Hydrological evaluation of satellite soil moisture data in two basins of different climate and vegetation density conditions

Zhuo, Lu and Han, Dawei 2017. Hydrological evaluation of satellite soil moisture data in two basins of different climate and vegetation density conditions. Advances in Meteorology 2017 , pp. 1-15. 10.1155/2017/1086456

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Abstract

Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Earth and Environmental Sciences
Additional Information:	Copyright © 2017 Lu Zhuo and Dawei Han. 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.
Publisher:	Hindawi
ISSN:	1687-9309
Date of First Compliant Deposit:	26 October 2022
Date of Acceptance:	6 December 2016
Last Modified:	24 May 2023 14:12
URI:	https://orca.cardiff.ac.uk/id/eprint/153245

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