Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data

Brown, Luke A.; Meier, Courtney L.; Morris, Harry; Pastor-Guzman, Julio; Bai, Gabriele; Lerebourg, Christophe; Gobron, Nadine; Lanconelli, Christian; Clerici, M.; Dash, Jadunandan

doi:10.1016/j.rse.2020.111935

Cited by 98 publications

(65 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will include a review of site deployment considerations and an initial plan for the establishment of permanent ESAsupported FRM4VEG 'supersites'. Finally, to increase the volume of FRM-compliant data available for vegetation bio-geophysical product validation, it is also anticipated that the FRM4VEG uncertainty evaluation procedures will be adopted, where applicable, by other related validation efforts, such as the Copernicus Ground Based Observations for Validation (GBOV) service [9]. Combined, these activities will provide a rich resource of reference data which can be exploited in future validation exercises for product conformity testing.…”

Section: Limitations and Potential Refinementsmentioning

confidence: 99%

“…Additionally, as EO products are further adopted in operational contexts, reliable information on compliance with uncertainty requirements will be increasingly important in the context of regulatory initiatives, auditing efforts, and liability debates [8]. Over the last two decades, substantial progress in the validation of vegetation bio-geophysical products has been made, including the development of standard in situ reference measurement datasets [9][10][11][12][13][14][15] and community-agreed best practices [16,17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework

et al. 2021

Self Cite

View full text Add to dashboard Cite

With a wide range of satellite-derived vegetation bio-geophysical products now available to users, validation efforts are required to assess their accuracy and fitness for purpose. Substantial progress in the validation of such products has been made over the last two decades, but quantification of the uncertainties associated with in situ reference measurements is rarely performed, and the incorporation of uncertainties within upscaling procedures is cursory at best. Since current validation practices assume that reference data represent the truth, our ability to reliably demonstrate compliance with product uncertainty requirements through conformity testing is limited. The Fiducial Reference Measurements for Vegetation (FRM4VEG) project, initiated by the European Space Agency, is aiming to address this challenge by applying metrological principles to vegetation and surface reflectance product validation. Following FRM principles, and in accordance with the International Standards Organisation’s (ISO) Guide to the Expression of Uncertainty in Measurement (GUM), for the first time, we describe an end-to-end uncertainty evaluation framework for reference data of two key vegetation bio-geophysical variables: the fraction of absorbed photosynthetically active radiation (FAPAR) and canopy chlorophyll content (CCC). The process involves quantifying the uncertainties associated with individual in situ reference measurements and incorporating these uncertainties within the upscaling procedure (as well as those associated with the high-spatial-resolution imagery used for upscaling). The framework was demonstrated in two field campaigns covering agricultural crops (Las Tiesas–Barrax, Spain) and deciduous broadleaf forest (Wytham Woods, UK). Providing high-spatial-resolution reference maps with per-pixel uncertainty estimates, the framework is applicable to a range of other bio-geophysical variables including leaf area index (LAI), the fraction of vegetation cover (FCOVER), and canopy water content (CWC). The proposed procedures will facilitate conformity testing of moderate spatial resolution vegetation bio-geophysical products in future validation exercises.

show abstract

Section: Limitations and Potential Refinementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Land Products (LPs) (unscaled LAI/FPAR ground-based reference maps at 300 m × 300 m pixel size) from Ground-Based Observations for Validation (GBOV, https://gbov.acri.fr/) were used in this study to validate the ABI retrievals [37]. In addition to the LP values themselves, per-pixel quality indicators are provided to identify pixels.…”

Section: Evaluation and Validation Sitesmentioning

confidence: 99%

Prototyping of LAI and FPAR Retrievals From GOES-16 Advanced Baseline Imager Data Using Global Optimizing Algorithm

Chen

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The latest Geostationary (GEO) Operational Environmental Satellite-16 (GOES-16) equipped with Advanced Baseline Imager (ABI) has comparable spectral and spatial resolution as low earth orbiting (LEO) sensors (i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS)), but with up-tothe-minute image acquisition capability. This enables greater opportunities to generate two essential climate variables-Leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation with more cloud-free observations and at much higher frequency. The improved GEO LAI/FPAR products will increase the capacity for monitoring highly dynamic ecosystems in a timely manner. However, the radiative transfer (RT)-based MODIS operational algorithm cannot be directly applied to GOES-16 ABI data due to different sensor characteristics. Fortunately, it has been shown theoretically and practically, that the RT-based algorithm can be transplanted to any other optical sensors by optimizing the sensor-specific parameters-the single scattering albedo (SSA) and relative stabilized precision (RSP). We built the RT-based ABI-specific lookup tables (LUTs) using a global optimizing algorithm (SCE-UA) that can quickly find the optimal solution. SCE-UA optimizes the SSAs and RSPs in the LUTs by minimizing the difference between ABI and MODIS retrievals and maximizing the main algorithm execution rate. Our efforts indicate that these strategies of parametric optimization is able to decrease the discrepancy between the ABI and MODIS LAI/FPAR products. Comprehensive evaluations were conducted to evaluate ABI retrievals. These indirect inter-comparisons suggest a spatiotemporal consistency between ABI and the benchmark MODIS products, while direct validation with field measurements increases confidence in their accuracy. The proposed approach is applicable to any other optical sensors for LAI/FPAR estimation, especially, GEO sensors (i.e., Himawari-8, Geo-KOMPSAT-2A, FengYun-4 etc.). Index Terms-GeostationaryOperational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI), Leaf area index (LAI), fraction of photosynthetically active radiation (FPAR), moderate resolution imaging spectroradiometer (MODIS), global optimizing algorithm (SCE-UA).

show abstract

“…F. been explicitly evaluated against independent reference data, whilst direct comparison of multitemporal transfer functions to RTM-based approaches, which have the potential to provide more robust results by accounting for variations in sun-sensor geometry [13]- [15], have not been made in the case of upscaling temporally continuous in situ data. Using temporally continuous in situ data collected at a deciduous broadleaf forest site in Southern England, we compare both vegetation indexbased multitemporal transfer functions and RTM-based methods for the generation of high spatial resolution LAI reference maps.…”

Section: Introductionmentioning

confidence: 99%

Deriving Leaf Area Index Reference Maps Using Temporally Continuous In Situ Data: A Comparison of Upscaling Approaches

Brown

Ogutu

Camacho³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

To further progress the validation of global leaf area index (LAI) products, temporally continuous reference data is a key requirement, as periodic field campaigns fail to adequately characterise temporal dynamics. Progress in cost-effective automated measurement techniques has been made in recent years, but appropriate upscaling methodologies are less mature. Recently, the use of multitemporal transfer functions has been proposed as a potential solution. Using data collected during an independent field campaign, we evaluated the performance of both vegetation index-based multitemporal transfer functions and a radiative transfer model (RTM)-based upscaling approach. Whether assessed using cross validation or data from the independent field campaign, the RTM-based approach provided the best performance (r 2 ≥ 0.88, RMSE ≤ 0.41, NRMSE < 13%). For upscaling temporally continuous in situ data, the ability of RTM-based approaches to account for seasonal changes in sunsensor geometry is a key advantage over vegetation index-based multitemporal transfer functions.

show abstract

Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data

Cited by 98 publications

References 87 publications

Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework

Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework

Prototyping of LAI and FPAR Retrievals From GOES-16 Advanced Baseline Imager Data Using Global Optimizing Algorithm

Deriving Leaf Area Index Reference Maps Using Temporally Continuous In Situ Data: A Comparison of Upscaling Approaches

Contact Info

Product

Resources

About