Automated Continuous Fields Prediction From Landsat Time Series: Application to Fractional Impervious Cover

Abstract

The characterization of fine temporal-resolution land surface dynamics from broadband optical satellite sensors is constrained by sparse acquisitions of high-quality imagery; interscene variation in radiometric, phenological, atmospheric, and illumination conditions; and subpixel variability in heterogeneous environments. In this letter, we address these concerns by developing and testing the automatic adaptive signature generalization and regression (AASGr) algorithm. Provided a robust reference map corresponding to the date of one image, AASGr automates the prediction of continuous fields maps from imagery time series that is adaptive to the spectral and radiometric characteristics of each target image and thereby requires neither atmospheric correction nor data normalization. We tested AASGr on a 22-year Landsat time series to quantify subannual impervious fractional cover dynamics in Houston, TX—an area characterized by a high degree of spatial heterogeneity in surface cover and high frequency in land cover change. The map time series achieved high accuracy in a three-part validation procedure and reveals spatio-temporal dynamics of urban intensification and extensification at a level of detail previously elusive in discrete classifications or coarse temporal-resolution map products. The automation of continuous fields time series is enabling a new generation of land surface products capable of characterizing precise morphologies along a continuum of spatio-temporal change. While AASGr was applied here to predict subpixel impervious fractional cover from Landsat imagery, the method is generalizable to a range of imagery and applications requiring dense continuous fields time series with uncertainty estimates of geophysical and biochemical characteristics, such as leaf area index, biomass, and albedo.