An Analysis of a Commercial GNSS-R Soil Moisture Dataset

被引：3

作者：

Al-Khaldi, Mohammad M. ^{[1
,2
]}

Johnson, Joel T. ^{[1
,2
]}

Horton, Dustin ^{[1
,2
]}

McKague, Darren S. ^{[3
]}

Twigg, Dorina ^{[4
]}

Russel, Anthony ^{[4
]}

Policelli, Frederick S. ^{[5
]}

Ouellette, Jeffrey D. ^{[6
]}

Bindlish, Rajat ^{[5
]}

Park, Jeonghwan ^{[5
]}

机构：

[1] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA

[2] Ohio State Univ, ElectroSci Lab, Columbus, OH 43210 USA

[3] Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA

[4] Univ Michigan, Space Phys Res Lab, Ann Arbor, MI 48109 USA

[5] NASA Goddard Space Flight Ctr, Greenbelt, MD 20771 USA

[6] US Naval Res Lab, Washington, DC 20375 USA

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2024年 / 17卷

关键词：

Soil moisture; Surface roughness; Rough surfaces; Receivers; Reflectivity; Scattering; Surface treatment; Bistatic radar systems; CubeSats; global navigation satellite systems reflectometry (GNSS-R); rough surface scattering; SmallSats; soil moisture; SIGNALS; PREDICTABILITY; REFLECTIONS; SCATTERING; DYNAMICS; SYSTEM; OCEAN; SMOS;

D O I：

10.1109/JSTARS.2024.3449773

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

An analysis of a Level-2 (L2) soil moisture record extending from 1 May 2021 to 1 January 2024 derived from Spire, Inc.'s Global Navigation Satellite System Reflectometry (GNSS-R) observatories is presented. The product's sensitivity to large scale soil moisture variability is demonstrated using an example of a 2022 flood in Pakistan. Product consistency among the constellation's multiple satellites is also investigated; no clear evidence of intersatellite biases is observed. Further comparisons are performed with soil moisture datasets from the Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) missions, from the European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), and from in situ International Soil Moisture Network (ISMN) sites. Although an overall product correlation with SMAP soil moisture of approximately 85$\%$ is determined, per-pixel correlations vary significantly and per-pixel root-mean-square errors (RMSE) can range from 0.02 to 0.09 (cm(3)/cm(3)) depending on land class. The importance of applying the product's quality flags is also demonstrated. The influence of other calibration effects and inland water body contamination on these results is also discussed.

引用

页码：15480 / 15493

页数：14

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