Relationship Between Vegetation Microwave Optical Depth and Cross-Polarized Backscatter From Multiyear Aquarius Observations

Rötzer, Kathrina; Montzka, Carsten; Entekhabi, Dara; Konings, Alexandra G.; McColl, Kaighin A.; Piles, María; Vereecken, Harry

doi:10.1109/jstars.2017.2716638

Cited by 10 publications

(6 citation statements)

References 41 publications

(54 reference statements)

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“…Reference Rötzer et al [19] did observe positive correlations between cross-polarized backscatter (HV from Aquarius SAC-D) and VOD from SMOS passive microwave observations. To assess if VH is more sensitive to vegetation dynamics as observed in VOD, we calculated the Pearson correlation coefficient between Sentinel-1 VH backscatter and ASCAT VOD, using the same resampling and masking methods as for the comparison between Sentinel-1 CR and ASCAT VOD.…”

Section: Vegetation Structure Effectsmentioning

confidence: 95%

“…In addition, the method proposed by Vreugdenhil et al [6] to obtain VOD from Metop ASCAT backscatter observations is not applicable to Sentinel-1 observations as it needs backscatter observations under a large range of incidence angles to obtain a robust slope estimate. One study has compared HV cross-polarized backscatter coefficients from the Aquarius radar on-board the Satélite de Applicaciones Científicas (SAC)-D satellite to VOD retrieved from the Microwave Imaging Radiometer Aperture Synthesis on-board the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite on a global scale, at 1 • spatial resolution, Rötzer et al [19] with the goal to obtain VOD from backscatter observations. In their study, a global linear relation between HV backscatter and VOD was found.…”

Section: Introductionmentioning

confidence: 99%

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Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

et al. 2020

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Vegetation products based on microwave remote sensing observations, such as Vegetation Optical Depth (VOD), are increasingly used in a variety of applications. One disadvantage is the often coarse spatial resolution of tens of kilometers of products retrieved from microwave observations from spaceborne radiometers and scatterometers. This can potentially be overcome byusing new high-resolution Synthetic Aperture Radar (SAR) observations from Sentinel-1. However, the sensitivity of Sentinel-1 backscatter to vegetation dynamics, or its use in radiative transfer models, such as the water cloud model, has only been tested at field to regional scale. In this study, we compared the cross-polarization ratio (CR) to vegetation dynamics as observed in microwave-based Vegetation Optical Depth from coarse-scale satellites over Europe. CR was obtained from Sentinel-1 VH and VV backscatter observations at 500 m sampling and resampled to the spatial resolution of VOD from the Advanced SCATterometer (ASCAT) on-board the Metop satellite series. Spatial patterns between median CR and ASCAT VOD correspond to each other and to vegetation patterns over Europe. Analysis of temporal correlation between CR and ASCAT VOD shows that high Pearson correlation coefficients (Rp) are found over croplands and grasslands (median Rp > 0.75). Over deciduous broadleaf forests, negative correlations are found. This is attributed to the effect of structural changes in the vegetation canopy which affect CR and ASCAT VOD in different ways. Additional analysis comparing CR to passive microwave-based VOD shows similar effects in deciduous broadleaf forests and high correlations over crop- and grasslands. Though the relationship between CR and VOD over deciduous forests is unclear, results suggest that CR is useful for monitoring vegetation dynamics over crop- and grassland and a potential path to high-resolution VOD.

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Section: Vegetation Structure Effectsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

et al. 2020

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“…For the vegetation correction we make use of the cross‐polarized backscatter product S X X of the airborne PolSAR measurement. Le Toan et al () provided experimental evidence that this quantity can be considered as a proxy for biomass, and Rötzer et al () found that it is also correlated with vegetation optical depth. Their findings suggest a linear relationship to the backscattering coefficient (or an exponential to the logarithmic coefficient).…”

Section: Methods and Datamentioning

confidence: 99%

Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network

Fersch

Jagdhuber

Schrön

et al. 2018

Water Resources Research

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The consistent determination of soil moisture across scales is a persistent challenge in hydrology. Several measurement methods exist at distinct scales, each of which is challenging in terms of data processing, removal of vegetation and surface effects, and calibration. While in situ measurements are trusted at the point scale, distributed sensor networks extend the areal representation to the field scale. At this scale, also cosmic ray neutron sensing (CRNS) has become an established method to derive volume‐averaged, root zone soil moisture over several tens of hectometers, but the signal is often biased due to biomass water. With airborne synthetic aperture radar (SAR) remote sensing, it is possible to cover regional scales, but the method is limited to the topmost soil layer and sensitive to vegetation parameters. In this study, the performance and synergistic potential of these complementary methods is investigated for the determination of soil moisture within a 55 km2 Alpine foothill river catchment in Southern Germany. The individual approaches are evaluated and brought into synergy for a 9 ha grassland and several other locations within the catchment. The results indicate that the sensor network data provide valuable information to calibrate the mobile CRNS rover, and to optimize the vegetation removal within the polarimetric SAR retrieval algorithm. The root‐mean‐square errors for polarimetric SAR soil permittivity are 9.32 with the standard agriculture approach, 4.29 with the semi‐stand‐alone approach, and 0.31 with the sensor network optimized approach. Furthermore, the CRNS soil moisture product was improved by considering the remotely sensed cross‐polarized backscatter product as a biomass water proxy.

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“…Fusion methods include temporal change detection methods [286], Bayesian merging approaches [287], statistical disaggregation [285,288,289], and physics-based covariation algorithms [290][291][292]. Other methods retrieve vegetation variables from active microwave measurements for the utilization of passive microwave soil moisture inversion [293,294]. Unfortunately, the RADAR on the SMAP satellite went out of service on 7 July 2015.…”

Section: Combining Active and Passive Microwave Sensorsmentioning

confidence: 99%

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

Lausch

Baade

Bannehr³

et al. 2019

Remote Sensing

Self Cite

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In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

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Relationship Between Vegetation Microwave Optical Depth and Cross-Polarized Backscatter From Multiyear Aquarius Observations

Cited by 10 publications

References 41 publications

Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

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