scholarly journals Passive Microwave Melt Onset Retrieval Based on a Variable Threshold: Assessment in the Canadian Arctic Archipelago

2019 ◽  
Vol 11 (11) ◽  
pp. 1304 ◽  
Author(s):  
Stephen Marshall ◽  
K. Andrea Scott ◽  
Randall K. Scharien
Keyword(s):  
Brightness Temperature ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Canadian Arctic ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Canadian Arctic Archipelago ◽  
Variable Threshold ◽  
Resolution Data

The Canadian Arctic Archipelago (CAA) presents unique challenges to the determination of melt onset (MO) using remote sensing data. High spatial resolution data is required to discern melt onset among the islands and narrow waterways of the region. Current passive microwave retrievals use daily averaged 19 GHz and 37 GHz data from the multi-channel microwave radiometer (SMMR) and/or the special sensor microwave/imager (SSM/I). The development of a new passive microwave melt onset method capable of using higher resolution data is desirable. The new passive microwave melt onset method described here, named the Dynamic Threshold Variability Method (DTVM), uses higher resolution data from the 37 GHz vertically-polarized channel from the advanced microwave scanning radiometers (AMSR-E and AMSR-2). The DTVM MO detection methodology differs from previously presented passive microwave Arctic MO methods in that it does not use a fixed threshold of a brightness temperature parameter. Instead, the DTVM determines MO dates based on the distribution of dates corresponding to the exceedance of a range of brightness temperature variability thresholds. The method also uses swath data instead of daily averaged brightness temperatures, which is found to lead to improved melt detection. Two current passive microwave MO methods are compared and evaluated for applicability in the CAA alongside the DTVM. The DTVM provides MO dates at a higher spatial resolution than earlier methods in addition to higher correlation with MO dates from surface air temperature (SAT) reanalyses. It is found that, for some years, MO dates in the CAA exhibit a latitudinal dependence, while in other years the MO dates in the CAA are relatively uniform across the domain.

scholarly journals Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

2016 ◽  
Author(s):  
Libo Wang ◽  
Peter Toose ◽  
Ross Brown ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Temporal Variations ◽  
Passive Microwave ◽  
The Arctic ◽  
Winter Period ◽  
Strongly Correlated ◽  
Air Temperatures

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

2021 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen
Keyword(s):  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Statistical Consistency ◽  
Microwave Data ◽  
Infrared Data

<p>Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>

scholarly journals VALIDATION OF ASMR2 SEA ICE CONCENTRATION DATA USING MODIS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1741-1746
Author(s):  
K. Cho ◽  
R. Nagao ◽  
K. Naoki
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Threshold Level ◽  
Passive Microwave ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Free Condition ◽  
Modis Data ◽  
Ice Concentration

<p><strong>Abstract.</strong> Passive microwave radiometer AMSR2 was launched by JAXA in May 2012 on-board GCOM-W satellite. The antenna diameter of AMSR2 is 2.0&amp;thinsp;m which provide highest spatial resolution as a passive microwave radiometer in space. The sea ice concentration images derived from AMSR2 data allow us to monitor the detailed sea ice distributions of whole globe every day. The AMSR bootstrap algorithm developed by Dr. Josefino Comiso is used as the standard algorithm for calculating sea ice concentration from AMSR2 data. Under the contract with JAXA, the authors have been evaluating the performance of the algorithm. The sea ice concentration estimated from AMSR2 data were evaluated using MODIS data observed from Aqua satellite within few minutes after AMSR2 observation from GCOM-W. Since the spatial resolution of MODIS is much higher than that of AMSR2, under the cloud free condition, the ice concentration corresponds to the size of a pixel of AMSR2 can be calculated much accurately with MODIS data. The procedures of the evaluation are as follows. Firstly, MODIS band 1 reflectance were binarized to discriminate sea ice(1) from open water(0) and sea ice concentration of each pixel size of AMSR2 were calculated. In calculating sea ice concentration from MODIS data, the selection of the threshold level of MODIS band 1 reflectance is critical. Through the detailed evaluation, the authors selected 5% as the optimum threshold level. Then the AMSR2 sea ice concentration of each pixel was compared with the sea ice concentration calculated from MODIS data. The result suggested the possibility of estimating sea ice concentration from AMSR2 data with less than 10% error under the cloud free condition.</p>

scholarly journals Constructing a Multifrequency Passive Microwave Hail Retrieval and Climatology in the GPM Domain

2019 ◽  
Vol 58 (9) ◽  
pp. 1889-1904 ◽  
Author(s):  
Sarah D. Bang ◽  
Daniel J. Cecil
Keyword(s):  
United States ◽  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
The United States ◽  
Low Earth Orbit ◽  
Passive Microwave ◽  
Central United States ◽  
Microwave Data

AbstractLarge hail is a primary contributor to damages and loss around the world, in both agriculture and infrastructure. The sensitivity of passive microwave radiometer measurements to scattering by hail led to the development of proxies for severe hail, most of which use brightness temperature thresholds from 37-GHz and higher-frequency microwave channels on board weather satellites in low-Earth orbit. Using 16+ years of data from the Tropical Rainfall Measuring Mission (TRMM; 36°S–36°N), we pair TRMM brightness temperature–derived precipitation features with surface hail reports in the United States to train a hail retrieval on passive microwave data from the 10-, 19-, 37-, and 85-GHz channels based on probability curves fit to the microwave data. We then apply this hail retrieval to features in the Global Precipitation Measurement (GPM) domain (from 69°S to 69°N) to develop a nearly global passive microwave–based climatology of hail. The extended domain of the GPM satellite into higher latitudes requires filtering out features that we believe are over icy and snowy surface regimes. We also normalize brightness temperature depression by tropopause height in an effort to account for differences in storm depth between the tropics and higher latitudes. Our results show the highest hail frequencies in the region of northern Argentina through Paraguay, Uruguay, and southern Brazil; the central United States; and a swath of Africa just south of the Sahel. Smaller hot spots include Pakistan, eastern India, and Bangladesh. A notable difference between these results and many prior satellite-based studies is that central Africa, while still active in our climatology, does not rival the aforementioned regions in retrieved hailstorm frequency.

scholarly journals Recent extreme light sea ice years in the Canadian Arctic Archipelago: 2011 and 2012 eclipse 1998 and 2007

2013 ◽  
Vol 7 (2) ◽  
pp. 1313-1358 ◽  
Author(s):  
S. E. L. Howell ◽  
T. Wohlleben ◽  
A. Komarov ◽  
L. Pizzolato ◽  
C. Derksen
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Atmospheric Circulation ◽  
Canadian Arctic ◽  
Surface Air Temperature ◽  
Ice Cover ◽  
The Arctic ◽  
First Year ◽  
Canadian Arctic Archipelago ◽  
Northwest Passage

Abstract. Record low mean September sea ice area in the Canadian Arctic Archipelago (CAA) was observed in 2011 (146 × 103 km2), a level that was nearly exceeded in 2012 (150 × 103 km2). These values eclipsed previous September records set in 1998 (200 × 103 km2) and 2007 (220 × 103 km2) and are ∼60% lower than the 1981–2010 mean September climatology. In this study, the driving processes contributing to the extreme light years of 2011 and 2012 were investigated, compared to previous extreme minima of 1998 and 2007, and contrasted against historic summer seasons with above average September ice area. The 2011 minimum was driven by positive July surface air temperature (SAT) anomalies that facilitated rapid melt, coupled with atmospheric circulation in July and August that restricted multi-year ice (MYI) inflow from the Arctic Ocean into the CAA. The 2012 minimum was also driven by positive July SAT anomalies (with coincident rapid melt) but further ice decline was temporarily mitigated by atmospheric circulation in August and September which drove Arctic Ocean MYI inflow into the CAA. Atmospheric circulation was comparable between 2011 and 1998 (impeding Arctic Ocean MYI inflow) and 2012 and 2007 (inducing Arctic Ocean MYI inflow). However, evidence of both preconditioned thinner Arctic Ocean MYI flowing into CAA and maximum landfast first-year ice (FYI) thickness within the CAA was more apparent leading up to 2011 and 2012 than 1998 and 2007. The rapid melt process in 2011 and 2012 was more intense than observed in 1998 and 2007 because of the thinner ice cover being more susceptible to positive SAT forcing. The thinner sea ice cover within the CAA in recent years has also helped counteract the processes that facilitate extreme heavy ice years. The recent extreme light years within the CAA are associated with a longer navigation season within the Northwest Passage.

scholarly journals ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1009-1014
Author(s):  
S. Lin ◽  
J. Li ◽  
Q. Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Temporal Scale ◽  
Estimation Accuracy ◽  
Sensing Data ◽  
Resolution Data

Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16&amp;thinsp;days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (&amp;gt;&amp;thinsp;1&amp;thinsp;km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50&amp;thinsp;% (R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64&amp;thinsp;% of PAR variance (R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.85, RMSE &amp;lt;&amp;thinsp;3&amp;thinsp;gC/m<sup>2</sup>/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

scholarly journals Performance evaluation of super resolution algorithms in generating high resolution images using MSE and PSNR

2021 ◽  
Vol 10 (02) ◽  
pp. 25284-25291
Author(s):  
Palani Murugan ◽  
Vivek Kumar Gautam ◽  
V. Ramanathan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Optical Systems ◽  
High Resolution Data ◽  
Sensing Data ◽  
Resolution Data

In recent days, requirement of high spatial resolution remote sensing data in various fields has increased tremendously.  High resolution satellite remote sensing data is obtained with long focal length optical systems and low altitude. As fabrication of high-resolution optical system and accommodating on the satellite is a challenging task, various alternate methods are being explored to get high resolution imageries. Alternately the high-resolution data can be obtained from super resolution techniques. The super resolution technique uses single or multiple low-resolution mis-registered data sets to generate high resolution data set.  Various algorithms are employed in super resolution technique to derive high spatial resolution. In this paper we have compared two methods namely overlapping and interleaving methods and their capability in generating high resolution data are presented.

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