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

Abstract

Abstract. Profile soil moisture (SM) in mountainous areas are significant in water resources management and ecohydrological studies of downstream arid watersheds. Satellite products are useful in providing spatially distributed SM information, but only have limited penetration depth (e.g. top 5 cm). In contrast, in situ observations can provide multi-depth measurements, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using statistical methods, but this requires sufficient coupling strength among surface and subsurface SM. This study evaluates methods to calculate subsurface SM from surface SM and an application to the satellite SM product based on a SM observation network in the Qilian Mountains (China) established since 2013. First, we used cross-correlation to analyze the coupling strength among surface (0–10 cm) and subsurface (10–20, 20–30, 30–50, 50–70 cm, and profile of 0–70 cm) SM. Our results indicated an overall strong coupling among surface and subsurface SM in this study area. Afterwards, three different methods were tested to estimate subsurface SM from in-situ surface SM: the exponential filter (ExpF), artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods. The results showed that both ANN and ExpF methods were able to provide accurate estimates of subsurface soil moisture at 10–20 cm, 20–30 cm, and for the profile of 0–70 cm using surface (0–10 cm) soil moisture only. Specifically, the ANN method had the lowest estimation error (RSR) of 0.42, 0.62 and 0.49 for depths of 15 and 25 cm and profile SM, respectively, while the ExpF method best captured the temporal variation of subsurface soil moisture. Furthermore, it could be shown that the performance of the profile SM estimation was not significantly lower with using an area-generalized Topt (optimum T) compared to the station-specific Topt. In a final step, the ExpF method was applied to the satellite SM product (Soil Moisture Active Passive Level 3: SMAP_L3) to estimate profile SM, and the resulting profile SM was compared to in situ observations. The results showed that the ExpF method was able to estimate profile SM from SMAP_L3 surface products with reasonable accuracy (median R of 0.718). It was also found that the combination of ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in the mountainous areas in comparison to the SMAP_L4 root zone product. Overall, it was concluded that the ExpF method is able to estimate profile SM using SMAP surface products in the Qilian Mountains.

Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

“…ECH2O 5TE soil moisture probes (DECAGON Devices, USA) and EM50 datalogger were installed at each in situ observation site. The 5TE probe measures the soil volumetric water content by the dielectric constant of the soil, with a precision of ±3% and accuracy of ±1-2% [3,41]. The 5TE probes with a sensing depth of 2.5 cm were vertically centered in each of the sampled layers (0-10 cm, 10-20 cm, 20-30 cm, 30-50 cm and 50-70 cm) [42].…”

“…The system collected soil moisture content data at 30 min intervals. Regular site maintenance took place twice a year at the beginning of June and at the end of October [3].…”

“…Wireless data transmission is not possible because there is no mobile communication network coverage in the study area. As a result, there are some gaps in the measurement datasets due to severe weather and unstable batteries or sensors and the sensor damage by livestock and rats [3].…”

“…Soil moisture is an essential component of the terrestrial water cycle, and serves as a critical link between the precipitation, surface water, groundwater and vegetation water [1][2][3][4][5][6]. It plays an important role in hydrological processes and land surface-atmosphere interactions such as the Soil-Plant-Atmosphere Continuum (SPAC) [7].…”

“…Tobin et al [27] downscaled AMSR-E and ERS-CCI (European remote sensing satellite-climate change initiative) soil moisture data using an exponential filter (ExpF) with soil moisture index derived from MODIS NDVI. Tian et al [3] coupled the surface soil moisture and profile soil moisture, applied ExpF, artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods in an alpine region to estimate the deep profile soil moisture at the regional scale. Lu et al [28] investigated a nonlinear autoregressive neural network method with exogenous input (NARXnn) to estimate time series soil moisture by multiple remote sensing data.…”