B. Datt

TL;DR: Preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results and it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes.

TL;DR: In this paper, a Monte Carlo procedure was used to randomly stack various numbers of bands into thousands of separate image combinations that were then classified using an ML algorithm, and 95% of the accuracy attained using n = 30p samples could be achieved by using approximately 2p to 4p samples, or ≤ 1/7th the currently recommended value of n.

TL;DR: In this article, the benefits of EO-1 data, and especially Hyperion hyperspectral data, are studied at sites in the Coleambally Irrigation Area of Australia where a seasonal time series has been developed.

TL;DR: Results show that new hyperspectral indices such as the first derivative value and wavelength position of the red edge inflection point are more sensitive to within-paddock variations in yield than commonly used indicessuch as NDVI.

TL;DR: The basic processing steps that have been applied to geometrically and atmospherically correct the data and reduce the effects of noise prior to investigating the time series of specific measures of crop productivity through its biochemistry are described.