Abstract
This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC (Optical Properties of Aerosols and Clouds) model, while aerosol optical depth is estimated using the dark target method. A new approach is proposed for adjacency effect correction. Topographic effects were also taken into account, and a new model was developed for forest canopies. Validation has shown that ground reflectance estimation with REFLECT is performed with an accuracy of approximately ±0.01 in reflectance units (for the visible, near-infrared, and mid-infrared spectral bands), even for surfaces with varying topography. The validation of the software was performed through many tests. These tests involve the correction of the effects that are associated with sensor calibration, irradiance, and viewing conditions, atmospheric conditions (aerosol optical depth AOD and water vapour), adjacency, and topographic conditions.
Highlights
Multispectral satellite imagery, especially at high spatial resolution (30 m or finer), represents an invaluable source of information for decision making [1] in various domains in connection with natural resources management, environment preservation, or urban planning and management
In order to find the equivalent slope that adapts our topographic correction model to the forest canopy, we looked for the multiplicative factor κ, which minimises the RMSE between the ground reflectance of inclined terrains and of flat terrains (β < 6◦) for all of the forested area of the three SPOT-1 images used, and all of the forest stands combined
The objective of this research was to develop a tool (REFLECT software package) for ground reflectance restitution, which considers all of the radiometric effects affecting multispectral images, including illumination and viewing conditions, sensor properties, atmospheric conditions, and topography
Summary
Multispectral satellite imagery, especially at high spatial resolution (30 m or finer), represents an invaluable source of information for decision making [1] in various domains in connection with natural resources management, environment preservation, or urban planning and management. Common applications of remotely sensed imagery concern vegetation cover (species, biomass, damages, crop yields, etc.), surface conditions (land use, types of materials, soil physics, and chemistry, etc.) or the inventory of natural resources [2]. This involves the transition from a relative scale (gray scale) of measured physical variables (radiances) to a scale of biophysical variables (biomass, leaf cover, etc.) or a system of classes (e.g., type of land use). The basic assumption is that the signal recorded by the sensor is directly related to the state and/or nature of the target objects on the ground
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