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Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering
Backscatter measured by scatterometers and Synthetic Aperture Radars is sensitive to the dielectric properties of the soil and normally increases with increasing soil moisture content. However, when the soil is dry, the radar waves penetrate deeper into the soil, potentially sensing subsurface scatterers such as near-surface rocks and stones. In this paper we propose an exponential model to describe the impact of such subsurface scatterers on C-Band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on board of the METOP satellites. The model predicts an increase of the subsurface scattering contributions with decreasing soil wetness that may counteract the signal from the soil surface. This may cause anomalous backscatter signals that deteriorate soil moisture retrievals from ASCAT. We test whether this new model is able to explain ASCAT observations better than a bare soil backscatter model without a subsurface scattering term, using k-fold cross validation and the Bayesian Information Criterion for model selection. We find that arid landscapes with Leptosols and Arenosols represent ideal environmental conditions for the occurrence of subsurface scattering. Nonetheless, subsurface scattering may also become important in more humid environments during dry spells. We conclude that subsurface scattering is a widespread phenomenon that (i) needs to be accounted for in active microwave soil moisture retrievals and (ii) has a potential for soil mapping, particularly in arid and semi-arid environments
Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data
Effective agricultural water management requires accurate and timely
information on the availability and use of irrigation water. However, most
existing information on irrigation water use (IWU) lacks the
objectivity and spatiotemporal representativeness needed for operational
water management and meaningful characterization of land–climate
interactions. Although optical remote sensing has been used to map the area
affected by irrigation, it does not physically allow for the estimation of
the actual amount of irrigation water applied. On the other hand, microwave
observations of the moisture content in the top soil layer are directly
influenced by agricultural irrigation practices and thus potentially allow
for the quantitative estimation of IWU. In this study, we combine surface
soil moisture (SM) retrievals from the spaceborne SMAP, AMSR2 and
ASCAT microwave sensors with modeled soil moisture from MERRA-2 reanalysis to
derive monthly IWU dynamics over the contiguous United States (CONUS) for the
period 2013–2016. The methodology is driven by the assumption that the
hydrology formulation of the MERRA-2 model does not account for irrigation,
while the remotely sensed soil moisture retrievals do contain an irrigation
signal. For many CONUS irrigation hot spots, the estimated spatial irrigation
patterns show good agreement with a reference data set on irrigated areas.
Moreover, in intensively irrigated areas, the temporal dynamics of observed
IWU is meaningful with respect to ancillary data on local irrigation
practices. State-aggregated mean IWU volumes derived from the combination of
SMAP and MERRA-2 soil moisture show a good correlation with statistically
reported state-level irrigation water withdrawals (IWW) but systematically
underestimate them. We argue that this discrepancy can be mainly attributed
to the coarse spatial resolution of the employed satellite soil moisture
retrievals, which fails to resolve local irrigation practices. Consequently,
higher-resolution soil moisture data are needed to further enhance the
accuracy of IWU mapping.</p