Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

Fuster, Beatriz; Sánchez-Zapero, Jorge; Camacho, Fernando; García-Santos, Vicente; Verger, Aleixandre; Lacaze, Roselyne; Weiss, Marie; Baret, Frédéric; Smets, Bruno

doi:10.3390/rs12061017

Cited by 118 publications

(68 citation statements)

References 34 publications

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“…This result is consistent with two validation studies which both showed MODIS LAI had the poorest performance for the evergreen forests in the south of China [80,81]. The MODIS LAI values for tropical evergreen forests are severely impacted by atmospheric conditions, especially clouds during the growing season (around 42% data are influenced by the cloud in this study, Figure S8), which lead to strong noise in the input reflectance data and affect the retrieval [78]. Additionally, the reflectance saturation usually happened in dense canopies and the main algorithm is sensitive to uncertainties in atmospheric correction, particularly when red and NIR BRFs are saturated [82,83].…”

Section: Discussionsupporting

confidence: 90%

“…In contrast, the core operational algorithm (neural network techniques), data filtering and smoothing processes are similar for these two products. There are differences in the method used for temporal compositing, where temporal smoothing and gap filling using a climatology are used for GEOV2 1 km and interpolation applied in GEOV3 300 m) [78]. Differences in the applied gap-filling approach between these two products do not impact our conclusion that resolution is the primary driver of performance improvement at 300 m relative to 1 km, as all gap-filled and interpolated retrievals were removed in our study.…”

Section: Discussionmentioning

confidence: 95%

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Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

et al. 2020

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Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 95%

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

et al. 2020

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“…Figure 4 shows the global distribution of the sampling used in this study. The product intercomparison is evaluated over a 725-site land validation network (LANDVAL) of sites [67], which is designed to globally represent the variability of land surface types, and was used as the spatial sampling to evaluate these criteria. This network also includes 20 well-known desert calibration sites [26] for the precision evaluation, due to their high temporal stability.…”

Section: Validation Methodsmentioning

confidence: 99%

Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service

et al. 2020

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The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.

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“…LAI, fAPAR and fCover products from lowresolution sensors such as MODIS, MERIS, SPOT-VEGETATION and PROBA-V have been routinely generated and made available free of charge to the public (Bacour et al, 2006;Baret et al, 2007;Fuster et al, 2020;Myneni et al, 2002). Recent work by Clevers et al (2017), Delloye et al (2018) and Pan et al (2019) has shown the potential of Sentinel-2 data for retrieving LAI for crops.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria

Kamenova

Dimitrov

2020

European Journal of Remote Sensing

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The red-edge bands of Sentinel-2 allow for a greater diversity of spectral Vegetation Indices (VIs) to be calculated and used for vegetation characterization. We evaluated the utility of a selection of 40 VIs to derive Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and fraction of vegetation Cover (fCover) of winter wheat crop using regression method. We calibrated models for specific winter wheat development stages and compared the predictions with all-season models. The most useful VIs could be grouped into several types: (1) indices which use green and NIR band, (2) indices based on red edge bands, (3) indices which use red and NIR band and (4) the MCARI/OSAVIre index. It was found that fAPAR and fCover could be predicted with good accuracy using all-season models (rRMSE of 14% and 23% respectively), while LAI showed lower accuracy (rRMSE = 45%). The LAI model calibrated over the tillering stage was recommended for usage in the early stages of crop development. Compared with the existing methods for biophysical variables retrieval from Sentinel-2 data (i.e. the Level2B processor in SNAP) the regression approach based on VIs showed to be a viable alternative.

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Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

Cited by 118 publications

References 34 publications

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service

Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria

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