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.
Highlights
Since the advent of spaceborne microwave sensors in the late 1970s, various large-scale surface soil moisture products have been derived from C- and/or X-band data collected by the Scanning MultichannelMicrowave Radiometer (SMMR) [1], followed by the Special Sensor Microwave/Imager (SSM/I) [2], Advanced Microwave Instrument (AMI) [3], Advanced Microwave Scanning Radiometer (AMSR) [4]and Advanced Scatterometer (ASCAT) [5], among others
The three performance metrics RMSDHR, GRMSD and GDOWN are assessed from an ensemble of DISPATCH and in situ soil moisture datasets
For annual crop sites (Beet’12, Wheat’12, Wheat’13 North and Wheat’13 South), a strong negative bias is visible in both non-disaggregated and disaggregated data, indicating that the soil moisture variability occurs at a scale significantly higher than the DISPATCH 1-km resolution
Summary
Since the advent of spaceborne microwave sensors in the late 1970s, various large-scale surface soil moisture products have been derived from C- and/or X-band data collected by the Scanning MultichannelMicrowave Radiometer (SMMR) [1], followed by the Special Sensor Microwave/Imager (SSM/I) [2], Advanced Microwave Instrument (AMI) [3], Advanced Microwave Scanning Radiometer (AMSR) [4]and Advanced Scatterometer (ASCAT) [5], among others. The forthcoming Soil Moisture Active Passive (SMAP) [7] mission is scheduled for launch in early 2015. It will ensure the continuity of L-band microwave data for global soil moisture monitoring. The current spatial resolution of microwave radiometers and scatterometers is still lower than 40 km, which is very coarse for most hydrological and agricultural applications. In this context, a number of downscaling strategies of the surface soil moisture derived from microwave data have been imagined
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