Quantitative relationship study between spatial heterogeneity and uncertainty of high-resolution pixel scale based on spectral measurement simulation

2021 ◽  
Author(s):  
Peng Jiang ◽  
Jun Pan ◽  
Hailiang Gao ◽  
Han Sun ◽  
Kaisi Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Heterogeneity ◽  
Quantitative Relationship ◽  
Spectral Measurement ◽  
Relationship Study ◽  
Pixel Scale

scholarly journals Impacts of Spatial Heterogeneity and Temporal Non-Stationarity on Intensity-Duration-Frequency Estimates—A Case Study in a Mountainous California-Nevada Watershed

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1296 ◽  
Author(s):  
Huiying Ren ◽  
Z. Jason Hou ◽  
Mark Wigmosta ◽  
Ying Liu ◽  
L. Ruby Leung
Keyword(s):  
High Resolution ◽  
Spatial Heterogeneity ◽  
Time Windows ◽  
Exceedance Probability ◽  
Extreme Precipitation Events ◽  
Climate Simulations ◽  
Engineering Standards ◽  
Intensity Duration Frequency ◽  
Using Data

Changes in extreme precipitation events may require revisions of civil engineering standards to prevent water infrastructures from performing below the designated guidelines. Climate change may invalidate the intensity-duration-frequency (IDF) computation that is based on the assumption of data stationarity. Efforts in evaluating non-stationarity in the annual maxima series are inadequate, mostly due to the lack of long data records and convenient methods for detecting trends in the higher moments. In this study, using downscaled high resolution climate simulations of the historical and future periods under different carbon emission scenarios, we tested two solutions to obtain reliable IDFs under non-stationarity: (1) identify quasi-stationary time windows from the time series of interest to compute the IDF curves using data for the corresponding time windows; (2) introduce a parameter representing the trend in the means of the extreme value distributions. Focusing on a mountainous site, the Walker Watershed, the spatial heterogeneity and variability of IDFs or extremes are evaluated, particularly in terms of the terrain and elevation impacts. We compared observations-based IDFs that use the stationarity assumption with the two approaches that consider non-stationarity. The IDFs directly estimated based on the traditional stationarity assumption may underestimate the 100-year 24-h events by 10% to 60% towards the end of the century at most grids, resulting in significant under-designing of the engineering infrastructure at the study site. Strong spatial heterogeneity and variability in the IDF estimates suggest a preference for using high resolution simulation data for the reliable estimation of exceedance probability over data from sparsely distributed weather stations. Discrepancies among the three IDFs analyses due to non-stationarity are comparable to the spatial variability of the IDFs, underscoring a need to use an ensemble of non-stationary approaches to achieve unbiased and comprehensive IDF estimates.

scholarly journals High-resolution modeling of the spatial heterogeneity of soil moisture: Applications in network design

2015 ◽  
Vol 51 (1) ◽  
pp. 619-638 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Joshua K. Roundy ◽  
Julio E. Herrera-Estrada ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Heterogeneity ◽  
Network Design

scholarly journals Evaluating the impact of spatial resolution on tropospheric NO<sub>2</sub> column comparisons within urban areas using high-resolution airborne data

2019 ◽  
Vol 12 (11) ◽  
pp. 6091-6111 ◽  
Author(s):  
Laura M. Judd ◽  
Jassim A. Al-Saadi ◽  
Scott J. Janz ◽  
Matthew G. Kowalewski ◽  
R. Bradley Pierce ◽  
...  
Keyword(s):  
High Resolution ◽  
Los Angeles ◽  
Spatial Heterogeneity ◽  
Spatial Resolution ◽  
Urban Areas ◽  
High Temporal Resolution ◽  
Ozone Monitoring Instrument ◽  
Vertical Column ◽  
Monitoring Instrument ◽  
The Impact

Abstract. NASA deployed the GeoTASO airborne UV–visible spectrometer in May–June 2017 to produce high-resolution (approximately 250 m×250 m) gapless NO2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO2 observations (r2=0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and spatial heterogeneity that may be observed differently by the sunward-viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO (Tropospheric Emissions: Monitoring Pollution), TROPOMI (TROPOspheric Monitoring Instrument), and OMI (Ozone Monitoring Instrument), the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale =0.88; TROPOMI scale =0.77; OMI scale =0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30×1015 molecules cm−2. Two publicly available OMI tropospheric NO2 retrievals are found to be biased low with respect to these Pandora observations. However, the agreement improves when higher-resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 standard product slope =0.18 and Berkeley High Resolution product slope =0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high-spatial-resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high-temporal-resolution ground-based column observations to evaluate the influence of spatial heterogeneity on validation results.

scholarly journals MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

2021 ◽  
Vol 13 (21) ◽  
pp. 4220
Author(s):  
Yu Tao ◽  
Jan-Peter Muller ◽  
Siting Xiong ◽  
Susan J. Conway
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Spatial Resolution ◽  
Large Scale ◽  
Fine Scale ◽  
Martian Surface ◽  
Multi Scale ◽  
Single Scale ◽  
Imaging Science ◽  
Pixel Scale

The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25–50 cm/pixel of the surface of Mars. However, due to the spatial resolution being so high, the total area covered by HiRISE targeted stereo acquisitions is very limited. This results in a lack of the availability of high-resolution digital terrain models (DTMs) which are better than 1 m/pixel. Such high-resolution DTMs have always been considered desirable for the international community of planetary scientists to carry out fine-scale geological analysis of the Martian surface. Recently, new deep learning-based techniques that are able to retrieve DTMs from single optical orbital imagery have been developed and applied to single HiRISE observational data. In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes. In particular, we employ optimised single-scale inference and multi-scale reconstruction (in MADNet 2.0), instead of multi-scale inference and single-scale reconstruction (in MADNet 1.0), to produce more accurate large-scale topographic retrieval with boosted fine-scale resolution. We demonstrate the improvements of the MADNet 2.0 DTMs produced using HiRISE images, in comparison to the MADNet 1.0 DTMs and the published Planetary Data System (PDS) DTMs over the ExoMars Rosalind Franklin rover’s landing site at Oxia Planum. Qualitative and quantitative assessments suggest the proposed MADNet 2.0 system is capable of producing pixel-scale DTM retrieval at the same spatial resolution (25 cm/pixel) of the input HiRISE images.

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