Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data

By M. Piles, Adriano Camps, M. Vall-llossera, I. Corbella, R. Panciera, C. Rüdiger, Yann H. Kerr, Jeffrey P. Walker. IEEE Transactions on Geoscience and Remote Sensing, 2011

392 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/8241c36b362b6cb972c72cf0ed5d57fcfba9483d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. M. Ramirez
2. Dawei Han
3. Prashant Kumar Srivastava
4. Tanvir Islam

In Environmental Engineering

1. M. Ramirez
2. Dawei Han
3. Prashant Kumar Srivastava
4. Tanvir Islam

Citations in the corpus

• Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application (2013, background): “Overall findings suggest that AI techniques explicitly doing far better performance as comparison to the performance as obtained by other workers using simple linking models (Piles et al. 2011).”