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TitleValidation of simplified level 2 prototype processor sentinel 2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests
AuthorFernandes, R AORCID logo; Canisius, F; Hong, G; MacDougall, C; Shah, H; Sun, L; Brown, L; Meier, C; Dash, J; Baret, F; Weiss, M; Spafford, L; Darko, P O
SourceRemote Sensing of Environment vol. 293, 113600, 2023 p. 1-19,
Alt SeriesNatural Resources Canada, Contribution Series 20220404
Mediapaper; digital; on-line
File formatpdf; html
ProvinceCanada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut
NTS1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65; 66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560
AreaNorth America; Canada; United States of America
Lat/Long WENS-141.0000 -50.0000 90.0000 41.7500
SubjectsScience and Technology; Forests
Illustrationstables; location maps; diagrams; charts; cross-plots
ProgramCanada Centre for Mapping and Earth Observation
Released2023 08 01
AbstractCanopy biophysical variables such as the fraction of canopy cover (fCOVER), fraction of absorbed photosynthetically active radiation (fAPAR), and leaf area index (LAI) are widely used for ecosystem modelling and monitoring. The Sentinel-2 mission was designed for systematic global mapping of these variables at 20 m resolution using imagery from the MultiSpectral Instrument. The Simplified Level 2 Prototype Processor (SL2P) is available as a baseline mapping solution. Previous validation over limited sites indicates that SL2P generally satisfies user requirements for all three variables over crops, but underestimates LAI over forests. In this study, Sentinel-2 fAPAR, fCOVER, and LAI products, from SL2P, were validated over 281 sites representative of most North American forest ecozones and also compared to Moderate Resolution Imaging Spectrometer (MODIS) and Copernicus Global Land Service (CGLS) products. In addition to meeting the Committee on Earth Observation Satellites Stage 3 validation requirements for these areas, our study also explores the relationship between bias in SL2P products and canopy clumping and provides empirical bias correction functions for each variable. SL2P was implemented within the Landscape Evolution and Forecasting Toolbox in Google Earth Engine both for efficiency and due to bugs in the Sentinel Application Platform implementation. SL2P was found to underestimate LAI by 20% to 50% over forests with LAI > 2; in agreement with other studies and with comparisons to MODIS and CGLS products. SL2P bias for fCOVER and fAPAR transitions from ~0.1 at low values to ~ - 0.1 at high values. Precision error, at one standard deviation, was ~0.5 for LAI and slightly less than ~0.1 for fCOVER and fAPAR. Total uncertainty was dominated by bias for LAI and was slightly greater than precision error for fCOVER and fAPAR. Target user requirements were satisfied for 51% of LAI, 37% of fCOVER and 31% of fAPAR comparisons to in-situ measurements. For all variables, accuracy exhibited weak to moderate linear relationships to clumping (r2 =0.52), but scatter plots indicated larger negative LAI biases over northern latitude sites where canopies exhibited greater clumping. With the exception of evergreen broadleaf forests, empirical bias correction using in-situ data reduced accuracy error by 40% for fCOVER, 57% for fAPAR and, 92% for LAI and increased the agreement rate with uncertainty requirements by up to 8%. Users of SL2P LAI over forests are recommended to apply bias correction or consider recalibrating SL2P with spatially heterogenous radiative transfer model simulations.
Summary(Plain Language Summary, not published)
An algorithm for mapping vegetation using 20m resolution satellite imagery was validated over 295 forest plots across North America. Comparison of maps with field measurements found that some parameters (canopy cover and fraction of absorbed light for photosynthesis) were mapped with low bias and overall uncertainty close to international standards. However, a parameter related to vegetation density (leaf area index) was mapped with significant bias likely related to the clumped nature of forest canopies. Further improvements in the algorithm are required to reduce this bias.

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