Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy

Abstract

Abstract:The availability of new fAPAR satellite products requires simultaneous efforts in validation to provide users with a better comprehension of product performance and evaluation of uncertainties. This study aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground references to determine to what extent the GCOS requirements on accuracy (maximum 10% or 5%) can be met in a deciduous beech forest site in a gently and variably sloped mountain site. Three ground reference fAPAR, differing for temporal (continuous or campaign mode) and spatial sampling (single points or Elementary Sampling Units-ESUs), were collected using different devices: (1) Apogee (defined as benchmark in this study); (2) PASTIS; and (3) Digital cameras for collecting hemispherical photographs (DHP). A bottom-up approach for the upscaling process was used in the present study. Radiometric values of decametric images (Landsat-8) were extracted over the ESUs and used to develop empirical transfer functions for upscaling the ground measurements. The resulting high-resolution ground-based maps were aggregated to the spatial resolution of the satellite product to be validated considering the equivalent point spread function of the satellite sensors, and a correlation analysis was performed to accomplish the accuracy assessment. PASTIS sensors showed good performance as fAPAR PASTIS appropriately followed the seasonal trends depicted by fAPAR APOGEE (benchmark) (R 2 = 0.84; RMSE = 0.01). Despite small dissimilarities, mainly attributed to different sampling schemes and errors in DHP classification process, the agreement between fAPAR PASTIS and fAPAR DHP was noticeable considering all the differences between both approaches. The temporal courses of the three satellite products were found to be consistent with both Apogee and PASTIS, except at the end of the summer season when ground data were more affected by senescent leaves, with both MODIS C5 and C6 displaying larger short-term variability due to their shorter temporal composite period. MODIS C5 and C6 retrievals were obtained with the backup algorithm in most cases. The three green fAPAR satellite products under study showed good agreement with ground-based maps of canopy fAPAR at 10 h, with RMSE values lower than 0.06, very low systematic differences, and more than 85% of the pixels within GCOS requirements. Among them, GEOV1 fAPAR showed up to 98% of the points lying within Remote Sens. 2017, 9, 126; doi:10.3390/rs9020126 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 126 2 of 28 the GCOS requirements, and slightly lower values (mean bias = −0.02) as compared with the ground canopy fAPAR, which is expected to be only slightly higher than green fAPAR in the peak season.

Global Estimation of Biophysical Variables from Google Earth Engine Platform

“…Regarding FAPAR, an overall negative bias is found for all biomes. However, this bias could not be regarded as an issue in the estimations, since different studies have pointed out a systematic overestimation of MODIS retrievals in both C5 and C6 at low FAPAR values as a main drawback of the product [59][60][61]. For example, Xu et al [64] assessed MODIS FAPAR through comparisons to ground measurements available from 2012-2016, obtaining a reasonable agreement (R 2 = 0.83, RMSE = 0.10) but with an overall overestimation tendency (bias = 0.08, scatters distributed within 0-0.2 difference).…”

“…In the case of FAPAR, there is a constant negative bias of ≈0.05 which is also noticeable in the scatter plots shown in Figure 8. This is related with a documented systematic overestimation of MODIS FAPAR retrievals [59][60][61], which is partly corrected by the proposed retrieval approach. The spatial consistency of LAI/FAPAR estimates was also compared over the African continent (Figure 10).…”

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

“…Following previous studies [36,40,56,57], the multivariate ordinary least squares (OLS) regression based on iteratively re-weighted least squares (IRLS) method was used to establish a relationship between the average PAI eff measurements from each ESU and the corresponding multispectral data from high-resolution data, in our case Landsat-7/8 data.…”

Characterisation of Functional-Trait Dynamics at High Spatial Resolution in a Mediterranean Forest from Sentinel-2 and Ground-Truth Data

“…Ground-truth data were acquired using digital hemispherical photography (DHP), which allowed the calculation of FAPAR, and other variables of canopy architecture (e.g., Leaf Area Index (LAI), Fraction of green vegetation cover (FCOVER)), based on directional measurements of the fraction of gaps in the vegetation cover [28][29][30][31][32]. We used a digital Nikon camera and an extreme wide-angle lens (180 • fisheye lens) for each measurement.…”