Pub Date : 2022-11-25DOI: 10.3389/frsen.2022.1094060
Thais A. G. Medeiros, M. Zoffoli, R. Frouin, F. D. Cortivo, G. M. Cesar, M. Kampel
In the original article, there was an error in the Funding statement. We failed to include “Agencia Espacial Brasileira (AEB)” and “Instituto Nacional de Pesquisas Espaciais (INPE)” in the Funding statement. It should be corrected as follows: “Funding This study is a contribution from the Abrolhos Network (www.abrolhos.org) and was co-funded by Brazil’s National Research Council (CNPq) through the Abrolhos Long Term Ecological Monitoring Program (PELD-Site ABRS). The work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001, Agencia Espacial Brasileira (AEB) and Instituto Nacional de Pesquisas Espaciais (INPE). TM and FC were supported by OPEN ACCESS
{"title":"Corrigendum: Bio-optical properties of the Brazilian Abrolhos Bank’s shallow coral-reef waters","authors":"Thais A. G. Medeiros, M. Zoffoli, R. Frouin, F. D. Cortivo, G. M. Cesar, M. Kampel","doi":"10.3389/frsen.2022.1094060","DOIUrl":"https://doi.org/10.3389/frsen.2022.1094060","url":null,"abstract":"In the original article, there was an error in the Funding statement. We failed to include “Agencia Espacial Brasileira (AEB)” and “Instituto Nacional de Pesquisas Espaciais (INPE)” in the Funding statement. It should be corrected as follows: “Funding This study is a contribution from the Abrolhos Network (www.abrolhos.org) and was co-funded by Brazil’s National Research Council (CNPq) through the Abrolhos Long Term Ecological Monitoring Program (PELD-Site ABRS). The work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001, Agencia Espacial Brasileira (AEB) and Instituto Nacional de Pesquisas Espaciais (INPE). TM and FC were supported by OPEN ACCESS","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115112950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.3389/frsen.2022.1020184
Ileana Callejas, K. Osborn, Christine M. Lee, D. Mishra, Nicole Auil Gomez, Abel Carrias, E. Cherrington, R. Griffin, A. Rosado, Samir Rosado, Jennifer A. Jay
Coral reefs are highly diverse ecosystems that provide many goods and ecosystem services globally. Coral reef ecosystems are also threatened by environmental stressors from anthropogenic sources and shifting climates. The United Nations Sustainable Development Goal 14 (“Life Below Water”) addresses the need to conserve and sustainably use the ocean, seas, and marine ecosystems, including reef systems. Belize’s coral reef system is the second largest in the world, providing sources of income to Belizeans through tourism and fisheries as well as coastline protection. In order to conserve their marine ecosystems, Belize has a network of Marine Protected Areas (MPAs) throughout their coastal waters. Using Aqua MODIS satellite imagery from 2002 to 2022, Google Earth Engine, and RStudio, we present a workflow to calculate stress days on MPAs and a coral vulnerability index based on sea surface temperature (SST) and Kd (490), a proxy of water clarity. The Corozal Bay, Swallow Caye, Port Honduras, and South Water Caye MPAs had the highest percentages of stress days and coral vulnerability stress index score based on these two parameters among the 24 MPAs analyzed. Additionally, SST in the warmest month of the year in Belize were seen to increase across all MPAs from 2002 to 2022 (p < 0.01). This GEE toolkit provides a straightforward and accessible tool to help governments monitor both water quality and risks to coral reefs in accordance with SDG 14.
{"title":"A GEE toolkit for water quality monitoring from 2002 to 2022 in support of SDG 14 and coral health in marine protected areas in Belize","authors":"Ileana Callejas, K. Osborn, Christine M. Lee, D. Mishra, Nicole Auil Gomez, Abel Carrias, E. Cherrington, R. Griffin, A. Rosado, Samir Rosado, Jennifer A. Jay","doi":"10.3389/frsen.2022.1020184","DOIUrl":"https://doi.org/10.3389/frsen.2022.1020184","url":null,"abstract":"Coral reefs are highly diverse ecosystems that provide many goods and ecosystem services globally. Coral reef ecosystems are also threatened by environmental stressors from anthropogenic sources and shifting climates. The United Nations Sustainable Development Goal 14 (“Life Below Water”) addresses the need to conserve and sustainably use the ocean, seas, and marine ecosystems, including reef systems. Belize’s coral reef system is the second largest in the world, providing sources of income to Belizeans through tourism and fisheries as well as coastline protection. In order to conserve their marine ecosystems, Belize has a network of Marine Protected Areas (MPAs) throughout their coastal waters. Using Aqua MODIS satellite imagery from 2002 to 2022, Google Earth Engine, and RStudio, we present a workflow to calculate stress days on MPAs and a coral vulnerability index based on sea surface temperature (SST) and Kd (490), a proxy of water clarity. The Corozal Bay, Swallow Caye, Port Honduras, and South Water Caye MPAs had the highest percentages of stress days and coral vulnerability stress index score based on these two parameters among the 24 MPAs analyzed. Additionally, SST in the warmest month of the year in Belize were seen to increase across all MPAs from 2002 to 2022 (p < 0.01). This GEE toolkit provides a straightforward and accessible tool to help governments monitor both water quality and risks to coral reefs in accordance with SDG 14.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132736143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.3389/frsen.2022.1064244
Jiuyi Zhang, Y. Sua, Yongxiang Hu, Jeevanandha Ramanathan, Yu-ping Huang
Oxygen A-band measurements can provide important information about cloud top height, cloud physical and optical thickness, and surface atmospheric pressure. So far, O2 A-band measurements are typically made with spectrometers at a spectral resolution of 40 p.m. (such as in the OCO-2 satellite) or a coarser resolution. This paper reports a new CubeSat measurement concept with higher spectral resolution over the O2 A-band using integrated photonic circuits and solar photon counting techniques. An integrated Micro-ring filter (MRR) chip with 10 p.m. resonant linewidth with an extinction ratio of 25 dB or higher is designed, fabricated, and used for precise measurements of the atmospheric oxygen A-band absorption line-shapes around 770 nm. With solar-photon counting and using a narrow-band filter made of an integrated, fast-swept MRR on lithium niobate on insulator (LNOI), we have demonstrated a high-resolution measurement of the O2 A-band absorption spectrum, exhibiting good agreement with the HITRAN database.
{"title":"Oxygen A-band absorption spectroscopy with solar photon counting and lithium niobate nanophotonic circuits","authors":"Jiuyi Zhang, Y. Sua, Yongxiang Hu, Jeevanandha Ramanathan, Yu-ping Huang","doi":"10.3389/frsen.2022.1064244","DOIUrl":"https://doi.org/10.3389/frsen.2022.1064244","url":null,"abstract":"Oxygen A-band measurements can provide important information about cloud top height, cloud physical and optical thickness, and surface atmospheric pressure. So far, O2 A-band measurements are typically made with spectrometers at a spectral resolution of 40 p.m. (such as in the OCO-2 satellite) or a coarser resolution. This paper reports a new CubeSat measurement concept with higher spectral resolution over the O2 A-band using integrated photonic circuits and solar photon counting techniques. An integrated Micro-ring filter (MRR) chip with 10 p.m. resonant linewidth with an extinction ratio of 25 dB or higher is designed, fabricated, and used for precise measurements of the atmospheric oxygen A-band absorption line-shapes around 770 nm. With solar-photon counting and using a narrow-band filter made of an integrated, fast-swept MRR on lithium niobate on insulator (LNOI), we have demonstrated a high-resolution measurement of the O2 A-band absorption spectrum, exhibiting good agreement with the HITRAN database.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125962169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.3389/frsen.2022.953733
A. J. Luis, Mahfooz Alam, S. Jawak
Using Scatterometer-based backscatter data, the spatial and temporal melt dynamics of Antarctic ice shelves were tracked from 2000 to 2018. We constructed melt onset and duration maps for the whole Antarctic ice shelves using a pixel-based, adaptive threshold approach based on backscatter during the transition period between winter and summer. We explore the climatic influences on the spatial extent and timing of snowmelt using meteorological data from automatic weather stations and investigate the climatic controls on the spatial extent and timing of snowmelt. Melt extent usually starts in the latter week of November, peaks in the end of December/January, and vanishes in the first/second week of February on most ice shelves. On the Antarctic Peninsula (AP), the average melt was 70 days, with the melt onset on 20 November for almost 50% of the region. In comparison to the AP, the Eastern Antarctic experienced less melt, with melt lasting 40–50 days. For the Larsen-C, Shackleton, Amery, and Fimbul ice shelf, there was a substantial link between melt area and air temperature. A significant correlation is found between increased temperature advection and high melt area for the Amery, Shackleton, and Larsen-C ice shelves. The time series of total melt area showed a decreasing trend of −196 km2/yr which was statistical significant at 97% interval. The teleconnections discovered between melt area and the combined anomalies of Southern Annular Mode and Southern Oscillation Index point to the high southern latitudes being coupled to the global climate system. The most persistent and intensive melt occurred on the AP, West Ice Shelf, Shackleton Ice Shelf, and Amery Ice Shelf, which should be actively monitored for future stability.
{"title":"Spatiotemporal change analysis for snowmelt over the Antarctic ice shelves using scatterometers","authors":"A. J. Luis, Mahfooz Alam, S. Jawak","doi":"10.3389/frsen.2022.953733","DOIUrl":"https://doi.org/10.3389/frsen.2022.953733","url":null,"abstract":"Using Scatterometer-based backscatter data, the spatial and temporal melt dynamics of Antarctic ice shelves were tracked from 2000 to 2018. We constructed melt onset and duration maps for the whole Antarctic ice shelves using a pixel-based, adaptive threshold approach based on backscatter during the transition period between winter and summer. We explore the climatic influences on the spatial extent and timing of snowmelt using meteorological data from automatic weather stations and investigate the climatic controls on the spatial extent and timing of snowmelt. Melt extent usually starts in the latter week of November, peaks in the end of December/January, and vanishes in the first/second week of February on most ice shelves. On the Antarctic Peninsula (AP), the average melt was 70 days, with the melt onset on 20 November for almost 50% of the region. In comparison to the AP, the Eastern Antarctic experienced less melt, with melt lasting 40–50 days. For the Larsen-C, Shackleton, Amery, and Fimbul ice shelf, there was a substantial link between melt area and air temperature. A significant correlation is found between increased temperature advection and high melt area for the Amery, Shackleton, and Larsen-C ice shelves. The time series of total melt area showed a decreasing trend of −196 km2/yr which was statistical significant at 97% interval. The teleconnections discovered between melt area and the combined anomalies of Southern Annular Mode and Southern Oscillation Index point to the high southern latitudes being coupled to the global climate system. The most persistent and intensive melt occurred on the AP, West Ice Shelf, Shackleton Ice Shelf, and Amery Ice Shelf, which should be actively monitored for future stability.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131799158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.3389/frsen.2022.1028240
R. Nassar, O. Moeini, John Mastrogiacomo, C. O’Dell, R. Nelson, M. Kiel, A. Chatterjee, A. Eldering, D. Crisp
We quantify CO2 emissions from Europe’s largest fossil fuel power plant, the Bełchatόw Power Station in Poland, using CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions on 10 occasions from March 2017 to June 2022. The space-based CO2 emission estimates reveal emission changes with a trend that is consistent with the independent reported hourly power generation trend that results from both permanent and temporary unit shutdowns. OCO-2 and OCO-3 emission estimates agree with the bottom-up emission estimates within their respective 1σ uncertainties for 9 of the 10 occasions. Different methods for defining background values and corresponding uncertainties are explored in order to better understand this important potential error contribution. These results demonstrate the ability of existing space-based CO2 observations to quantify emission reductions for a large facility when adequate coverage and revisits are available. The results are informative for understanding the expected capability and potential limitations of the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) and other future satellites to support monitoring and verification of CO2 emission reductions resulting from climate change mitigation efforts such as the Paris Agreement.
{"title":"Tracking CO2 emission reductions from space: A case study at Europe’s largest fossil fuel power plant","authors":"R. Nassar, O. Moeini, John Mastrogiacomo, C. O’Dell, R. Nelson, M. Kiel, A. Chatterjee, A. Eldering, D. Crisp","doi":"10.3389/frsen.2022.1028240","DOIUrl":"https://doi.org/10.3389/frsen.2022.1028240","url":null,"abstract":"We quantify CO2 emissions from Europe’s largest fossil fuel power plant, the Bełchatόw Power Station in Poland, using CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions on 10 occasions from March 2017 to June 2022. The space-based CO2 emission estimates reveal emission changes with a trend that is consistent with the independent reported hourly power generation trend that results from both permanent and temporary unit shutdowns. OCO-2 and OCO-3 emission estimates agree with the bottom-up emission estimates within their respective 1σ uncertainties for 9 of the 10 occasions. Different methods for defining background values and corresponding uncertainties are explored in order to better understand this important potential error contribution. These results demonstrate the ability of existing space-based CO2 observations to quantify emission reductions for a large facility when adequate coverage and revisits are available. The results are informative for understanding the expected capability and potential limitations of the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) and other future satellites to support monitoring and verification of CO2 emission reductions resulting from climate change mitigation efforts such as the Paris Agreement.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124028787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-24DOI: 10.3389/frsen.2022.986013
Thais A. G. Medeiros, M. Zoffoli, R. Frouin, F. D. Cortivo, G. M. Cesar, M. Kampel
The Abrolhos Bank harbors the richest coral reef ecosystem in the South Atlantic Ocean. It exhibits unique geomorphologic structures, is localized in shallow depths, and is divided into two reef regions with an inner arc close to the coast (3–20 m depth) and an outer deeper arc (5–30 m depth). This study aims to describe some bio-optical properties of the Abrolhos Bank waters and to evaluate the performance of the inversion Hyperspectral Optimization Processing Exemplar (HOPE) model, developed to retrieve optical properties in shallow waters, in the region. To this end, measurements at 75 stations during two field campaigns conducted during the 2013 and 2016 wet seasons were analyzed, and the HOPE model was applied to both in situ remote sensing reflectance (R rs ) spectra and PRecursore IperSpettrale della Missione Applicativa (PRISMA) imagery. Significant differences in optical and biological properties were found between the two arcs. The empirical relationships between chlorophyll-a concentration (Chl-a) and absorption coefficient of phytoplankton at 440 nm (a phy(440)) diverged from Bricaud’s models, suggesting differences in phytoplankton diversity and cell size. In both arcs, total non-water absorption coefficient at 440 nm (a T-w(440)) was dominated by colored dissolved organic matter (CDOM) by ∼60%. Absorption coefficient by CDOM (a cdom) presented a higher variability within the outer arc, with the lowest contribution from non-algal particles (NAPs), and the spectral slopes of a CDOM resembled those of the inner arc. The spectral slopes of the NAP absorption coefficient suggested a dominance by organic rather than mineral particles that probably originated from biological production. The HOPE model applied to in situ R rs performed satisfactorily for depth in the Abrolhos Bank waters, although retrievals of a phy(440), CDOM plus NAP (a dg(440)) and a T-w(440) were underestimated with a relative bias of −27.9%, −32.1% and −45.8%, respectively. The HOPE model retrievals from the PRISMA image exhibited low a phy(440) values over the whole scene and the highest a dg(440) values in the Caravelas river plume. Very shallow depths (≤3 m), bottom substrate reflectance used as input in the HOPE model, model parametrization associated with the water complexity in the study site, and uncertainties associated to R rs measurements used as input might be responsible for differences found when comparing HOPE retrievals with in situ measurements.
{"title":"Bio-optical properties of the Brazilian Abrolhos Bank’s shallow coral-reef waters","authors":"Thais A. G. Medeiros, M. Zoffoli, R. Frouin, F. D. Cortivo, G. M. Cesar, M. Kampel","doi":"10.3389/frsen.2022.986013","DOIUrl":"https://doi.org/10.3389/frsen.2022.986013","url":null,"abstract":"The Abrolhos Bank harbors the richest coral reef ecosystem in the South Atlantic Ocean. It exhibits unique geomorphologic structures, is localized in shallow depths, and is divided into two reef regions with an inner arc close to the coast (3–20 m depth) and an outer deeper arc (5–30 m depth). This study aims to describe some bio-optical properties of the Abrolhos Bank waters and to evaluate the performance of the inversion Hyperspectral Optimization Processing Exemplar (HOPE) model, developed to retrieve optical properties in shallow waters, in the region. To this end, measurements at 75 stations during two field campaigns conducted during the 2013 and 2016 wet seasons were analyzed, and the HOPE model was applied to both in situ remote sensing reflectance (R rs ) spectra and PRecursore IperSpettrale della Missione Applicativa (PRISMA) imagery. Significant differences in optical and biological properties were found between the two arcs. The empirical relationships between chlorophyll-a concentration (Chl-a) and absorption coefficient of phytoplankton at 440 nm (a phy(440)) diverged from Bricaud’s models, suggesting differences in phytoplankton diversity and cell size. In both arcs, total non-water absorption coefficient at 440 nm (a T-w(440)) was dominated by colored dissolved organic matter (CDOM) by ∼60%. Absorption coefficient by CDOM (a cdom) presented a higher variability within the outer arc, with the lowest contribution from non-algal particles (NAPs), and the spectral slopes of a CDOM resembled those of the inner arc. The spectral slopes of the NAP absorption coefficient suggested a dominance by organic rather than mineral particles that probably originated from biological production. The HOPE model applied to in situ R rs performed satisfactorily for depth in the Abrolhos Bank waters, although retrievals of a phy(440), CDOM plus NAP (a dg(440)) and a T-w(440) were underestimated with a relative bias of −27.9%, −32.1% and −45.8%, respectively. The HOPE model retrievals from the PRISMA image exhibited low a phy(440) values over the whole scene and the highest a dg(440) values in the Caravelas river plume. Very shallow depths (≤3 m), bottom substrate reflectance used as input in the HOPE model, model parametrization associated with the water complexity in the study site, and uncertainties associated to R rs measurements used as input might be responsible for differences found when comparing HOPE retrievals with in situ measurements.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.3389/frsen.2022.1042460
D. Cremons
Lidar has enabled advances in the knowledge of the Solar System through geophysical and atmospheric studies of the Moon, Mercury, Mars, and several asteroids. The technique will continue to be used to obtain high-precision topographic data from orbit, but new techniques on the horizon are suited to uniquely address fundamental planetary science questions related to the evolution of airless bodies, volatile delivery and sequestration, atmospheric transport, and small body formation and evolution. This perspective highlights the history of lidar in planetary science and identifies several measurement approaches that may be adopted in the coming years.
{"title":"The future of lidar in planetary science","authors":"D. Cremons","doi":"10.3389/frsen.2022.1042460","DOIUrl":"https://doi.org/10.3389/frsen.2022.1042460","url":null,"abstract":"Lidar has enabled advances in the knowledge of the Solar System through geophysical and atmospheric studies of the Moon, Mercury, Mars, and several asteroids. The technique will continue to be used to obtain high-precision topographic data from orbit, but new techniques on the horizon are suited to uniquely address fundamental planetary science questions related to the evolution of airless bodies, volatile delivery and sequestration, atmospheric transport, and small body formation and evolution. This perspective highlights the history of lidar in planetary science and identifies several measurement approaches that may be adopted in the coming years.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127481586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.3389/frsen.2022.1021781
C. L. Parkinson
Satellite passive-microwave instrumentation has allowed the monitoring of Arctic sea ice over the past 43 years, and this monitoring has revealed and quantified major changes occurring in Arctic sea ice coverage. The 43-year 1979–2021 record shows considerable interannual variability but also a long-term downward trend in Arctic sea ice that is clear from many vantage points: A linear-least-square trend of −54,300 ± 2,700 km2/year for yearly average sea ice extents; statistically significant negative trends for each of the 12 calendar months; negative trends for each of nine regions into which the Arctic sea ice cover is divided; the fact that for all 12 calendar months the highest monthly average sea ice extent came in the first 8 years of the record and the lowest monthly average sea ice extent came in the last 10 years of the record; and a prominent shortening of the sea ice season throughout much of the marginal ice zone, with the length of the sea ice season in some locations decreasing by over 100 days and some locations previously experiencing months-long sea ice seasons now typically no longer having a sea ice season at all. The overall, Arctic-wide trend value of the yearly average sea ice extents since 1979 has consistently had a negative magnitude exceeding two standard deviations of the trend line slope since 1990 and has remained in the narrow range of −53,000 km2/yr to −55,500 km2/yr since 2011.
{"title":"Arctic sea ice coverage from 43 years of satellite passive-microwave observations","authors":"C. L. Parkinson","doi":"10.3389/frsen.2022.1021781","DOIUrl":"https://doi.org/10.3389/frsen.2022.1021781","url":null,"abstract":"Satellite passive-microwave instrumentation has allowed the monitoring of Arctic sea ice over the past 43 years, and this monitoring has revealed and quantified major changes occurring in Arctic sea ice coverage. The 43-year 1979–2021 record shows considerable interannual variability but also a long-term downward trend in Arctic sea ice that is clear from many vantage points: A linear-least-square trend of −54,300 ± 2,700 km2/year for yearly average sea ice extents; statistically significant negative trends for each of the 12 calendar months; negative trends for each of nine regions into which the Arctic sea ice cover is divided; the fact that for all 12 calendar months the highest monthly average sea ice extent came in the first 8 years of the record and the lowest monthly average sea ice extent came in the last 10 years of the record; and a prominent shortening of the sea ice season throughout much of the marginal ice zone, with the length of the sea ice season in some locations decreasing by over 100 days and some locations previously experiencing months-long sea ice seasons now typically no longer having a sea ice season at all. The overall, Arctic-wide trend value of the yearly average sea ice extents since 1979 has consistently had a negative magnitude exceeding two standard deviations of the trend line slope since 1990 and has remained in the narrow range of −53,000 km2/yr to −55,500 km2/yr since 2011.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121500215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.3389/frsen.2022.1009398
Yuting Zhu, L. Powers, D. Kieber, W. Miller
Hydrogen peroxide (H2O2) is an important reactive oxygen species (ROS) in natural waters, affecting water quality via participation in metal redox reactions and causing oxidative stress for marine ecosystems. While attempts have been made to better understand H2O2 dynamics in the global ocean, the relative importance of various H2O2 sources and losses remains uncertain. Our model improves previous estimates of photochemical H2O2 production rates by using remotely sensed ocean color to characterize the ultraviolet (UV) radiation field in surface water along with quantitative chemical data for the photochemical efficiency of H2O2 formation. Wavelength- and temperature-dependent efficiency (i.e., apparent quantum yield, AQY) spectra previously reported for a variety of seawater sources, including coastal and oligotrophic stations in Antarctica, the Pacific Ocean at Station ALOHA, the Gulf of Mexico, and several sites along the eastern coast of the United States were compiled to obtain a “marine-average” AQY spectrum. To evaluate our predictions of H2O2 photoproduction in surface waters using this single AQY spectrum, we compared modeled rates to new measured rates from Gulf Stream, coastal, and nearshore river-outflow stations in the South Atlantic Bight, GA, United States; obtaining comparative differences of 33% or less. In our global model, the “marine-average” AQY spectrum was used with modeled solar irradiance, together with satellite-derived surface seawater temperature and UV optical properties, including diffuse attenuation coefficients and dissolved organic matter absorption coefficients estimated with remote sensing-based algorithms. The final product of the model, a monthly climatology of depth-resolved H2O2 photoproduction rates in the surface mixed layer, is reported for the first time and provides an integrated global estimate of ∼21.1 Tmol yr−1 for photochemical H2O2 production. This work has important implications for photo-redox reactions in seawater and improves our understanding of the role of solar irradiation on ROS cycling and the overall oxidation state in the oceans.
{"title":"Depth-resolved photochemical production of hydrogen peroxide in the global ocean using remotely sensed ocean color","authors":"Yuting Zhu, L. Powers, D. Kieber, W. Miller","doi":"10.3389/frsen.2022.1009398","DOIUrl":"https://doi.org/10.3389/frsen.2022.1009398","url":null,"abstract":"Hydrogen peroxide (H2O2) is an important reactive oxygen species (ROS) in natural waters, affecting water quality via participation in metal redox reactions and causing oxidative stress for marine ecosystems. While attempts have been made to better understand H2O2 dynamics in the global ocean, the relative importance of various H2O2 sources and losses remains uncertain. Our model improves previous estimates of photochemical H2O2 production rates by using remotely sensed ocean color to characterize the ultraviolet (UV) radiation field in surface water along with quantitative chemical data for the photochemical efficiency of H2O2 formation. Wavelength- and temperature-dependent efficiency (i.e., apparent quantum yield, AQY) spectra previously reported for a variety of seawater sources, including coastal and oligotrophic stations in Antarctica, the Pacific Ocean at Station ALOHA, the Gulf of Mexico, and several sites along the eastern coast of the United States were compiled to obtain a “marine-average” AQY spectrum. To evaluate our predictions of H2O2 photoproduction in surface waters using this single AQY spectrum, we compared modeled rates to new measured rates from Gulf Stream, coastal, and nearshore river-outflow stations in the South Atlantic Bight, GA, United States; obtaining comparative differences of 33% or less. In our global model, the “marine-average” AQY spectrum was used with modeled solar irradiance, together with satellite-derived surface seawater temperature and UV optical properties, including diffuse attenuation coefficients and dissolved organic matter absorption coefficients estimated with remote sensing-based algorithms. The final product of the model, a monthly climatology of depth-resolved H2O2 photoproduction rates in the surface mixed layer, is reported for the first time and provides an integrated global estimate of ∼21.1 Tmol yr−1 for photochemical H2O2 production. This work has important implications for photo-redox reactions in seawater and improves our understanding of the role of solar irradiation on ROS cycling and the overall oxidation state in the oceans.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125245680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.3389/frsen.2022.1025447
K. Stamnes, Wei Li, S. Stamnes, Yongxiang Hu, Yingzhen Zhou, N. Chen, Yongzhen Fan, B. Hamre, Xiaomei Lu, Yuping Huang, C. Weimer, Jennifer H. Lee, X. Zeng, J. Stamnes
Inversion of electromagnetic (EM) signals reflected from or transmitted through a medium, or emitted by it due to internal sources can be used to investigate the optical and physical properties of a variety of scattering/absorbing/emitting materials. Such media encompass planetary atmospheres and surfaces (including water/snow/ice), and plant canopies. In many situations the signals emerging from such media can be described by a linear transport equation which in the case of EM radiation is the radiative transfer equation (RTE). Solutions of the RTE can be used as a forward model to solve the inverse problem to determine the medium state parameters giving rise to the emergent (reflected/transmitted/emitted) EM signals. A novel method is developed to determine layer-by-layer contributions to the emergent signals from such stratified, multilayered media based on the solution of the pertinent RTE. As a specific example of how this approach may be applied, the radiation reflected from a multilayered atmosphere is used to solve the problem relevant for EM probing by a space-based lidar system. The solutions agree with those obtained using the standard lidar approach for situations in which single scattering prevails, but this novel approach also yields reliable results for optically thick, multiple scattering aerosol and cloud layers that cannot be provided by the traditional lidar approach.
{"title":"A novel approach to solve forward/inverse problems in remote sensing applications","authors":"K. Stamnes, Wei Li, S. Stamnes, Yongxiang Hu, Yingzhen Zhou, N. Chen, Yongzhen Fan, B. Hamre, Xiaomei Lu, Yuping Huang, C. Weimer, Jennifer H. Lee, X. Zeng, J. Stamnes","doi":"10.3389/frsen.2022.1025447","DOIUrl":"https://doi.org/10.3389/frsen.2022.1025447","url":null,"abstract":"Inversion of electromagnetic (EM) signals reflected from or transmitted through a medium, or emitted by it due to internal sources can be used to investigate the optical and physical properties of a variety of scattering/absorbing/emitting materials. Such media encompass planetary atmospheres and surfaces (including water/snow/ice), and plant canopies. In many situations the signals emerging from such media can be described by a linear transport equation which in the case of EM radiation is the radiative transfer equation (RTE). Solutions of the RTE can be used as a forward model to solve the inverse problem to determine the medium state parameters giving rise to the emergent (reflected/transmitted/emitted) EM signals. A novel method is developed to determine layer-by-layer contributions to the emergent signals from such stratified, multilayered media based on the solution of the pertinent RTE. As a specific example of how this approach may be applied, the radiation reflected from a multilayered atmosphere is used to solve the problem relevant for EM probing by a space-based lidar system. The solutions agree with those obtained using the standard lidar approach for situations in which single scattering prevails, but this novel approach also yields reliable results for optically thick, multiple scattering aerosol and cloud layers that cannot be provided by the traditional lidar approach.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125427210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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