Joint Sentinel‐1 and SMAP data assimilation to improve soil moisture estimates

Abstract

SMAP (Soil Moisture Active and Passive) radiometer observations at ∼40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9 km SMAP Level‐4 Soil Moisture product. This study demonstrates that adding high‐resolution radar observations from Sentinel‐1 to the SMAP assimilation can increase the spatiotemporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3‐hourly, 9 km surface and root‐zone soil moisture simulations with in situ measurements from 9 km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel‐1 assimilation consistently improved surface soil moisture, whereas root‐zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel‐1 observations performed best, demonstrating the complementary value of radar and radiometer observations.

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

“…2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques.…”

Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model

“…The recent Soil Moisture Active Passive (SMAP) satellite provides global surface SM with generally low errors across different climate regions (Kumar et al, 2018). Numerous studies have evaluated SMAP data with ground-based observations (Chan et al, 2016;Pan et al, 2016) and used SMAP data to improve hydrological and carbon flux simulations (Alvarez-Garreton et al, 2016;He et al, 2017;Kumar et al, 2015;Lievens et al, 2017Lievens et al, , 2015. Recently, Lawston, Santanello, and Kumar (2017) demonstrated that SMAP data can be used to detect seasonal timing and spatial signature of irrigation.…”

An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

“…the Advanced SCATterometer, ASCAT; the Soil Moisture and Ocean Salinity, SMOS; and Soil Moisture Active Passive, SMAP). Recent analyses were carried out by assimilating S1 SAR and SMAP radiometer observations within the NASA Catchment Land Surface Model to improve the accuracy of SM estimates (Lievens et al, 2017) and by exploiting a S1-based SM map as input data for an event-based rainfall-runoff model for improving its hydrological simulation (Alexakis et al, 2017). However, to our knowledge, there is a lack of studies dealing with the assimilation of S1-derived SM products within hydrological modelling to improve continuous streamflow simulations and flash flood predictions (preliminary analyses -preparatory to this study -can be found in Cenci, 2016, Cenci et al, 2016b, c, and Cenci et al, 2017a.…”