Impact of differences in the solar irradiance spectrum on surface reflectance retrieval with different radiative transfer codes

Surface reflectance retrieval from imaging spectrometer data as acquired with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has become important for quantitative analysis. In order to calculate surface reflectance from remotely measured radiance, radiative transfer codes such as 5S and MODTRAN2 play an increasing role for removal of scattering and absorption effects of the atmosphere. Accurate knowledge of the exo-atmospheric solar irradiance (E(sub 0)) spectrum at the spectral resolution of the sensor is important for this purpose. The present study investigates the impact of differences in the solar irradiance function, as implemented in a modified version of 5S (M5S), 6S, and MODTRAN2, and as proposed by Green and Gao, on the surface reflectance retrieved from AVIRIS data. Reflectance measured in situ is used as a basis of comparison

Estimation of crown closure from AVIRIS data using regression analysis

Crown closure is one of the input parameters used for forest growth and yield modelling. Preliminary work by Staenz et al. indicates that imaging spectrometer data acquired with sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have some potential for estimating crown closure on a stand level. The objectives of this paper are: (1) to establish a relationship between AVIRIS data and the crown closure derived from aerial photography of a forested test site within the Interior Douglas Fir biogeoclimatic zone in British Columbia, Canada; (2) to investigate the impact of atmospheric effects and the forest background on the correlation between AVIRIS data and crown closure estimates; and (3) to improve this relationship using multiple regression analysis

Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data

Sherpa Romeo green journal; open accessSoil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).Ye

Soil Backgrounds Impact Analysis on Chlorophyll Indices Using Field, Airborne and Satellite Hyperspectral Data

Non

Scene-based spectral response function shape discernibility for the APEX imaging spectrometer

Scene-based spectrometer calibration is becoming increasingly interesting due to the decreasing cost of computing resources as compared with laboratory calibration costs. Three of the most important instrument parameters needed for deriving surface reflectance products are per-band bandwidths, i.e., full-width at half-maximum, band centers, and spectral response function (SRF) shape. Methods for scene-based bandwidth and band center retrieval based on curve matching in the spectral regions near well-known solar and atmospheric absorption features have been investigated with satisfying results. The goal of this work is to establish the feasibility of per-band SRF shape discernibility. To this end, at-sensor radiances in multiple application configurations have been modeled using Moderate-Resolution Atmospheric Transmission (MODTRAN) 4 configured for the currently being built Airborne Prism Experiment (APEX) imaging spectrometer in its unbinned configuration (i.e., optimized for spectral resolution). To establish SRF shape discernment feasibility, per-band MODTRAN 4 spectral “filter response function” files have been generated for five common theoretical shapes using APEX nominal bandwidth and band center specifications and are provided as MODTRAN 4 input for the instrument model. In several application configurations, the typically used Gaussian SRF is used as reference and compared with radiances resulting from hypothetical instruments based on the four other shapes to detect differences in selected spectral subsets or “windows” near well-known Fraunhofer features. A relative root-mean-square metric is used to show that discernment in some cases is directly feasible, and in others, feasible if noise reduction techniques (e.g., along-track averaging of homogeneous targets) are possible