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Recent Advances in Soil Moisture Estimation from Remote Sensing

1,2,* and 1,2
Department of Geography, Ludwig-Maximilians Universität München, Munich, 80333, Germany
Max Planck Institute for Meteorology, Hamburg, 20146, Germany
Author to whom correspondence should be addressed.
Water 2017, 9(7), 530;
Received: 9 June 2017 / Revised: 5 July 2017 / Accepted: 13 July 2017 / Published: 16 July 2017
(This article belongs to the Special Issue Remote Sensing of Soil Moisture)
PDF [184 KB, uploaded 16 July 2017]


Monitoring soil moisture dynamics from local to global scales is essential for a wide range of applications. The field of remote sensing of soil moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) were launched, and new missions, such as SENTINEL-1 provide long-term perspectives for land surface monitoring. This special issue aims to summarize the recent advances in soil moisture estimation from remote sensing, including recent advances in retrieval algorithms, validation, and applications of satellite-based soil moisture products. Contributions in this special issue exploit the estimation of soil moisture from both microwave remote sensing data and thermal infrared information. The validation of satellite soil moisture products can be very challenging, due to the different spatial scales of in situ measurements and satellite data. Some papers present validation studies to quantify soil moisture uncertainties. On the other hand, soil moisture downscaling schemes and new methods for soil moisture retrieval from GPS are also addressed by some contributions. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Several studies explore the use of soil moisture data for hydrological application such as runoff prediction. View Full-Text
Keywords: soil moisture; remote sensing; retrieval algorithms; uncertainties; validations; applications soil moisture; remote sensing; retrieval algorithms; uncertainties; validations; applications
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Peng, J.; Loew, A. Recent Advances in Soil Moisture Estimation from Remote Sensing. Water 2017, 9, 530.

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