Retrieving leaf area index from multi-angular airborne data

This work is aimed to demonstrate the feasibility of a methodology for retrieving bio-geophysical variables whilst at the same time fully accounting for additional information on directional anisotropy. A model-based approach has been developed to deconvolve the angular reflectance into single landcovers reflectances, attempting to solve the inconsistencies of 1D models and linear mixture approaches. The model combines the geometric optics of large scale canopy structure with principles of radiative transfer for volume scattering within individual crowns. The reliability of the model approach to retrieve LAI has been demonstrated using data from DAISEX- 99 campaign at Barrax, Spain. Airborne data include POLDER and HyMap data in which various field plots were observed under varying viewing/illumination angles. Nearly simultaneously, a comprehensive field data set was acquired on specific crop plots. The inversions provided accurate LAI values, revealing the model potential to combine spectral and directional information to increase the likely accuracy of the retrievals. In addition, the sensitivity of retrievals with the angular and spectral subset of observations was analysed, showing a high consistency between results. This study has contributed to assess the uncertainties with products derived from satellite data like SEVIRI/MSG

Semi-empirical modeling of the scene reflectance of snow-covered boreal forest : Validation with airborne spectrometer and LIDAR observations

This work aims at the development and validation of a zeroth order radiative transfer (RT) approach to describe the visible band (555 nm) reflectance of conifer-dominated boreal forest for the needs of remote sensing of snow. This is accomplished by applying airborne and mast-borne spectrometer data sets together with high-resolution information on forest canopy characteristics. In case of aerial spectrometer observations, tree characteristics determined from airborne LIDAR observations are applied to quantify the effect of forest canopy on scene reflectance. The results indicate that a simple RT model is feasible to describe extinction and reflectance properties of both homogeneous and heterogeneous forest scenes (corresponding to varying scales of satellite data footprints and varying structures of forest canopies). The obtained results also justify the application of apparent forest canopy transmissivity to describe the influence of forest to reflectance, as is done e.g. in the SCAmod method for the continental scale monitoring of fractional snow cover (FSC) from optical satellite data. Additionally, the feasibility of the zeroth order RT approach is compared with the use of linear mixing model of scene reflectance. Results suggest that the nonlinear RT approach describes the scene reflectance of a snow-covered boreal forest more realistically than the linear mixing model (in case when shadows on tree crowns and surface are not modeled separately, which is a relevant suggestion when considering the use of models for large scale snow mapping applications). (C) 2014 The Authors. Published by Elsevier Inc.Peer reviewe

A refined four-stream radiative transfer model for row-planted crops

In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world

Estimating the crop leaf area index using hyperspectral remote sensing

AbstractThe leaf area index (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the characteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the “curse of dimensionality” and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review

Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data

A synergistic algorithm for producing global leaf area index and fraction of absorbed photosynthetically active radiation fields from canopy reflectance data measured by MODIS (moderate resolution imaging spectroradiometer) and MISR (multiangle imaging spectroradiometer) instruments aboard the EOS-AM 1 platform is described here. The proposed algorithm is based on a three-dimensional formulation of the radiative transfer process in vegetation canopies. It allows the use of information provided by MODIS (single angle and up to 7 shortwave spectral bands) and MISR (nine angles and four shortwave spectral bands) instruments within one algorithm. By accounting features specific to the problem of radiative transfer in plant canopies, powerful techniques developed in reactor theory and atmospheric physics are adapted to split a complicated three-dimensional radiative transfer problem into two independent, simpler subproblems, the solutions of which are stored in the form of a look-up table. The theoretical background required for the design of the synergistic algorithm is discussed