DATA CALL QUESTIONS RELATED TO POTENTIAL NEPA/DATA GAPS

AbstractThe Southern Andes are often viewed as a classic example for kinematic partitioning of oblique plate convergence into components of continental margin-parallel strike-slip and transverse shortening. In this regard, the Liquiñe-Ofqui Fault Zone, one of Earth’s most prominent intra-arc deformation zones, is believed to be the most important crustal discontinuity in the Southern Andes taking up margin-parallel dextral strike-slip. Recent structural studies, however, are at odds with this simple concept of kinematic partitioning, due to the presence of margin-oblique and a number of other margin-parallel intra-arc deformation zones. However, knowledge on the extent of such zones in the Southern Andes is still limited. Here, we document traces of prominent structural discontinuities (lineaments) from the Southern Andes between 39° S and 46° S. In combination with compiled low-temperature thermochronology data and interpolation of respective exhumation rates, we revisit the issue of kinematic partitioning in the Southern Andes. Exhumation rates are maximal in the central parts of the orogen and discontinuity traces, trending predominantly N–S, WNW–ESE and NE–SW, are distributed across the entire width of the orogen. Notably, discontinuities coincide spatially with large gradients in Neogene exhumation rates and separate crustal domains characterized by uniform exhumation. Collectively, these relationships point to significant components of vertical displacement on these discontinuities, in addition to horizontal displacements known from published structural studies. Our results agree with previously documented Neogene shortening in the Southern Andes and indicate orogen-scale transpression with maximal vertical extrusion of rocks in the center of the transpression zone. The lineament and thermochronology data call into question the traditional view of kinematic partitioning in the Southern Andes, in which deformation is focused on the Liquiñe-Ofqui Fault Zone.

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COVID-19 data gaps and lack of transparency undermine pandemic response

Journal of Public Health ◽

10.1093/pubmed/fdab016 ◽

2021 ◽

Author(s):

Elias Kondilis ◽

Dimitris Papamichail ◽

Valentina Gallo ◽

Alexis Benos

Keyword(s):

Data Gaps ◽

Pandemic Response

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A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

Remote Sensing ◽

10.3390/rs13142838 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2838

Author(s):

Yaping Mo ◽

Yongming Xu ◽

Huijuan Chen ◽

Shanyou Zhu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Spatial Scales ◽

Remotely Sensed ◽

Urban Heat Islands ◽

Reconstruction Algorithms ◽

Gap Filling ◽

Practical Applications ◽

Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

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Conditioning and PPP processing of smartphone GNSS measurements in realistic environments

Satellite Navigation ◽

10.1186/s43020-021-00042-2 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Ganga Shinghal ◽

Sunil Bisnath

Keyword(s):

Single Point ◽

A Priori ◽

Density Ratio ◽

Satellite System ◽

Quality Parameters ◽

Urban Environments ◽

Dual Frequency ◽

Missing Measurements ◽

Data Gaps ◽

Point Positioning

AbstractSmartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (handheld, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments. In such environments, the system suffers from frequent phase loss-of-lock leading to data gaps. The smartphone measurements have low and irregular carrier-to-noise (C/N0) density ratio and high multipath, which leads to poor or no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine. Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS) error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were 20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic scenario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solution as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5–10% percentage point more availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged file cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution. The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Electronics Association) file was computed using point positioning with the aid of measurements from internal sensors. The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous computation of solution.

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