Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture

Abstract

Using the first full annual cycle of Cyclone Global Navigation Satellite System (CyGNSS) observations, we investigated the limitations and capabilities of CyGNSS observations for soil moisture (SM) estimates (0–5 cm). A relative signal‐to‐noise ratio (rSNR) value from a CyGNSS‐derived delay‐Doppler map is introduced to improve the temporal resolution of SM derived from Soil Moisture Active Passive (SMAP) data. We then evaluated the CyGNSS‐derived rSNR using ground‐based SM measurements and the triple collocation method with SMAP and modeled SM products. We found that CyGNSS can provide useful SM estimates over moderately vegetated regions (correlation coefficient of the individual data: 0.77) but shows degraded performance over arid and densely vegetated regions (correlation coefficient of the individual data: 0.68 and 0.67). However, when rSNR data is combined with SM data from SMAP, daily SM estimates can be achieved. These results show that synergistic use of CyGNSS observations can improve on SM estimates from current satellite systems.

Global Dynamics of Stored Precipitation Water in the Topsoil Layer From Satellite and Reanalysis Data

“…In future studies, calculation of F P ( f ) metrics from different soil moisture layers and consideration of observation‐based high sampling frequency soil moisture data (Kim & Lakshmi, ) and high spatial resolution of soil moisture products (Das et al, ) can further help elucidate the F P ( f ) metric and many geophysical applications. In addition, the investigation of longer data records of the stored precipitation fraction metric from satellite (Dorigo et al, ) and reanalysis data sets would also allow investigation of the impact of climate change on the water cycle.…”

“…However, with an irregular temporal sampling data set, the data set should be refined to include a fixed data sampling frequency before the F P ( f ) calculation is performed. For example, soil moisture retrieval satellite systems in Sun‐synchronous orbit can provide soil moisture estimates with relatively fixed data sampling frequency, while soil moisture retrievals from a non‐Sun‐synchronous orbit which is based on the signals of opportunity produce an irregular data sampling frequency (Kim & Lakshmi, ); this kind of irregular data should be refined to produce a fixed data sampling frequency.…”

“…Significant strides have been made in estimating surface soil moisture from space at global and regional scales with relatively high spatial and temporal resolution (Burgin et al, ; Karthikeyan et al, ; Kim & Lakshmi, ). However, as with land surface models, microwave satellite‐based soil moisture retrievals from different observation systems bear innate systemic and algorithmic limitations.…”

Lake Level and Surface Topography Measured With Spaceborne GNSS‐Reflectometry From CYGNSS Mission: Example for the Lake Qinghai

“…It consists of a constellation of eight microsatellites, whose primary objective is the measurement of near-surface wind speed over the ocean in and near the inner core of tropical cyclones (Ruf, Atlas, et al, 2016, Ruf, Gleason, & McKague, 2018. CYGNSS operates continuously over both ocean and land, and some other scientific utilities have been demonstrated with the CYGNSS observations , Kim & Lakshmi, 2018Ruf, Chew, et al, 2018). Moreover, the raw intermediate frequency samples of the direct and reflected signals (known as Level 0 or L0 data) have been also recorded occasionally for the exploration of other possible GNSS-R applications.…”

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

“…Thus, it was considered as the most effective method in remote sensing of SM [11,13]. In recent years, the surface-reflected Global Navigation Satellite System (GNSS) signals have also been evaluated for SM estimations, which applies a different source of signals from the active/passive microwave sensors to observe the Earth's surface [14]. Moreover, the Advanced Scatterometer (ASCAT), which is an active microwave remote sensing instrument, provides global SM data sets derived from the backscatter measurements [15,16].SM products obtained from active/passive microwave remotely-sensed data have been applied in wide spectra of contexts [17][18][19][20][21][22][23][24][25][26].…”