Influence of data acquisition geometry on soybean spectral response simulated by the prosail model

Abstract

View angle and directional effects significantly affect reflectance and vegetation indices, especially when daily images collected by large field-of-view (FOV) sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) are used. In this study, the PROSAIL radiative transfer model was chosen to evaluate the impact of the geometry of data acquisition on soybean reflectance and two vegetation indices (Normalized Difference Vegetation Index -NDVI and Enhanced Vegetation Index -EVI) by varying biochemical and biophysical parameters of the crop. Input values for PROSAIL simulation were based on the literature and were adjusted by the comparison between simulated and real satellite soybean spectra acquired by the MODIS/Terra and hyperspectral Hyperion/Earth Observing-One (EO-1). Results showed that the influence of the view angle and view direction on reflectance was stronger with decreasing leaf area index (LAI) and chlorophyll concentration. Because of the greater dependence on the near-infrared reflectance, the EVI was much more sensitive to viewing geometry than NDVI presenting larger values in the backscattering direction. The contrary was observed for NDVI in the forward scattering direction. In relation to the LAI, NDVI was much more isotropic for closed soybean canopies than for incomplete canopies and a contrary behavior was verified for EVI. Influence of data acquisition geometry on soybean spectral response simulated by the PROSAIL Model KEYWORDSEng.

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

“…Hyperspectral data used in the studies (blue bars in Figure 3) were mostly collected by ground-based spectrometer (ASD field spec) and airborne sensors, typically Compact Airborne Spectrographic Imager (CASI), DLR airborne sensor HySpex, Airborne imaging spectrometer HyMap or AHS, INTA. Nevertheless, the two operating hyperspectral satellite platform, i.e., ESA's CHRIS on Proba, and Hyperion/Earth Observing-One (EO-1), were also exploited by a few studies, e.g., CHRIS [140,[144][145][146] and Hyperion: [41,112,121]. The collection of hyperspectral data does not imply that the studies exploited the full spectral range measured.…”

“…Indeed, a study estimating water-related variables (leaf and canopy water content) from forest, shrubs, and grassland found that the PROSAIL model inversion performance was rather limited across sites without precise in-situ knowledge [133]. For crops, most studies found that PROSAIL achieved reasonably accurate simulations, e.g., [41,83,110,111,137,147]. Compared to a three-dimensional dynamic maize model, the PROSAIL model performance was only slightly decreased [81].…”

Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency

“…For instance, using a field spectrometer it can be difficult to control variations in illumination geometry, canopy height and weather conditions across time and space under natural lighting [33]. Thus, the timing and order in which locations are visited to collect data can affect plant trait measurements, spectral measurements or both [34,35].…”

Retrieval of Salt Marsh Above-Ground Biomass from High-Spatial Resolution Hyperspectral Imagery Using PROSAIL

“…In spatially heterogeneous marshes, even this scale may be too coarse to capture the detailed variability inherent in these systems and derive accurate models of biomass. Physics-based radiative transfer models, such as PROSAIL, have also been widely used to characterize biophysical properties of vegetation [47][48][49][50][51][52][53][54][55][56][57][58]. Physics-based models are capable of simulating the propagation of light and interaction with plant canopies, and interpreting vegetation reflectance in terms of plant structure and biophysical parameters [48].…”