Analysis of in-situ soil moisture data and validation of SMOS soil moisture products at selected agricultural sites over a tropical region

Chuen Siang Kang, Kasturi Devi Kanniah (Lead / Corresponding author), Yann H. Kerr, Arthur Philip Cracknell

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Calibration and validation activities on Soil Moisture and Ocean Salinity (SMOS)-derived soil moisture products have been conducted worldwide since the data became available, but this has not been the case over tropical regions. This study focuses on the setting up of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia and on the validation of SMOS soil moisture products. The in-situ data over a one-and-a-half-year period was analysed and the validation of the SMOS soil moisture products with this in-situ data was conducted. Bias and root mean square error (RMSE) were computed between the SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing times (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with a vegetation water content higher than 5 kg m−2, an overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias ranged from 0.064 to 0.119 m3 m−3 and the RMSE was from 0.090 to 0.158 m3 m−3, when both ascending and descending mode data were measured. This RMSE was found to be similar to those of a number of studies conducted previously at different regions. However, a wet bias was found during the validation, while previous validation activities at other locations showed dry biases. The result of this study is useful to support the continuous development and improvement of the SMOS soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially in tropical regions.

Original languageEnglish
Pages (from-to)3636-3654
Number of pages19
JournalInternational Journal of Remote Sensing
Volume37
Issue number16
Early online date13 Jul 2016
DOIs
Publication statusPublished - 17 Aug 2016

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