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Remote Sens. 2018, 10(11), 1788;

Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate

Centre for Water Resource Systems, TU Wien, 1040 Vienna, Austria
Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
Institute for Land and Water Management Research, Federal Agency for Water Management Austria, 3252 Petzenkirchen, Austria
Author to whom correspondence should be addressed.
Received: 24 September 2018 / Revised: 30 October 2018 / Accepted: 7 November 2018 / Published: 11 November 2018
(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)
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Previous validation studies have demonstrated the accuracy of the Metop-A ASCAT soil moisture (SM) product, although over- and underestimation during different seasons of the year suggest a need for improving the retrieval algorithm. In this study, we analyzed whether adapting the vegetation characterization based on global parameters to regional conditions improves the seasonal representation of SM and vegetation optical depth ( τ ). SM and τ are retrieved from ASCAT using both a seasonal (mean climatological) and a dynamic vegetation characterization that allows for year-to-year changes. The retrieved SM and τ are compared with in situ and satellite SM, and with vegetation products (SMAP, AMSR2, and SPOT-VGT/PROBA-V). The study region is set in an agricultural area of Lower Austria that is characterized by heterogeneous land cover and topography, and features an experimental catchment equipped with a SM network (HOAL SoilNet). We found that a stronger vegetation correction within the SM retrieval improves the SM product considerably (increase of the Spearman correlation coefficient r s by 0.15 on average, and r s comparable to SMAP and AMSR2). The vegetation product derived with a dynamic vegetation characterization compares well to the reference datasets and reflects vegetation dynamics such as start and peak of season and harvest. Although some vegetation effects cannot be corrected by the adapted vegetation characterization, our results demonstrate the benefits of a parameterization optimized for regional conditions in this temperate climate zone. View Full-Text
Keywords: microwave remote sensing; advanced scatterometer (ASCAT); soil moisture; vegetation optical depth microwave remote sensing; advanced scatterometer (ASCAT); soil moisture; vegetation optical depth

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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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Pfeil, I.; Vreugdenhil, M.; Hahn, S.; Wagner, W.; Strauss, P.; Blöschl, G. Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate. Remote Sens. 2018, 10, 1788.

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