Community geometric standards for remote sensing products

2021 ◽  
Author(s):  
Guoqing (Gary) Lin ◽  
Robert Wolfe ◽  
Bin Tan ◽  
Jaime Nickeson
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
European Space Agency ◽  
Remote Sensing Data ◽  
Performance Standards ◽  
Positional Accuracy ◽  
Factors Affecting ◽  
Remote Sensors ◽  
Data Products ◽  
Moderate Resolution Imaging Spectroradiometer

<p>We have developed a set of geometric standards for assessing earth observing data products derived from space-borne remote sensors.  We have worked with the European Space Agency (ESA) Earthnet Data Assessment Pilot (EDAP) project to provide a set of guidelines to assess geometric performance in data products from commercial electronic-optical remote sensors aboard satellites such as those from Planet Labs. The guidelines, or the standards, are based on performance from a few NASA procured sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors and the Advanced Baseline Imager (ABI) sensors. The standards include sensor spatial response, absolute positional accuracy, and band-to-band co-registration. They are tiered in “basic”, “intermediate” and “goal” criteria. These are important geometric factors affecting scientific use of remote sensing data products. We also discuss possible approaches achieving the highest goal in geometric performance standards.</p>

scholarly journals Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

2021 ◽  
Vol 13 (2) ◽  
pp. 184
Author(s):  
Rongjie Liu ◽  
Jie Zhang ◽  
Tingwei Cui ◽  
Haocheng Yu
Keyword(s):  
Remote Sensing ◽  
Indian Ocean ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Spatiotemporal Pattern ◽  
Anthropogenic Aerosols ◽  
Remote Sensing Reflectance ◽  
The Indian Ocean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

scholarly journals Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night

2020 ◽  
Vol 12 (24) ◽  
pp. 4139
Author(s):  
Ruirui Wang ◽  
Wei Shi ◽  
Pinliang Dong
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Important Indicator ◽  
Artificial Lighting ◽  
Sensing Data ◽  
Dragon Fruit ◽  
Positive Correlations

The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.

Tsunami Assessment and Evacuation Analysis Using Remote Sensing for Tianya District of Sanya City, China

2019 ◽  
Vol 13 (05n06) ◽  
pp. 1941003
Author(s):  
Jingming Hou ◽  
Zhiyuan Ren ◽  
Peitao Wang ◽  
Juncheng Wang ◽  
Yi Gao
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Tsunami Inundation ◽  
Sensing Data ◽  
Inundation Area ◽  
Nighttime Lights ◽  
Elevation Data ◽  
Landsat Satellite

Tsunami is one of the world’s most dangerous marine disaster. In this paper, freely available remote sensing data are applied to study the hazard, vulnerability, and evacuation in the event that a tsunami strikes the district of Tianya in the city of Sanya. Tsunami inundation is calculated using a tsunami numerical model, and the tsunami vulnerability and evacuation in the inundation area are analyzed. Aster Global Digital Elevation Model elevation data are applied to provide input data for the tsunami numerical model and thus obtain tsunami inundation areas, while they are also used to study the surface slope for evacuation. Landsat satellite imagery is used to analyze land–water borders and land cover in both hazard assessment and evacuation analysis. Visible Infrared Imaging Radiometer Suite nighttime lights data provide information of the socioeconomic activity for vulnerability analysis. The analysis results show that the remote sensing data is suitable for tsunami assessment and evacuation analysis of China’s county-level region. We can get a general understanding about tsunami vulnerability and evacuation situation. One kind of remote sensing data can accomplish several tasks, avoiding the error caused by different source data. Remote sensing can play an important role in tsunami assessment.

Blending remote sensing data products to estimate photochemical production of hydrogen peroxide and superoxide in the surface ocean

2014 ◽  
Vol 16 (4) ◽  
pp. 792-806 ◽  
Author(s):  
Leanne C. Powers ◽  
William L. Miller
Keyword(s):  
Remote Sensing ◽  
Hydrogen Peroxide ◽  
Remote Sensing Data ◽  
Production Rates ◽  
Sensing Data ◽  
Surface Ocean ◽  
Photochemical Production ◽  
Production Of Hydrogen ◽  
Data Products ◽  
Global Surface

A novel combination of remote sensing products is used to estimate photochemical production rates of hydrogen peroxide and superoxide in the global surface ocean.

Using a model-of-models approach and remote sensing technologies to improve flood disaster alerting

2020 ◽  
Author(s):  
Guy J.-P. Schumann ◽  
Margaret Glasscoe ◽  
Douglas Bausch ◽  
Marlon Pierce ◽  
Jun Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Structural Damage ◽  
Flood Hazard ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Flood Impacts ◽  
Coordination System ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Almost All

<p>Floods are happening regularly in almost all places of the world and impact people, societies and economies, causing widespread devastation that can be hard to recover from. Yet, accurately predicting and alerting for floods is challenging, primarily since flood events are very local in nature and processes causing a flood can be very complex. In an era of open-access geospatial data proliferation as well as data and model interoperability, it makes sense to leverage on existing data and models, many of which are underutilized by decision-making applications. Thus, the objective of the project is to develop an open-access rapid alerting and severity assessment component for global flooding based on existing models and observation data sources. We do this within the DisasterAWARE platform of the Pacific Disaster Center (PDC).</p><p>This paper will outline the proposed concept of model-of-models that will leverage existing flood-hazard modeling capabilities, illustrating products that we will leverage, such as: GLOFAS (Global Flood Forecasting Feeds) probabilistic hydrologic data, IMERG (The Integrated Multi-satellitE Retrievals for GPM) observed precipitation grids, GDACS (Global Disaster Alerting Coordination System) anomaly points, GFMS (Global Flood Monitoring System) depth above baseline grids, the NASA MODIS (Moderate Resolution Imaging Spectroradiometer) and Dartmouth Observatory flood maps, as well as new models as they are developed. We will further combine the flood hazard data with existing exposure data to estimate property loss using a probabilistic fragility approach. With the use of an end-to-end deep learning framework, structural damage will be detected using different remote sensing data. The approach will further incorporate other, non-routinely-generated remotely-sensed products for ground-truthing for areas and events where and when such products are available.</p><p>The existing resilience and capacity of communities to rapidly respond to and recover from flood impacts will be incorporated into the severity determination on an administrative area and watershed risk basis. This model-of-models approach will leverage major efforts, improve reliability and reduce false triggers by ensuring two or more models agree.</p>

Detecting drought effects on tree mortality in forests of Franconia (Germany)

2020 ◽  
Author(s):  
Johannes Heisig ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Landsat 8 ◽  
Sensing Data ◽  
Dead Trees ◽  
Spatial Coverage ◽  
Data Products ◽  
Dead Vegetation

<p>Central European forests face challenges with climate changing much faster than they can adapt. Extremely hot and dry summers like in 2018 deprive forests of soil moisture, leaving them with low ground water levels. While individuals with deep and well-established root systems survive, young individuals and shallow-rooted species perish.</p><p>In southern Germany, die-off of single trees or small groups got noticeable recently. Such effects of harsher conditions rarely occur over large areas, but more in a spotted, irregular manner. This makes the phenomenon difficult to detect and to estimate its extent. The share of trees lately deteriorated may be larger than expected and represent a considerable portion of forests. Therefore, we see the great need for monitoring. Remote sensing data is suitable to examine inaccessible areas at a large scale. To quantify mortality of individual trees among a majority of vital ones, sensor platforms and respective data have to fulfill certain criteria regarding spatial, temporal and spectral resolution. Dead trees can be distinguished from others due to discoloration and defoliation. This change in appearance affects the spectral response, even in pixels larger than the tree’s extent.</p><p>This study aims at recommending a suitable spatial scale for space-borne multispectral imagery products to achieve this task. We evaluate commercial and free remote sensing data products and their ability to estimate fractional cover of dead vegetation. Satellite data employed in this study comes from Landsat 8 (30 m), Sentinel-2 (10 m), RapidEye (6.5 m) and PlanetScope (3 m). Classification performance is tested against high-resolution multispectral aerial imagery (17 cm) acquired with a Micasense RedEdge-M camera.</p><p>High-resolution Micasense images are capable of detecting single dead trees, even after downgrading the resolution from 17 cm to 3 m. For all data products tested, fraction of dead trees per pixel did not differ significantly among land cover types (dead vegetation, vital vegetation, pavement, open soil). This indicates that individual dead trees may not be detectable in vital forest stands. The finding even seems to be valid for a resolution of 3 m (PlanetScope), which is identical to the downgraded Micasense data. In the near future the detection of this phenomenon might profit from technical developments towards even higher spatial detail of space-borne sensors. Alternatively, high resolution images from aerial campaigns, manned or unmanned, could bridge this gap when flight time and spatial coverage are increased significantly and facilitating policies are in place.</p>

scholarly journals Vicarious calibration of S-NPP VIIRS reflective solar M-bands against MODIS Aqua over dark water scenes

2016 ◽  
Author(s):  
A. M. Sayer ◽  
N. C. Hsu ◽  
C. Bettenhausen ◽  
R. E. Holz ◽  
J. Lee ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Earth Science ◽  
Total Error ◽  
Absolute Calibration ◽  
Vicarious Calibration ◽  
Modis Aod ◽  
Data Products ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Data Continuity

Abstract. The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) when similar algorithms are applied to the different sensors. This study presents a vicarious calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to VIIRS between approximately +2 % and −7 % (dependent on band) are needed to bring the two into alignment, and indications of relative trending of up to ~ 0.45 % per year in some bands. The derived vicarious gains are also applied in an AOD retrieval, and are shown to decrease the bias and total error in AOD across the midvisible spectral region compared to the standard VIIRS NASA calibration. The resulting bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multisensor data continuity.

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