Initial results of China’s GNSS-R airborne campaign: soil moisture retrievals

Wan, Wei; Bai, Weihua; Zhao, Limin; Long, Di; Sun, Yueqiang; Meng, Xiang‐Gao; Chen, Hua; Cui, Xiai; Hong, Yang

doi:10.1007/s11434-015-0789-9

Cited by 12 publications

(5 citation statements)

References 30 publications

(42 reference statements)

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“…The variation in the dielectric constant results in an alteration of the reflectance of the received surface reflected signal. In addition, changes in vegetation cover and roughness on the surface will affect or complicate the forward process of soil moisture (Wan et al, 2015;Wu et al, 2021a). Therefore, the simplest foundation experiments are often carried out on relatively flat and open bare ground.…”

Section: Soil Moisture Retrievalmentioning

confidence: 99%

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Jin,

Camps,

Jia

et al. 2024

Satell Navig

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The Global Navigation Satellite Systems (GNSS), including the US’s GPS, China’s BDS, the European Union’s Galileo, and Russia’s GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth’s surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring.

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Section: Soil Moisture Retrievalmentioning

confidence: 99%

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Jin,

Camps,

Jia

et al. 2024

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“…Since the flight area includes a crop land and a part of the Yellow River, we subsequently separate the data analysis into two parts: soil moisture retrieval and the Yellow River remote sensing. The soil moisture retrieval and analysis have been presented by a previous paper, as part I [23]. The initial results of the Yellow River remote sensing are illustrated in this paper as part II.…”

Section: Introductionmentioning

confidence: 98%

A first comprehensive evaluation of China’s GNSS-R airborne campaign: part II—river remote sensing

et al. 2015

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“…Table 1. Summary of the current empirical and semi-empirical spaceborne GNSS-R SM retrieval methods (reorganized from Lei et al, 2022) (Masters et al, 2004;Katzberg et al, 2006;Alonso-Arroyo et al, 2014;Wan et al, 2015). To the best of our knowledge, only a few studies explore the capability of the physics-based framework on spaceborne GNSS-R (Calabia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Concept and initial realization of PHYSER — A PHYsics-based framework for SpacebornE GNSS-R soil moisture retrieval with accurate soil reflectivity

Yang,

Guo,

et al. 2024

Preprint

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Global soil moisture (SM) observation using the spaceborne Global Navigation Satellite Reflectometry (GNSS-R) is becoming an effective supplement and enhancement to traditional microwave remote sensing observations. The state-of-the-art SM retrieval frameworks for spaceborne GNSS-R are based on empirical or semi-empirical modeling, which relies on reference SM data from other sources (e.g., microwave radiometer or in situ SM products) to eliminate the effects of land surface random errors (e.g., surface roughness and vegetation). This study defines a generic framework for PHYsics-based SpacebornE GNSS-R SM retrieval, namely PHYSER, and proposes initial strategies to realize the framework. The framework concept devotes to deriving accurate soil reflectivity and retrieving SM by estimating soil permittivity from Fresnel reflection coefficients, thus wholly independent of external SM products. It assumes that GNSS-R surface reflectivity and its related soil reflectivity are affected by observing system errors and land surface random errors. The framework is initially realized by deriving accurate soil reflectivity from empirical corrections to avoid the grand challenge of building a forward scattering model under complex land surface conditions. Accurate soil reflectivity is derived through two steps: 1) Surface Reflectivity CALibrating (SuR-CAL), aiming to calibrate the system errors using the reflectivity of inland water bodies, and 2) Soil Reflectivity CORrecting (SoR-COR), aiming to correct the random errors mainly from surface roughness and vegetation using the zeroth-order radiative transfer (τ–ω) model. The framework is validated using one-year data from BuFeng-1 A/B (BF-1) twin satellites. The findings and conclusions mainly include: 1) PHYSER reveals that independent spaceborne GNSS-R SM retrieval without reference SM products is achievable through deriving accurate soil reflectivity. 2) Land surface random errors play a more significant role in influencing soil reflectivity than system errors. The SuR-CAL and SoR-COR steps improve the correlation coefficient (R) between BF-1 reflectivity and the SMAP SM up to ~ 7% and ~ 36%, respectively. 3) The BF-1 SM estimates agree well with the SMAP SM and ERA5 SM (ubRMSD = 0.067 m3m− 3 and MAE = 0.073 m3m− 3 against SMAP; ubRMSD = 0.079 m3m− 3 and MAE = 0.088 m3m− 3 against ERA5). The BF-1 SM also agrees well with the in-situ measurements with mean ubRMSE = 0.055 m3m− 3 and MAE = 0.066 m3m− 3. The proposed framework provides a promising physics-based concept to independently retrieve SM for the GNSS-R community, which is expected to considerably support the in-orbit and next-generation GNSS-R missions to promote operational SM retrieval and applications.

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Initial results of China’s GNSS-R airborne campaign: soil moisture retrievals

Cited by 12 publications

References 30 publications

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Remote sensing and its applications using GNSS reflected signals: advances and prospects

A first comprehensive evaluation of China’s GNSS-R airborne campaign: part II—river remote sensing

Concept and initial realization of PHYSER — A PHYsics-based framework for SpacebornE GNSS-R soil moisture retrieval with accurate soil reflectivity

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