Abstract
Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.
Highlights
Water availability and thereby food security, human health and ecosystem function are directly affected by surface soil moisture [1]
We extend the work from Leroux et al [69] to the global scale and estimate the pixel-wise statistical relationship between SMAP Enhanced (SMAP_E) and Soil Moisture and Ocean Salinity (SMOS) brightness temperatures, which we approximate with a set of bivariate copula-functions
We focus on the bivariate case, i.e., f (x, y) = c ( fX (x), fY (y)) · fX (x) · fY (y) where x and y are the simultaneous SMOS and SMAP_E Tb records within a time window of six hours during the period April 2015–March 2018 in a single 9 km pixel
Summary
Water availability and thereby food security, human health and ecosystem function are directly affected by surface soil moisture [1]. Soil moisture is an important storage memory component for precipitation and radiation anomalies, inducing persistence in the climate system It is involved in various feedback mechanisms at the local, the regional and the global scales [3]. Long time series of essential variables, like precipitation, temperature, soil freeze/thaw, snow cover or snow water equivalent, are requested, which typically span over several decades [4,5]. These long records are necessary in order to reveal and separate the slowly changing climate signal from short-term variations. The European Space Agency (ESA) identified soil moisture to be such an essential climate variable and developed within its Climate Change Initiative (CCI) a long-term global satellite-based soil moisture data record [6,7]
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