In:
Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 22, No. 8 ( 2018-08-22), p. 4473-4489
Abstract:
Abstract. Remotely sensed soil moisture products are influenced by vegetation and how
it is accounted for in the retrieval, which is a potential source of
time-variable biases. To estimate such complex, time-variable error
structures from noisy data, we introduce a Bayesian extension to triple
collocation in which the systematic errors and noise terms are not constant
but vary with explanatory variables. We apply the technique to the
Soil Moisture Active Passive (SMAP) soil moisture product over croplands,
hypothesizing that errors in the vegetation correction during the retrieval
leave a characteristic fingerprint in the soil moisture time series. We find
that time-variable offsets and sensitivities are commonly associated with an
imperfect vegetation correction. Especially the changes in sensitivity can be
large, with seasonal variations of up to 40 %. Variations of this size
impede the seasonal comparison of soil moisture dynamics and the detection of
extreme events. Also, estimates of vegetation–hydrology coupling can be
distorted, as the SMAP soil moisture has larger R2 values with a biomass
proxy than the in situ data, whereas noise alone would induce the
opposite effect. This observation highlights that time-variable biases can
easily give rise to distorted results and misleading interpretations. They
should hence be accounted for in observational and modelling studies.
Type of Medium:
Online Resource
ISSN:
1607-7938
DOI:
10.5194/hess-22-4473-2018
DOI:
10.5194/hess-22-4473-2018-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2018
detail.hit.zdb_id:
2100610-6
