Abstract
Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.
Highlights
The importance of vegetation is widely perceived in studies of land–atmosphere interactions [1,2,3]
Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) retrievals were derived for non-vegetated pixels
According to the scene classification algorithm described in Sentinel-2 surface reflectance products algorithm theoretical basis document (ATBD), the vegetation or non-vegetated pixels were identified using NDVI and a reflectance ratio index defined by the ratio of the reflectance of the near-infrared band to that of the green band
Summary
The importance of vegetation is widely perceived in studies of land–atmosphere interactions [1,2,3]. I.e., Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC), characterize the function of vegetation and are widely used in a broad range of user communities [4,5,6,7]. Since the mono-angle observation of remote sensing is not sensitive to the possible heterogeneity in leaf distribution within the canopy, LAI derived from the remote sensing is often called effective LAI (hereafter, LAIe) that assumes a random distribution of leaves in canopy volume [9]. FAPAR measures the fraction of radiation absorbed by leaves in the 0.4–0.7 μm spectrum [11], and FVC is the ratio of the vertically projected area of vegetation to the total surface area [12]. To collect long-term global LAI, FAPAR, and FVC datasets for the monitoring and modeling of large-scale agroecosystems, satellite remote sensing provides an effective way to generate these biophysical products on a regular basis [17]
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