreme sun angles (Weiss et al., 2002). Using directly, as the input data of GEOV2 algorithm, the daily surface reflectance data and the associated sun-view angles was also demonstrated an efficient alternative to the use of directionally normalized surface reflectance to overcome these limitations (Verger et al., 2023). Further, some benchmark pseudo-invariant sites from which a great stability is reported from ground observations or from independent satellite observations should be selected to ensure that the transition between sensors is consistent (Giglio and Roy, 2020) (Fig. S21). The harmonization of surface reflectance with recent and stable datasets such as MODIS also allows to enhance the temporal consistency of AVHRR time series (Jeong et al., 2024).

The generation of improved long-term datasets, such as GEOV2-AVHRR, will contribute to elucidate whether global vegetation is continuously greening, declining its activity or even browning in recent years, as well as in the attribution of changes, and to have a better understanding of the interaction of vegetation with climate system and carbon cycle.

The users are advised to pay attention to the quality indicators associated to the products. The GEOV2-AVHRR approach used to process pixels flagged as EBF (Table 1, Fig. S5) assumes a low seasonality and only reproduces the high values of the time series which constitutes an oversimplification of the reality because of the possible seasonality of EBFs. The high uncertainty associated with the data due to poor atmospheric correction and very high cloud occurrence in EBF areas

prevented the extraction of meaningful phenology, anomalies and trends which partially explains the discrepancies between products (Fig. 10).

## 4.6. Qualifications of the four LAI/FAPAR long term datasets

The selection of the most appropriate product depends on the targeted application. Because of the relatively close relationship between LAI and FAPAR, we mainly focused on the LAI products since LAI offers the advantage of being more sensitive to medium to large vegetation amounts. We selected five criterions that were analyzed previously to qualify the four long-term products (Table 4): (1) the direct evaluation on FAPAR and (2) LAI, (3) the spatio-temporal continuity, (4) the intra-annual consistency: the realism of the seasonality and the smoothness of the temporal profiles, and (5) the inter-annual consistency: the robustness against sensor changes and the stability as compared to MODIS.

Following our evaluation study, GEOV2-AVHRR provides the best or second-best qualifications across all criterions (Table 4). GEOV2-AVHRR agrees the best with ground measurements both for LAI and FAPAR, and improves other products in terms of intra-annual consistency. GLASS performs better than GEOV2-AVHRR in terms of product completeness even if the later shows only 1 % of missing data. GIMMS is slightly more robust against sensor changes even if it also shows some inconsistencies in the transition from AVHRR to MODIS. GIMMS and GEOV2-AVHRR would be the preferred products to be used for trends and anomaly detection. The smoothness and the realism of the seasonality of GEOV2-AVHRR time series may be exploited for phenology description (Bórquez et al., 2020a; Bórquez et al., 2020b). Conversely, GIMMS appears mainly limited by its relatively poor spatio-temporal continuity and the high frequency noise observed over the temporal profiles. This may be smoothed out by degrading the spatial resolution or applying temporal filters through postprocessing. GLASS is limited both to track abrupt changes and to detect trends due respectively to the over-smoothing in the time series and the inconsistencies related to the transition between sensors. C3S shows a poor ranking for all the considered criterions.

## 5. Conclusions

Building long-term time series from several sensors is a difficult task when high temporal consistency is mandatory to document subtle changes in vegetation phenology and magnitude. The GEOV2-AVHRR LAI and FAPAR 1981–2022 global time series capitalize on the improved pre-processing of AVHRR historical reflectance data by the Long Term Data Record (LTDR) dataset, and the recent development of improved processing of biophysical variables with the Copernicus Global Land Service (CLMS) LAI and FAPAR 1 km V2 (GEOV2-CLMS) 1999–2020 time series. GEOV2-AVHRR aimed to extend these time series and ensure long-term continuity, intra-consistency over time, inter-consistency with GEOV2-CLMS products, and accuracy.

The comparison between GEOV2-AVHRR and GEOV2-CLMS demonstrate that these objectives were achieved: >90 % of land pixels are within ±max(20 %, 0.5) LAI and > 70 % within ±max(10 %, 0.05) FAPAR GCOS uncertainty requirements. We also compared GEOV2-AVHRR with HiQ-MODIS and other long-term global LAI and FAPAR datasets derived from AVHRR data: GIMMS, GLASS and C3S. GEOV2-AVHRR agrees within GCOS requirements with HiQ-MODIS, GIMMS and GLASS in ~80 % of land pixels for LAI, and 34 %, 48 % and 57 % of pixels, respectively, for FAPAR. GEOV2-AVHRR shows very consistent distribution per biome classes which agree with other datasets, with however some discrepancies mainly for the extreme high LAI values and low FAPAR values corresponding, respectively, to EBF and sparse vegetation where GEOV2-AVHRR and GLASS provide lower values than GIMMS and GEOV2-CLMS for LAI, and GIMMS and HiQ-MODIS for FAPAR.

GEOV2-AVHRR outperforms other products in terms of uncertainty as evaluated over the DIRECT V2.1 and GBOV V3 ground reference data:
<caption>Table 4</caption>

Qualifications of the four long-term products against criteria usually sought in different applications. Results related to direct evaluation are limited by the representativeness of the validation dataset. The color scale ranges from green (good agreement) to red (poor agreement).

<table><thead><tr><th></th><th>GEOV2</th><th>GIMMS</th><th>GLASS</th><th>C3S</th></tr></thead><tbody><tr><td>Direct Evaluation (FAPAR)</td><td>+++</td><td>++</td><td>++</td><td>-</td></tr><tr><td>Direct Evaluation (LAI)</td><td>+++</td><td>++</td><td>++</td><td>--</td></tr><tr><td>Spatio-Temporal Continuity (LAI)</td><td>++</td><td>-</td><td>+++</td><td>-</td></tr><tr><td>Seasonality & Smoothness (LAI)</td><td>+++</td><td>-</td><td>+</td><td>-</td></tr><tr><td>Stability and Robustness against sensor changes (LAI)</td><td>-</td><td>+</td><td>--</td><td>---</td></tr></tbody></table>

uncertainty <1 LAI and ~ 0.10–0.15 FAPAR for non-woody and woody sites of both DIRECT2.1 and GBOV V3 datasets.

In terms of product completeness, GEOV2 (1 % of land pixels missing) and GLASS (no fraction of missing data 0 %) outperform C3S (12 % of missing data), HiQ-MODIS (13 %) and GIMMS (14 % for LAI4g and 24 % for FAPAR4g). The application of post-processing and temporal filters in GEOV2 and GLASS, along with gap filling gaps enhance spatio-temporal continuity and proves effective in improving the temporal precision: GEOV2 and GLASS show a smoother temporal evolution than HiQ-MODIS, GIMMS and C3S that show more date to date variation and noise due to uncorrected aerosol and residual clouds. GEOV2, GLASS and HiQ-MODIS shows stable intra-annual consistency values whilst GIMMS and C3S shows a shift in their temporal evolution with smoother products for the transition from AVHRR to more recent sensors. The temporal smoothness of GEOV2-AVHRR and GEOV2-CLMS highly agree even the input data differs indicating that the intra-annual consistency of GEOV2 is mostly driven by the algorithm and postprocessing.

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 meets GCOS stability criteria as evaluated with MODIS LAI/FAPAR and improves the stability of GEOV2-CLMS products. Some residual inter-annual inconsistencies from the transition to sensors are observed for GEOV2-AVHRR as well as for all the other long term AVHRR datasets. Further investigations are necessary to fully correct orbital drift and sensor degradation issues which is critical for the study of vegetation anomalies and trends. 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. Further confrontations and evaluation of long-term trends with known variations in climate drivers will be explored in forthcoming studies.

The GEOV2-AVHRR products are in open access at 0.05° and 0.5° spatial resolution and 10-day frequency from July 1981 to December 2022 (https://geodes.cnes.fr). They are distributed in multi-band hdfs format including the associated quality indicators. Since AVHRR sensors are still in orbit, GEOV2-AVHRR 1981–2022 time series can be extended and updated on an annual basis. A full reprocessing of the GEOV2-AVHRR time series using novel long-term surface reflectance data with enhanced temporal consistency is also planned. These consistent GEOV2-AVHRR long-term LAI and FAPAR data records will contribute to a better understanding of vegetation dynamics and their interactions with climate system and carbon cycle.

### CRediT authorship contribution statement

**Aleixandre Verger:** Writing – original draft, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Conceptualization.
**Marie Weiss:** Writing – review & editing, Conceptualization.
**Frédéric Baret:** Writing – review & editing, Supervision, Methodology, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

M. Weiss serves as editor of RSE If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

GEOV2-AVHRR products were produced and distributed by CNES based on the algorithm developed by CSIC, CREAF and INRAE in the framework of the THEIA Land Data Centre. This research has received funding from LONGLOVE (140570/00-CNES) and VALOLAI (5700007172-CNES) projects, the Copernicus Global Land Service (CGLOPS-1, 199494-JRC), the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement n°218795 (geoland2 project), and the Spanish Ministry of Science, Innovation and Universities grant PROFECIA (PID2023-1521140B-100). The LTDR surface reflectance products were processed and distributed by the MEaSURES Long-Term Data Record project by the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) at the Goddard Space Flight Center. We acknowledge the Copernicus Climate Change Service (C3S), GIMMS and GLASS programs for the LAI and FAPAR satellite products; and, the FP7 ImagineS project (Grant Agreement N_311766) and the Copernicus Ground-based Observations for Validation (GBOV) service for the ground datasets used for the accuracy assessment. We thank our colleagues from EOLAB who contributed to the gathering of the ground datasets. This study contributes to TELEDETECT and ALIANZA OBSERVA initiatives.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2025.115029.

### Data availability

Data will be made available on request.

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