On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP

The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA’s Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active–passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (
$\sigma ^{0}$
) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between
$\sigma ^{0}$
(no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations (
$\sigma ^{0}_{\text {hh}}$
versus TB
_h
,
$\sigma ^{{0}}_{\text {hh}}$
versus TB
_v
,
$\sigma ^{{0}}_{\text {vv}}$
versus TB
_h
, and
$\sigma ^{{0}}_{\text {vv}}$
versus TB
_v
) with the
$\sigma ^{{0}}_{\text {vv}}$
and TB
_h
combination displaying an overall slightly higher correlation. The linear relationship between
$\sigma ^{{0}}$
and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active–passive joint algorithms for future satellite missions.