Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

Zribi, Mehrez; Motte, Erwan; Baghdadi, Nicolas; Baup, Frédéric; Dayau, Sylvia; Fanise, Pascal; Guyon, Dominique; Huc, Mireille; Wigneron, Jean‐Pierre

doi:10.3390/rs10081245

Cited by 37 publications

(35 citation statements)

References 33 publications

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“…As the elevation angle increases, the absolute value of the slope (a) and the independent coefficient (b) both increase ( Table 2, columns 6 and 7). These results are in agreement with [10], showing how the polarization ratio of the GNSS-R observables decreased with increasing vegetation, in [10] parametrized with the Leaf Area Index.…”

Section: Discussionsupporting

confidence: 87%

L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning

et al. 2020

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At L-band (1–2 GHz), and particularly in microwave radiometry (1.413 GHz), vegetation has been traditionally modeled with the τ-ω model. This model has also been used to compensate for vegetation effects in Global Navigation Satellite Systems-Reflectometry (GNSS-R) with modest success. This manuscript presents an analysis of the vegetation impact on GPS L1 C/A (coarse acquisition code) signals in terms of attenuation and depolarization. A dual polarized instrument with commercial off-the-shelf (COTS) GPS receivers as back-ends was installed for more than a year under a beech forest collecting carrier-to-noise (C/N0) data. These data were compared to different ground-truth datasets (greenness, blueness, and redness indices, sky cover index, rain data, leaf area index or LAI, and normalized difference vegetation index (NDVI)). The highest correlation observed is between C/N0 and NDVI data, obtaining R2 coefficients larger than 0.85 independently from the elevation angle, suggesting that for beech forest, NDVI is a good descriptor of signal attenuation at L-band, which is known to be related to the vegetation optical depth (VOD). Depolarization effects were also studied, and were found to be significant at elevation angles as large as ~50°. Data were also fit to a simple τ-ω model to estimate a single scattering albedo parameter (ω) to try to compensate for vegetation scattering effects in soil moisture retrieval algorithms using GNSS-R. It is found that, even including dependence on the elevation angle (ω(θe)), at elevation angles smaller than ~67°, the ω(θe) model is not related to the NDVI. This limits the range of elevation angles that can be used for soil moisture retrievals using GNSS-R. Finally, errors of the GPS-derived position were computed over time to assess vegetation impact on the accuracy of the positioning.

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Section: Discussionsupporting

confidence: 87%

L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning

et al. 2020

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“…For full water balance accounting, observations of drainage from the rooting zone using drainage lysimeters, soil moisture at multiple soil levels, the flow of water down plant stems (stem flow), leaf wetness sensors, and of course multiple precipitation gages are required. Such a multi-measurement approach would also create an opportunity to compare the performance of emerging technologies like distributed temperature sensing from fiber optic cables (Schilperoort et al, 20 2018), modeling cosmic ray neutron fields for soil water source estimation (Andreasen et al, 2016), and Global Navigation Satellite System Reflectometry (GNSS-R) for soil moisture estimation (Zribi et al, 2018). It remains difficult to assimilate E and T measurements into models using conventional data assimilation techniques because observations may contain substantial bias error yet still provide valuable information (Williams et al, 2009).…”

Section: Research Imperativesmentioning

confidence: 99%

Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Stoy¹,

El‐Madany²,

Fisher³

et al. 2019

Preprint

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Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. Our ability to partition evapotranspiration (ET) into E and T is limited at the ecosystem scale, which renders the validation of satellite data and land surface models incomplete. Here, we review current progress in partitioning E and T, and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques provide additional opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully 35 tested. Many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency (EWUE) exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but the conditions under which this assumption holds require further analysis. Another critical assumption for many ET partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been 40 Biogeosciences Discuss., https://doi.

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“…It was then tested over continental surfaces, where it revealed its strong potential for the monitoring of various surface parameters such as the water content of the soil or vegetation biomass [18][19][20][21][22][23][24][25]. Following various recent in situ and airborne campaigns collecting GNSS-R measurements [22,26,27], several studies have illustrated their potential for the development of GNSS-R based applications [28][29][30][31][32][33][34][35][36][37][38]. [28,31,33] have shown the strong potential of GNSS-R TechDemoSat-1 receiver and reflections collected by the SMAP (Soil Moisture Active Passive) receiver for the mapping of surface conditions, such as soil moisture or vegetation properties.…”

Section: Introductionmentioning

confidence: 99%

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

et al. 2020

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The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° × 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.

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Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

Cited by 37 publications

References 33 publications

L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning

L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning

Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Desert Roughness Retrieval Using CYGNSS GNSS-R Data

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