Dissemin is shutting down on January 1st, 2025

Published in

Elsevier, Remote Sensing of Environment, 10(114), p. 2317-2325

DOI: 10.1016/j.rse.2010.05.008

Links

Tools

Export citation

Search in Google Scholar

Soil moisture variations monitoring by AMSU-based soil wetness indices: A long-term inter-comparison with ground measurements

Journal article published in 2010 by T. Lacava, L. Brocca ORCID, G. Calice, F. Melone, T. Moramarco, N. Pergola, V. Tramutoli ORCID
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Red circle
Postprint: archiving forbidden
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Soil moisture controls the partitioning of rainfall into runoff and infiltration and, consequently, the runoff generation. On the catchment scale its routine monitoring can be performed through remote sensing technologies. Within this framework, the purpose of this study is to investigate the potential of the Advanced Microwave Sounding Unit (AMSU), radiometer on board the NOAA (National Oceanic and Atmospheric Administration) satellites and operating since 1998, for the assessment of soil wetness conditions by comparing soil moisture data with both those measured in situ and provided by a continuous rainfall-runoff model applied to four catchments located in the Upper Tiber River (Central Italy). In particular, in order to perform a robust analysis an extensive and long-term period (nine years) of data was investigated. In detail, the Soil Wetness Variation Index, derived from the AMSU data modified in order to take account of the difference between the soil layer investigated by the satellite sensor and that used as a benchmark, was found to be correlated both with the in-situ and modeled soil moisture variations showing correlation coefficients in the range of 0.42-0.49 and 0.33-0.48, respectively. As far as the soil moisture temporal pattern is concerned, higher correlations were obtained (0.59-0.84 for the in-situ data and 0.82-0.87 for the modeled data set) partly due to the soil moisture seasonal pattem that enhances the correlation. Overall, the root mean square error was found to be less than 0.05 m(3)/m(3) for both the comparisons, thus assessing the potential of the AMSU sensor to quantitatively retrieve soil moisture temporal patterns. Moreover. the AMSU sensor can be considered as a useful tool to provide a reliable and frequently updated global soil moisture data set, considering its higher temporal resolution now available (about 4 passes per day) thanks to the presence of the sensor aboard different satellites.