Downscaling Landsat 7 canopy reflectance employing a multi-soil sensor platform

Downscaling Landsat 7 canopy reflectance employing a multi-soil sensor platform Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is too coarse to fully understand the spatial dynamics of crop status. The objective of this study was to downscale Landsat 7 (L7) reflectance from the native resolution of 30 × 30 m to that typical of yield maps (ca. 5 × 5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat (Triticum aestivum L.) in the 2002–2003 growing season. Geospatial yield measurements were available (1 per ca. 20 m2). Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data were acquired (resolution <5 × 5 m) to characterize soil spatial variability. Geographically-weighted regressions were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30 × 30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 < R2 < 0.94). Maps for regression parameters and intercept were obtained at 30 × 30 m and used to estimate the L7 reflectance at 5 × 5 m resolution. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5 × 5 m estimated reflectance showed stronger correlations (average increase in explained variance = 3.2 %) with yield than at the 30 × 30 m resolution. Land resource managers, producers, agriculture consultants, extension specialists and Natural Resource Conservation Service field staff would be the beneficiaries of downscaled L7 reflectance data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Downscaling Landsat 7 canopy reflectance employing a multi-soil sensor platform

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York (outside the USA)
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-015-9406-9
Publisher site
See Article on Publisher Site

Abstract

Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is too coarse to fully understand the spatial dynamics of crop status. The objective of this study was to downscale Landsat 7 (L7) reflectance from the native resolution of 30 × 30 m to that typical of yield maps (ca. 5 × 5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat (Triticum aestivum L.) in the 2002–2003 growing season. Geospatial yield measurements were available (1 per ca. 20 m2). Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data were acquired (resolution <5 × 5 m) to characterize soil spatial variability. Geographically-weighted regressions were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30 × 30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 < R2 < 0.94). Maps for regression parameters and intercept were obtained at 30 × 30 m and used to estimate the L7 reflectance at 5 × 5 m resolution. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5 × 5 m estimated reflectance showed stronger correlations (average increase in explained variance = 3.2 %) with yield than at the 30 × 30 m resolution. Land resource managers, producers, agriculture consultants, extension specialists and Natural Resource Conservation Service field staff would be the beneficiaries of downscaled L7 reflectance data.

Journal

Precision AgricultureSpringer Journals

Published: Jun 30, 2015

References

  • On-the-go soil sensors for precision agriculture
    Adamchuk, VI; Hummel, J; Morgan, M; Upadhyaya, S
  • Remedial correction of yield map data
    Blackmore, S

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