Barest Pixel Composite for Agricultural Areas Using Landsat Time Series
AbstractMany soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making. View Full-Text
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Diek, S.; Fornallaz, F.; Schaepman, M.E.; de Jong, R. Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sens. 2017, 9, 1245.
Diek S, Fornallaz F, Schaepman ME, de Jong R. Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sensing. 2017; 9(12):1245.Chicago/Turabian Style
Diek, Sanne; Fornallaz, Fabio; Schaepman, Michael E.; de Jong, Rogier. 2017. "Barest Pixel Composite for Agricultural Areas Using Landsat Time Series." Remote Sens. 9, no. 12: 1245.
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