Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

Iiames, John; Congalton, Russell G.; Lewis, Timothy E.; Pilant, A. N.

doi:10.3390/rs70201397

Cited by 11 publications

(7 citation statements)

References 55 publications

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“…Also, they found significant differences dependent on the algorithm pathway chosen, which was a product of atmospheric interference (i.e., cloud contamination) and the number of quality scenes acquired in an 8-day chronosequence. EPIC or any other validated biomass model may work in combination with the satellite feed by constraining or inflating LAI values to reasonable figures based on biases observed in these validation studies [ 46 ]. Thus, the modeled LAI could provide bias corrections where the satellite-derived LAI could provide the timing of green-up and senescence and relative seasonal changes in LAI.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA

Iiames

Cooter

Schwede

et al. 2018

Forests

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Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult to develop as a result of interspecies competition, age and spatial distribution. Satellite-based LAI estimates hold promise for retrospective analyses, but we must continue to rely on numerical models for alternative management analysis. Our objective for this study is to calculate and validate LAI estimates generated from the USDA Environmental Policy Impact Climate (EPIC) model (a widely used, field-scale, biogeochemical model) on four forest complexes spanning three physiographic provinces in Virginia and North Carolina. Measurements of forest composition (species and number), LAI, tree diameter, basal area, and canopy height were recorded at each site during the 2002 field season. Calibrated EPIC results show stand-level temporally resolved LAI estimates with R2 values ranging from 0.69 to 0.96, and stand maximum height estimates within 20% of observation. This relatively high level of performance is attributable to EPIC’s approach to the characterization of forest stand biogeochemical budgets, stand history, interspecies competition and species-specific response to local weather conditions. We close by illustrating the extension of this site-level approach to scales that could support regional air quality model simulations.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA

Iiames

Cooter

Schwede

et al. 2018

Forests

Self Cite

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“…Due to the resolution-mismatch, geolocation errors, the fineresolution reference maps up-scaled from in situ measurements are recommended to validate coarse resolution remote sensing products instead of directly using ground data [49,50]. Since the widely used DIRECT 2.0 ground database (2000-2017) has no field measurements available during the GOES-16 ABI era (2018-present), the accuracy assessment of ABI products was performed using the field-measurements-derived reference maps from GBOV collected in 2018.…”

Section: Evaluation Of Derived Abi Lai/fpar Productsmentioning

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

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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).

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“…Error in the creation of the LAI-RM may be introduced in the sampling strategy employed, in the optical measurements used within the modified Beer-Lambert light extinction function, in the analyst-effect in LC delineation [50], and in the correspondence of vegetation indices to ground measured LAI. In a prior study, we investigated variables ingested into the modified Beer-Lambert light extinction function.…”

Section: Mc5 Mc6 and Lai-rm Comparisons (10 Km)mentioning

confidence: 99%

Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales

et al. 2020

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Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29–1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.

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Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

Cited by 11 publications

References 55 publications

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA

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

Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales

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