The combination of Sentinel-2 multispectral instrument (MSI) and Landsat 8 operational land imager (OLI) creates a virtual constellation of decametric sensors with high revisiting frequency. However, the differences in the spectral characteristics of the two sensors cause inconsistencies in downstream applications. This study proposed a multiband constraint spectral harmonization method called HARMU. In comparison to existing methods, HARMU uses all the spectral bands in the source sensor to predict the reflectance of the targeting sensor and so fully exploits spectral linkage among different bands. HARMU was specifically implemented by Gaussian process regression (GPR), with training data collected from the spatiotemporally representative BEnchmark Land Multisite ANalysis and Intercomparison of Products 2.1 (BELMANIP2.1) sites. We reproduced the top of the canopy reflectance at both common bands of OLI and MSI and also reflectance at red-edge (RE) bands that are only equipped on MSI. The results indicated that HARMU performed satisfactorily with$R^{2}$larger than 0.91 and Rel-Bias less than 0.19 for all bands over BELMANIP2.1 sites. HARMU offered similar performances as the widely used Harmonized Landsat and Sentinel-2 (HLS) products: average$R^{2}$slightly improved from 0.86 for HLS to 0.88 for HARMU for the common bands as evaluated over ground-based observations for validation (GBOV) sites, and additionally, it well reconstructs the missing RE band in HLS-based OLI ($R^{2} \gt 0.81$and Rel-Bias <0.15). HARMU will substantially contribute to generating spatiotemporally continuous time series of decametric data from the MSI-OLI virtual constellation and monitoring vegetation dynamics in large-scale and long-time sequences.

High spatiotemporal resolution time series of leaf area index (LAI) are essential for monitoring crop dynamics and validating coarse-resolution LAI products. The optical satellite sensors at decametric-resolution have historically suffered from a long revisit cycle and cloud contamination issues that hampered the acquisition of frequent and high-quality observations. The 16-m/4-day resolution of the new generation Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites provide an unprecedented opportunity to address these limitations. Here we developed an effective strategy to generate daily 16-m LAI maps combing GF-1/6 data and ground LAINet measurements. All high-quality GF-1/6 observations were utilized first to derive smoothed time series of vegetation indices (VIs). Second, a random forest regression (RF-r) model was trained to link the VIs with corresponding field LAI measurements. The trained RF-r was finally employed to generate the LAI maps. Results demonstrated the reliability of the reconstructed daily VIs (relative error2of 0.05, 0.59 and 0.75, respectively. The LAI time series well captured the spatiotemporal variation of crop growth. Furthermore, the continuous GF-1/6 LAI maps outperformed Sentinel-2 LAI estimates both in terms of temporal frequency and accuracy. Our study indicates the potential of GF-1/6 to generate continuous decametric-resolution LAI maps for fine-scale agricultural monitoring.

Accurate estimation of photosynthetic phenology is of great importance for understanding carbon cycles. Most vegetation indices (VIs) calculated from remotely sensed reflectances represent the canopy structure and have high uncertainty in detecting the photosynthetic phenology. We compared the start/end of the photosynthetically active season (SOS/EOS) extracted from the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of vegetation (NIRv) and the product of NIRv and solar incident radiation (NIRvP) over northern deciduous broadleaf forests, and we used the metrics generated from solar-induced chlorophyll fluorescence (SIF), a proxy for photosynthesis, as reference. We found that the growing season extracted from the structural VIs was generally longer than the duration of photosynthetic activity retrieved from SIF: SOS derived from NDVI < NIRvP < EVI ≈ NIRv ≈ SIF and EOS from NDVI > NIRv ≈ EVI > NIRvP ≈ SIF. We investigated the mechanism underlying these phenological discrepancies using the paradigm of light-use efficiency. Our results show that the divergent performances of VIs were related to main factors limiting photosynthesis, which vary across different growth stages. The fraction of absorbed photosynthetically active radiation absorbed by chlorophyll (FAPARchl) that is well characterized by both EVI and NIRv, was the dominant factor of spring photosynthetic phenology, whilst NIRvP that is a proxy of the total amount of photosynthetically active radiation absorbed by chlorophyll (APARchl) was the dominant factor in autumn when radiation determines photosynthetic phenology. As such, we suggest that these factors be accounted for when selecting VIs for the extraction of photosynthetic phenology, i.e., EVI and NIRv are more suitable for accurate retrieval of SOS, and NIRvP is more suitable for accurate retrieval of EOS.

The near-infrared reflectance of vegetation (NIRv) has been increasingly used as a proxy of gross primary production (GPP) across various temporal scales, ecosystems, and climate conditions. However, topography significantly distorts NIRv and GPP estimations over mountainous areas. We evaluated the topographic effects on NIRv and applied a path length correction (PLC) for improving its performance over mountainous areas. The proposed topographically corrected NIRv (referred to TCNIRv) was evaluated by multiple Landsat-8 operational land imager (OLI) images with concurrent${ in}~{ situ}$GPP measurements over the Lägeren mountainous forest area. TCNIRv reduced topographic effects in the original NIRv and it was comparable to the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI), which are often deemed to be independent of topographic effects. In addition, TCNIRv better agreed with GPP than the other vegetation indices (VIs): coefficient of determination$R^{2} $= 0.90 and root mean square error RMSE = 1.40$\text{g}\cdot $Cm$^{-2} \cdot \text{d}$−1for TCNIRv compared to$R^{2} $= 0.71 and RMSE = 2.47$\text{g}\cdot $Cm−2$\cdot \text{d}$−1for NIRv. The evaluation shows that TCNIRv is a reliable proxy of GPP, and because of its simplicity and physical soundness, it will facilitate vegetation monitoring over complex topography mountainous areas.

Topographic and angular corrections on Sentinel-2 imagery are crucial for the generation of consistent surface reflectance. We propose a novel topographic-angular integrated normalization approach based on the combination of the path length correction (PLC) and C-factor approaches. The PLC-C normalization approach is a semiphysical method with limited use of auxiliary data: only a digital elevation model and a fixed set of kernel coefficients, ensuring its transferability for operational implementation. For the validation, we used two Sentinel-2A images over a mountainous area observed in backward (BS) and forward scattering (FS) directions from laterally adjacent orbit swaths. PLC-C significantly reduced both the topographic and directional anisotropy effects: the overlapping ratio between BS and FS observations was increased from 84.1% to 92.8% for the near-infrared band, and from 81.0% to 93.1% for the red band; the coefficient of variation of the reflectances across different aspects, which was used as a criterion of topographic effects, was reduced from 9.8%/12.2% to 3.6%/5.7% in BS/FS direction for the near-infrared band, and from 8.1%/9.7% to 4.5%/4.2% for the red band. PLC-C will contribute to the generation of analysis ready data from Sentinel-2 top of canopy reflectance.

Near real-time (NRT) estimation of leaf area index (LAI) is essential for monitoring rapid surface process changes within operational systems. This paper assesses the performances of three methods for the NRT estimation of LAI: 1) Whittaker (Whit); 2) Gaussian process model (GPM); and 3) the climatological temporal smoothing and gap filling (CTSGF). The methods were evaluated using Advanced Very High Resolution Radiometer time series over a selection of BELMANIP2 sites representative of seasonal patterns of global biome vegetated areas and under varying level of noise and missing observations (gaps). A simulation experiment was designed to evaluate the predictive capabilities of the three methods with an emphasis on the global and local structure of missing observations in the time series. The results show that the three methods achieve similar performances (RMSE <; 0.4) when the fraction of missing data over the whole time series is lower than 65% or the length of gaps is smaller than 10 days. Conversely, for fraction of gaps higher than 65% or periods of gaps longer than 10 days, CTSGF is found to provide more accurate (RMSE <; 0.4 up to 60 days with missing data) NRT estimates of LAI than Whit and GPM. CTSGF uses the baseline seasonal cycle derived from the interannual median values of LAI to fill gaps in the time series and improve the NRT projections. Our findings support the operational use of the CTSGF algorithm for NRT estimation of biophysical products at the global scale.

The Copernicus Global Land Service is the achievement of more than 10 years of European R&D activities. It provides historic and NRT biophysical products useful for a wide range of applications. Its continuity and its evolution are ensured by the current research and innovation projects which are preparing the use of new EO missions data, including the future ESA Sentinels data.

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.

The performance of two time series processing methods, the Whittaker method (WM) and Gaussian Process Model (GPM), were assessed for real time estimation of leaf area index (LAI) derived from AVHRR daily data at 0.05° spatial resolution. The two methods were selected from an ensemble of methods, based on their ability to accept missing observations and to make short-term predictions. The performances of the two selected methods were evaluated as a function of the fraction of valid data and the length of gaps over a number of cases representing a range of temporal dynamics as well as distribution of missing observations. Results show that, when the length of gaps is smaller than 20 days and the fraction of valid data over the whole time series is lower than 50%, similar performances are achieved with the two methods with RMSE values lower than 0.25. For fraction of gaps higher than 50% or periods of gaps longer than 20 days GPM is more robust than the WM at the expenses of being more time consuming.

The aim of this work is to analyze the consistency of the FVC and LAI regional scale mapping products derived from new generation of sensor systems, SPOT/VEGETATION and MSG/SEVIRI, for vegetation monitoring, especially over hot spot areas affected by land degradation processes. The FVC has been developed from a probabilistic Spectral Mixture Analysis (SMA) using as input the nadir-zenith normalized TOC reflectance coefficient (k0) for the different spectral channels. In this sense, using k0as input accounts for the different acquisition geometry in the data. FVC is subsequently used to estimate LAI using the Roujean and Lacaze semi-empirical relationship. The results have demonstrated the competitiveness of the FVC and LAI derived products regarding similar satellite products at local scales and the suitability of using them to reproduce the temporal variability over dryland condition areas.

The aim of this work is to perform a direct validation of fraction of vegetation cover (FVC), leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) resulting products from applying the LSA SAF methodology to VEGETATION BRDF data. LSA SAF adapted algorithms were tested in adequate test sites comprising different continental biomes covering a wide range of FVC, LAI and FAPAR values. Results seem to indicate the competitiveness of LSA SAF proposed methodology to retrieve remotely sensed biophysical parameters. A noticeable good agreement regarding the ground measurements was found. The overall accuracy (RMSE) is around 20% for FVC and FAPAR and around 15% for LAI.