Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Abstract

Abstract:As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature T Bp and effective temperature T eff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m 3 /m 3 in mountainous area. These uncertainties also result in the "dry bias" of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m 3 /m 3 and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient b p result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature T Bp and effective temperature T eff ; determine coefficient b p and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future.

Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

“…Results showed that the MBE varied from -0.23 to 0.07 cm 3 /cm 3 with median of -0.021 cm 3 /cm 3 . This indicates that SMAP underestimated surface SM over the study region, which is consistent with previous studies in the area (Chen et al, 2017;Zhang et al, 2017b). And RMSE varied within 0.026-0.250 cm 3 /cm 3 between sites with a median value of 0.052 cm 3 /cm 3 .…”

“…Figs 10 and S2 show that the performance was low at two stations (D13 with R of 0.18, D15 with R of 0.08) with scrubland and relatively high soil moisture. The poor performance at scrubland sites persistent with Zhang et al (2017b)'s researches in this study region. Results showed that the MBE varied from -0.23 to 0.07 cm 3 /cm 3 with median of -0.021 cm 3 /cm 3 .…”

“…At each station, SM profiles from 0 to 70 cm were measured by soil moisture probes (ECH2O 5TE, METER Group Inc., USA) installed at depths of 5 (representing depth of 0-10 cm, SM5 cm), 15 (10-20 cm, SM15 cm), 25 (20-30 cm, SM25 cm), 40 (30-50 cm, SM40 cm) and 60 cm (50-70 cm, SM60 cm) below the soil surface at 30 min intervals. Soil-specific sensor calibrations were performed with the direct calibration method (Cobos and Chambers, 2010;Zhang et al, 2017b) using soil samples taken from each station. The profile integrated SM (SM0-70 cm) was calculated by the method of (González-Zamora et al, 2016):…”

Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

“…Previous studies have shown that subsurface SM is often related to surface and near-surface SM (Mahmood and Hubbard, 2007;Wang et al, 2017). A variety of methods for estimating subsurface SM from surface SM information have been developed, including data assimilation of remote sensing data into land surface models (Han et al, 2013), physically based methods (Manfreda et al, 2014), (semi-)empirical methods (Albergel et al, 2008), data-driven methods (Kornelsen and Coulibaly, 2014;Zhang et al, 2017a), and statistical methods (Gao et al, 2019). Among them, the application of both data assimilation and physically based methods are limited to data-rich areas due to the large amount of required input data, e.g., soil properties, which are often not available for data-scarce mountainous areas (Jin et al, 2015;Li et al, 2017;Dai et al, 2019).…”

Cloud Resolving WRF Simulations of Precipitation and Soil Moisture Over the Central Tibetan Plateau: An Assessment of Various Physics Options

“…Previous studies found that both SMAP L3 and L4 products can capture the temporal variation of in situ observations in the upper reach of Heihe River Watershed (Zhang et al, ). Reichle et al () indicated that the SMAP L4 performs well on the Ngari soil moisture observation site, with an unbiased root mean square error of 0.04, a bias of 0.01, and a correlation coefficient of 0.77.…”