Monitoring Freeze-Thaw State by Means of GNSS Reflectometry: An Analysis of TechDemoSat-1 Data

Comite, Davide; Cenci, Luca; Colliander, Andreas; Pierdicca, Nazzareno

doi:10.1109/jstars.2020.2986859

Cited by 37 publications

(22 citation statements)

References 46 publications

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“…The exploitation of this approach has been particularly accelerated with the availability of large and diverse datasets acquired from recent spaceborne GNSS-R observatories such as the United Kingdom's TechDemoSat-1 (TDS-1, launched in mid-2014 and retired in mid-2019) [12] and NASA's Cyclone Global Navigation Satellite System (CYGNSS, launched in late 2016) [13]. While TDS-1's GNSS-R measurements featured a relatively low revisit time due to the satellites intention for evaluating multiple payloads simultaneously, the dedicated research community surrounding TDS-1 has greatly improved the literature's understanding of spaceborne GNSS-R responses to ocean winds [14,15], ice sheets [16,17], and land geophysical parameter features [18][19][20] from space due to TDS-1's global coverage created by its polar orbiting configuration. On the other hand, CYGNSS features a high-temporal resolution over a smaller spatial extent in order to optimize its measurements for ocean studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

et al. 2020

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This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38 latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

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Section: Introductionmentioning

confidence: 99%

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

et al. 2020

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“…It was demonstrated in [34] that the increase of reflectivity between frozen and thawed states modelled according to the SMAP F/T product is observable in GNSS-R data. More recently (see Figure 9), a high similarity between TechDemoSat-1 reflectivity monthly means (in green) and seasonal variability of SMAP FT products (in red) was observed when analysing different land cover categories in Siberia [35]. With this capability, HydroGNSS can potentially help address any gap left when the ESA SMOS and NASA SMAP soil moisture satellites complete their missions.…”

Section: Soil Freeze/thaw and Permafrostmentioning

confidence: 85%

“…Figure 14). Freeze/thaw discrimination will be based on change detection methods [35], whilst inundation flag will exploit the coherence [29]. Biomass retrieval is foreseen to make use of Neural Networks [33].…”

Section: Level 2 Algorithms and Validationmentioning

confidence: 99%

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An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission

Unwin

Pierdicca

Cardellach

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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HydroGNSS has been selected as the second ESA Scout Earth Observation mission to demonstrate the capability of small satellites to deliver science. This paper summarises the case for HydroGNSS, as developed during its System Consolidation study. HydroGNSS is a high value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement existing observations and are high in ESA's Earth Observation scientific priorities. The mission delivers observations of four hydrological Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) using the new technique of GNSS Reflectometry. These will cover the world's land mass to 25 km resolution, with a 15 day revisit. The variables are soil moisture, inundation or wetlands, freeze / thaw state and above ground biomass.

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“…GNSS-R is ideally placed to meet this challenge. The L-band signals are sensitive to parameters such as soilmoisture, freeze-thaw status and AGB [3,[18][19][20]; and the low size, weight and power (SWaP) platforms used for the method enable-in a complementary way to other technologiessingle satellites or even constellations to be built and launched more quickly, cheaply and flexibly than large traditional radar missions. GNSS-R technology must be adapted to optimally target land parameters and this includes developing means of accurately predicting the reflection points over the land.…”

Section: Motivation and Requirementsmentioning

confidence: 99%

Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification

et al. 2021

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GNSS Reflectometry (GNSS-R), a method of remote sensing using the reflections from satellite navigation systems, was initially envisaged for ocean wind speed sensing. In recent times there has been significant interest in the use of GNSS-R for sensing land parameters such as soil moisture, which has been identified as an Essential Climate Variable (ECV). Monitoring objectives for ECVs set by the Global Climate Observing System (GCOS) organisation include a reduction in data gaps from spaceborne sources. GNSS-R can be implemented on small, relatively cheap platforms and can enable the launch of constellations, thus reducing such data gaps in these important datasets. However in order to realise operational land sensing with GNSS-R, adaptations are required to existing instrumentation. Spaceborne GNSS-R requires the reflection points to be predicted in advance, and for land sensing this means the effect of topography must be considered. This paper presents an algorithm for on-board prediction of reflection points over the land, allowing generation of DDMs on-board as well as compression and calibration. The algorithm is tested using real satellite data from TechDemoSat-1 in a software receiver with on-board constraints being considered. Three different resolutions of Digital Elevation Model are compared. The algorithm is shown to perform better against the operational requirements of sensing land parameters than existing methods and is ready to proceed to flight testing.

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Monitoring Freeze-Thaw State by Means of GNSS Reflectometry: An Analysis of TechDemoSat-1 Data

Cited by 37 publications

References 46 publications

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission

Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification

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