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Remote Sens. 2015, 7(4), 3783-3807; doi:10.3390/rs70403783

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

1
Faculté des Sciences Semlalia Marrakech (FSSM), Avenue Prince Moulay Abdellah, BP 2390,Marrakech 40000, Morocco
2
Centre d'Etudes Spatiales de la Biosphère (CESBIO), 18 Avenue, Edouard Belin, bpi 2801, Toulouse 31401, France
3
Faculté des Sciences et Techniques (FST), Avenue Abdelkarim Khettabi, BP 549, Marrakech 40000, Morocco
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabaill
Received: 30 September 2014 / Revised: 13 March 2015 / Accepted: 17 March 2015 / Published: 30 March 2015
View Full-Text   |   Download PDF [3120 KB, uploaded 30 March 2015]   |  

Abstract

Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named GDOWN, is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2–3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between GDOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good indicator of the overall performance of DISPATCH. Especially, the sign of GDOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The GDOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables. View Full-Text
Keywords: downscaling; validation; metric; soil moisture; disaggregation based on physical and theoretical scale change (DISPATCH) data downscaling; validation; metric; soil moisture; disaggregation based on physical and theoretical scale change (DISPATCH) data
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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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MDPI and ACS Style

Merlin, O.; Malbéteau, Y.; Notfi, Y.; Bacon, S.; Khabba, S.E.-R.; Jarlan, L. Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco. Remote Sens. 2015, 7, 3783-3807.

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