On the separate retrieval of soil and vegetation temperatures from ATSR data

The Along-Track Scanning Radiometer (ATSR) onboard the European Remote Sensing satellite (ERS) is presently the only one available to provide quasi-simultaneous thermal infrared measurements at two view angles. Such data represent an opportunity to explore the potential information on the directional observations in the thermal infrared region, in view of the preparation of a new generation of multi-angle satellite sensors. Based on the analysis of one ATSR image, the results of this work indicate that the magnitude of the directional effect on the brightness temperature (ground anisotropic radiance), although quite sensitive to errors in atmospheric conditions, may still be retrieved with acceptable uncertainty. In order to retrieve both vegetation and soil temperatures from directional brightness temperatures, it is shown that an appropriate description of the nature and content of the pixel is needed, otherwise this retrieval will be quite uncertain.

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machinelearning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery.The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací,Minas Gerais,Brazil.The proposed workflow includes subset feature selection,learning,and estimation algorithms.Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil(e.g.0.5 exceedance for pixels:75%clay-rich Nitisols,15%iron-rich Latosols,and 1%quartz-rich Arenosols)and vegetation(e.g.0.5 exceedance for pixels:4%Aspen-like trees,7%Blackberry-like trees,0%live grass,and 2%dead grass).The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages,and correctly classifies the clay-rich Nitisols and grasses on the intervening hills.These classifications are independently corroborated visually(Google Earth)and quantitatively(random soil samples and crossplots of field spectra).Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist,and the under representation of classified grass in some dry areas of the Hyperion image.These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.