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Evaluation of Canopy Biophysical Variable Retrieval Performances from the Accumulation of Large Swath Satellite Data

Remote Sensing of Environment
Publication Date
DOI: 10.1016/s0034-4257(99)00045-0
  • Computer Science
  • Physics


Abstract The objective of this study was to compare the retrieval performances of several biophysical variables from the accumulation of large swath satellite data, the VEGETATION/SPOT4 sensor being taken as an example. This included leaf area index (LAI), fraction of photosynthetically active radiation ( fAPAR) and chlorophyll content integrated over the canopy ( C ab·LAI ), gap fraction in any direction [ P 0(θ)], or in particular directions (nadir [ P 0(0)], sun direction [ P 0(θ s )], or 58° [ P 0 (58°)] for which the gap fraction is theoretically independent of the LAI). A database of top of canopy BRDF (bidirectional reflectance distribution function) of homogeneous canopies was built using simulations by the SAIL, PROSPECT, and SOILSPECT radiative transfer models for a large range of input variables ( LAI, mean leaf inclination angle, hot spot parameter, leaves and soil optical properties, date and latitude of observations) considering the accumulation of observations during an orbit cycle of 26 days. Walthall's BRDF model was used to estimate nadir (ρ 0) and hemispherical reflectance (ρ h). Results showed that ρ 0 and ρ h were estimated with a good accuracy (RMSE=0.02) even when few observations within a sequence were available due to cloud masking. The ρ 0 and ρ h estimates in the blue (445 nm), the red (645 nm), near-infrared (835 nm), and middle infrared (1665 nm) were then used as inputs to neural networks calibrated for estimation of the canopy biophysical variables using part of the data base. Performances evaluated over the rest of the database showed that variables such as nadir gap fraction (≅ P 0 (58°)≅ P 0(θ s)≅ fAPAR) were accurately estimated by neural networks (relative RMSE<0.05). Results of the estimation of LAI ( ≅LAI·Cab) was less satisfactory since the level of reflectance saturates for high values of LAI (relative RMSE<0.08). The estimation of the directional variation of the gap fraction was not accurate because the amount of directional information contained in the input variables of the neural network was not sufficient. We also investigated the problem of mixed pixels due to the low spatial resolution associated with large swath sensors. Results showed that variables such as nadir gap fraction were not as sensitive to high levels of heterogeneity in pixels as variables such as leaf area index.

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