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Remote Sens. 2009, 1(4), 1338-1352; doi:10.3390/rs1041338
Article
“Group Inversion Approach” for Detection of Soil Moisture Temporal-Invariant Locations
EURAC-Institute for Applied Remote Sensing, Viale Druso 1, 39100 Bolzano, Italy
Received: 26 October 2009; in revised form: 9 December 2009 / Accepted: 14 December 2009 / Published: 21 December 2009
(This article belongs to the Special Issue Microwave Remote Sensing)
Abstract: This paper presents an approach denominated Group Inversion Approach (GIA) which aims at detecting soil moisture temporal invariants, i.e., the stable temporal soil moisture locations, by using mainly remotely sensed data. The soil moisture temporal invariants are those locations where independently of the absolute value changes, the relative spatial distribution of soil moisture remains almost constant. In this procedure, the soil moisture values estimated from different inversion approaches and sensor configurations are compared among themselves and with the ground data. The procedure has been tested in a watershed of around 7,000 km2 with data collected during the SMEX’02 experiment in Iowa (USA). The results indicate that fields with inversion errors lower than five times the soil moisture variability detected with ground measurements represent well the mean watershed soil moisture values. The GIA technique has been also found in good agreement with the classical technique used to detect the stable soil moisture features, based exclusively on ground measurements.
Keywords: soil moisture; radar data; ground measurements; inversion approach; temporal stability
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MDPI and ACS Style
Notarnicola, C. “Group Inversion Approach” for Detection of Soil Moisture Temporal-Invariant Locations. Remote Sens. 2009, 1, 1338-1352.
AMA StyleNotarnicola C. “Group Inversion Approach” for Detection of Soil Moisture Temporal-Invariant Locations. Remote Sensing. 2009; 1(4):1338-1352.
Chicago/Turabian StyleNotarnicola, Claudia. 2009. "“Group Inversion Approach” for Detection of Soil Moisture Temporal-Invariant Locations." Remote Sens. 1, no. 4: 1338-1352.
Remote Sens.
EISSN 2072-4292
Published by MDPI AG, Basel, Switzerland
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