Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites

Abstract

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m 3 /m 3 ubRMSE, −0.015 m 3 /m 3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m 3 /m 3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation. Kim et al.

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

“…The objective of TxSON was to establish a spatially representative measure of SWC for the calibration and validation of remotely sensed estimates (Chan et al, 2016, 2018; Colliander et al, 2017b, 2018; Kim et al, 2017; Ouellette et al, 2017; Bindlish et al, 2018; Das et al, 2018), upscaling exercises (Clewley et al, 2017), and data assimilation and land surface model validation (Kolassa et al, 2017, 2018; Reichle et al, 2017). Much of the past work has focused on SMAP products that are posted to the Equal‐Area Scalable Earth Version 2 (EASE2) grid.…”

Consistency Between NASS Surveyed Soil Moisture Conditions and SMAP Soil Moisture Observations

“…The NASA SMAP mission was launched in late January 2015 (Entekhabi et al, ). SM products are available from the start of science measurements on 31 March 2015 to the present with a latency between a few hours for near‐real‐time Level‐2 retrievals and ~2.5 days for the L4SM product (Chan et al, , ; Das et al, ; Kim et al, ; Reichle, de Lannoy, Liu, Ardizzone, et al, ). The mission carries L‐band radiometer and radar instruments.…”

“…Water Resources Research hours for near-real-time Level-2 retrievals and~2.5 days for the L4SM product (Chan et al, 2016(Chan et al, , 2018Das et al, 2018;Kim et al, 2017;Reichle, de Lannoy, Liu, Ardizzone, et al, 2017). The mission carries L-band radiometer and radar instruments.…”

Estimating Regional Scale Hydroclimatic Risk Conditions from the Soil Moisture Active-Passive (SMAP) Satellite

“…Results from the descending (morning) pass only were used since these represent higher quality retrievals due to greater uniformity in surface soil temperature and reduced impacts of Faraday rotation [31]. The Active-Passive soil moisture combines soil moisture retrieved using the passive sensor, with soil moisture retrieved using the active microwave sensor located on the SMAP platform [32]. As these retrievals have higher noise levels, the soil moisture is retrieved at a 3 km grid spacing and combined with the retrieved soil moisture from the passive sensor to create a downscaled 9 km soil moisture data set (SMAP_AP).…”