An investigation of surface albedo variations during the recent sahel drought

Applications Technology Satellite 3 green sensor data were used to measure surface reflectance variations in the Sahara/Sahel during the recent drought period; 1967 to 1974. The magnitude of the seasonal reflectance change is shown to be as much as 80% for years of normal precipitation and less than 50% for drought years. Year to year comparisons during both wet and dry seasons reveal the existence of a surface reflectance cycle coincident with the drought intensity. The relationship between the green reflectance and solar albedo is examined and estimated to be about 0.6 times the reflectance change observed by the green channel

Ground Reflectance Measurements from Thematic Mapper Data

Satellite radiance data are measures of solar radiation that has been reflected by the Earth's surface and scattered and absorbed by atmospheric gases and aerosols. Of concern to geologists are the surface reflectance and the degrading effects on surface resolution and albedo contrast introduced by atmospheric phenomena. The objects of this research have been to: (1) provide an empirical relationship between scanner radiance and ground reflectance allowing interpretation of the satellite data in terms of the surface parameter, and (2) assess the precision with which surface spectral reflectance may be recovered from LANDSAT-4 TM data in the presence of perturbing atmospheric and instrumental factors. The approach is field-oriented, and utilizes ground observations of surface spectral reflectance with portable spectrometers and radiometers to develop the required empirical relationships

The temporal dynamics of calibration target reflectance

A field experiment investigated the hypothesis that the nadir reflectance of calibration surface substrates (asphalt and concrete) remains stable over a range of time-scales. Measurable differences in spectral reflectance factors were found over periods as short as 30 minutes. Surface reflectance factors measured using a dual-field-of-view GER1500 spectroradiometer system showed a relationship with the relative proportion of diffuse irradiance, over periods when solar zenith changes were minimal. Reflectance measurements were collected over precise points on the calibration surfaces using a novel mobile spectroradiometer device, and uncertainty in terms of absolute reflectance was calculated as being < 0.05% within the usable range of the instrument (400-1000nm). Multi-date reflectance factors were compared using one-way ANOVA and found to differ significantly (p = 0.001). These findings illustrate the anisotropic nature of calibration surfaces, and place emphasis on the need to minimise the temporal delay in collection of field spectral measurements for vicarious calibration or empirical atmospheric correction purposes

Reflectance Hashing for Material Recognition

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

A simplified treatment of SiB's land surface albedo parameterization

The earlier presented surface albedo parameterization is simplified by assuming that the reflectance of direct solar radiation is a simple function of solar zenith angle. The function chosen contains three parameters that vary with vegetation type, greenness, and leaf area index. Tables of parameter values are presented. Using these tables, SiB's (Simple Biosphere model) absorbances of direct solar radiation can be reproduced with an average relative error of less than 0.5 percent. Finally, the direct reflectance function is integrated over zenith angle to produce an equation for the surface reflectance of diffuse radiation