Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

@article{Ke2016DownscalingOM,
  title={Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches},
  author={Yinghai Ke and Jungho Im and Seonyoung Park and Huili Gong},
  journal={Remote Sensing},
  year={2016},
  volume={8},
  pages={215}
}
This study presented a MODIS 8-day 1 km evapotranspiration (ET) downscaling method based on Landsat 8 data (30 m) and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST), and vegetation indices (VIs) derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR), Cubist, and Random Forest (RF) were used to model the relationship between the Landsat indicators and MODIS 8-day 1… CONTINUE READING
9 Citations
83 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-9 of 9 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 83 references

Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in situ observations

  • Y. Ke, J. Im, J. Lee, H. Gong, Y. Ryu
  • Remote Sens. Environ
  • 2015
Highly Influential
5 Excerpts

Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe

  • G. Hu, L. Jia, M. Menenti
  • Remote Sens. Environ
  • 2011
Highly Influential
4 Excerpts

Improvements to a MODIS global terrestrial evapotranspiration algorithm

  • Q. Mu, M. Zhao, S. W. Running
  • Remote Sens. Environ
  • 2011
Highly Influential
5 Excerpts

MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) Collection 5

  • Q. Mu, M. Zhao, S. W. Algorithm Theoretical Basis Document Running
  • 2013; NASA Headquarters. Available online: http…
  • 2015
Highly Influential
3 Excerpts

Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion

  • C. Cammalleri, M. C. Anderson, F. Gao, C. R. Hain, W. P. Kustas
  • Agr. Forest Meteorol
  • 2014
Highly Influential
4 Excerpts

Expansion of LISSIII swath using AWiFS wider swath data and contourlet coefficients learning. GISci

  • C. Rao, M. Rao, A. Kumar, B. Lakshmi, V. Dadhwal
  • Remote Sens. 2015,
  • 2016

Similar Papers

Loading similar papers…