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Journal articles on the topic 'Advanced Microwave Scanning Radiometer-2 (AMSR2)'

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Published: 4 June 2025

Last updated: 1 August 2025

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1

Parinussa, Robert M., Thomas R. H. Holmes, Niko Wanders, Wouter A. Dorigo, and Richard A. M. de Jeu. "A Preliminary Study toward Consistent Soil Moisture from AMSR2." Journal of Hydrometeorology 16, no. 2 (2015): 932–47. http://dx.doi.org/10.1175/jhm-d-13-0200.1.

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Abstract A preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring

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Sun, Weifu, Jin Wang, Yuheng Li, Junmin Meng, Yujia Zhao, and Peiqiang Wu. "New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data." Remote Sensing 13, no. 12 (2021): 2402. http://dx.doi.org/10.3390/rs13122402.

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Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radio

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3

Zabolotskikh, E. V., and B. Chapron. "MODELING X-BAND MICROWAVE RADIATION OF THE ARCTIC SEAS BASED ON SATELLITE OBSERVATIONS: TAKING INTO ACCOUNT A MEASUREMENT ANGLE." Meteorologiya i Gidrologiya, no. 4 (2021): 69–77. http://dx.doi.org/10.52002/0130-2906-2021-4-69-77.

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The ocean X-band microwave emission model for modeling measurements of satellite radiometers over the cold Arctic seas at an observation angle of 65° is proposed. The model is based on the experimental geophysical model function (GMF) of microwave emission dependence on surface wind speed for an angle of 55°, that was developed from the AMSR2 (Advanced Microwave Scanning Radiometer 2) measurements and the two-scale theory of the ocean microwave radiation. The experimental GMF is derived from the comparison of AMSR2 measurements over the Arctic seas with surface wind speeds retrieved from these

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4

Hagan, Daniel Fiifi Tawia, Guojie Wang, Seokhyeon Kim, et al. "Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging." Remote Sensing 12, no. 13 (2020): 2164. http://dx.doi.org/10.3390/rs12132164.

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In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National A

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5

Okuyama, Arata, and Keiji Imaoka. "Intercalibration of Advanced Microwave Scanning Radiometer-2 (AMSR2) Brightness Temperature." IEEE Transactions on Geoscience and Remote Sensing 53, no. 8 (2015): 4568–77. http://dx.doi.org/10.1109/tgrs.2015.2402204.

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6

Fang, Bin, Venkat Lakshmi, Rajat Bindlish, and Thomas Jackson. "AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data." Remote Sensing 10, no. 10 (2018): 1575. http://dx.doi.org/10.3390/rs10101575.

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Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation

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7

Du, Jinyang, John S. Kimball, Lucas A. Jones, Youngwook Kim, Joseph Glassy, and Jennifer D. Watts. "A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations." Earth System Science Data 9, no. 2 (2017): 791–808. http://dx.doi.org/10.5194/essd-9-791-2017.

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Abstract. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fra

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8

Prakash, Satya, Hamid Norouzi, Marzi Azarderakhsh, Reginald Blake, Catherine Prigent, and Reza Khanbilvardi. "Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations." Journal of Applied Meteorology and Climatology 57, no. 4 (2018): 907–19. http://dx.doi.org/10.1175/jamc-d-17-0213.1.

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AbstractAccurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by usin

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9

Kim, Daesun, No-Wook Park, Nari Kim, et al. "Downscaling Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Data Using Regression-kriging." Journal of the Korean Cartographic Association 17, no. 2 (2017): 99–110. http://dx.doi.org/10.16879/jkca.2017.17.2.099.

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10

Dworak, Richard, Yinghui Liu, Jeffrey Key, and Walter N. Meier. "A Blended Sea Ice Concentration Product from AMSR2 and VIIRS." Remote Sensing 13, no. 15 (2021): 2982. http://dx.doi.org/10.3390/rs13152982.

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An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE)

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11

Zheng, Lei, Chunxia Zhou, Tingjun Zhang, Qi Liang, and Kang Wang. "Recent changes in pan-Antarctic region surface snowmelt detected by AMSR-E and AMSR2." Cryosphere 14, no. 11 (2020): 3811–27. http://dx.doi.org/10.5194/tc-14-3811-2020.

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Abstract. Surface snowmelt in the pan-Antarctic region, including the Antarctic ice sheet (AIS) and sea ice, is crucial to the mass and energy balance in polar regions and can serve as an indicator of climate change. In this study, we investigate the spatial and temporal variations in surface snowmelt over the entire pan-Antarctic region from 2002 to 2017 by using passive microwave remote sensing data. The stable orbits and appropriate acquisition times of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2)

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12

Lee, Yong-Keun, Cezar Kongoli, and Jeffrey Key. "An In-Depth Evaluation of Heritage Algorithms for Snow Cover and Snow Depth Using AMSR-E and AMSR2 Measurements." Journal of Atmospheric and Oceanic Technology 32, no. 12 (2015): 2319–36. http://dx.doi.org/10.1175/jtech-d-15-0100.1.

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AbstractThe Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched in 2012 on board the Global Change Observation Mission 1st–Water (GCOM-W1) satellite. This study presents a robust evaluation of AMSR2 algorithms for the retrieval of snow-covered area (SCA) and snow depth (SD) that will be used operationally by the National Oceanic and Atmospheric Administration (NOAA). Quantitative assessment of the algorithms was performed for a 10-yr period with AMSR-E and a 2-yr period with AMSR2 data using the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and in situ SD

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13

Xiao, Lin, Tao Che, and Liyun Dai. "Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016." Remote Sensing 12, no. 19 (2020): 3253. http://dx.doi.org/10.3390/rs12193253.

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Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Adva

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14

Zabolotskikh, E. V. "Review of methods to retrieve sea ice parameters from satellite microwave radiometer data." Известия Российской академии наук. Физика атмосферы и океана 55, no. 1 (2019): 128–51. http://dx.doi.org/10.31857/s0002-3515551128-151.

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Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations

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15

Banzon, Viva F., and Richard W. Reynolds. "Use of WindSat to Extend a Microwave-Based Daily Optimum Interpolation Sea Surface Temperature Time Series." Journal of Climate 26, no. 8 (2013): 2557–62. http://dx.doi.org/10.1175/jcli-d-12-00628.1.

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Abstract The NOAA ¼° daily optimum interpolation sea surface temperature analysis (DOISST) is available either as a 31-yr (from 1981 onward) time series based on Advanced Very High Resolution Radiometer (AVHRR) observations or as a 9-yr (2002–11) time series that incorporates additional data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (EOS) platform. In October 2011, AVHRR+AMSR DOISST production was discontinued when the AMSR instrument lost its capability to collect daily, global-coverage data. Sea surface temperatures from the follow-up AMSR2 instrume

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16

Zhang, Quan, Ninglian Wang, Yuwei Wu, and An’an Chen. "Adapting an Existing Empirical Algorithm for Microwave Land Surface Temperature Retrieval in China for AMSR2 Data." Remote Sensing 15, no. 13 (2023): 3228. http://dx.doi.org/10.3390/rs15133228.

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To extend the time span of the microwave (MW) land surface temperature (LST) dataset in China, this study proposed an optimized empirical algorithm for Advanced Microwave Scanning Radiometer 2 (AMSR2) LST retrieval based on the algorithm for its predecessor, the AMSR-Earth Observing System (AMSR-E). A modified comprehensive classification system of environmental variables (CCSEV) that considered the impact of landform, landcover, atmospheric conditions, and solar radiation on the variation of LST was first constructed, and the LST for each class in the CCSEV was then retrieved through stepwise

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17

You, Yalei, Veljko Petkovic, Jackson Tan, et al. "Evaluation of V05 Precipitation Estimates from GPM Constellation Radiometers Using KuPR as the Reference." Journal of Hydrometeorology 21, no. 4 (2020): 705–28. http://dx.doi.org/10.1175/jhm-d-19-0144.1.

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AbstractThis study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clear

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18

Kumar, Sujay V., Michael Jasinski, David M. Mocko, et al. "NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment." Journal of Hydrometeorology 20, no. 8 (2019): 1571–93. http://dx.doi.org/10.1175/jhm-d-17-0125.1.

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Abstract This article describes one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover, and irrigation intensity environmental data records (EDRs) from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), Advanced Scatterometer (ASCAT), Moderate-Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Soil Moisture Ocean Salinity (SMOS) mission, and Soil Moisture Active Passive (SMAP) mission. The analy

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19

Kumar, S. V., C. D. Peters-Lidard, J. A. Santanello, et al. "Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes." Hydrology and Earth System Sciences 19, no. 11 (2015): 4463–78. http://dx.doi.org/10.5194/hess-19-4463-2015.

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Abstract. Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a huma

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Kumar, S. V., C. D. Peters-Lidard, J. A. Santanello, et al. "Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes." Hydrology and Earth System Sciences Discussions 12, no. 6 (2015): 5967–6009. http://dx.doi.org/10.5194/hessd-12-5967-2015.

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Abstract. The Earth's land surface is characterized by tremendous natural heterogeneity and human engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human induced modifications to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a

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21

Du, Jinyang, John Kimball, Rolf Reichle, Lucas Jones, Jennifer Watts, and Youngwook Kim. "Global Satellite Retrievals of the Near-Surface Atmospheric Vapor Pressure Deficit from AMSR-E and AMSR2." Remote Sensing 10, no. 8 (2018): 1175. http://dx.doi.org/10.3390/rs10081175.

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Near-surface atmospheric Vapor Pressure Deficit (VPD) is a key environmental variable affecting vegetation water stress, evapotranspiration, and atmospheric moisture demand. Although VPD is readily derived from in situ standard weather station measurements, more spatially continuous global observations for regional monitoring of VPD are lacking. Here, we document a new method to estimate daily (both a.m. and p.m.) global land surface VPD at a 25-km resolution using a satellite passive microwave remotely sensed Land Parameter Data Record (LPDR) derived from the Advanced Microwave Scanning Radio

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22

Liu, Yinghui, Sean Helfrich, Walter N. Meier, and Richard Dworak. "Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS." Remote Sensing 12, no. 10 (2020): 1582. http://dx.doi.org/10.3390/rs12101582.

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This work assesses the AMSR2 (the Advanced Microwave Scanning Radiometer 2) ice extent and ice edge in the Arctic using the ice extent products of NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS) from the period of July 2015 to July 2019. Daily values and monthly means of four statistical scores (hit rate, false alarm ratio, false alarm rate, and Hanssen-Kuiper Skill Score) over the Arctic Ocean show distinct annual cycles. IMS ice edges often extend further south compared to those from AMSR2, with up to 100 km differences over the Beaufort, Chukchi, and East Siberian Seas in A

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23

Yan, Ran, and Jianjun Bai. "A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference." Remote Sensing 12, no. 17 (2020): 2818. http://dx.doi.org/10.3390/rs12172818.

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The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface te

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Cho, K., K. Naoki, and J. Comiso. "DETAILED VALIDATION OF AMSR2 SEA ICE CONCENTRATION DATA USING MODIS DATA IN THE SEA OF OKHOTSK." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 369–73. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-369-2020.

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Abstract. Global warming is one of the most serious problems we are facing in the 21st Century. Sea ice has an important role of reflecting the solar radiation back into space. However, once sea ice started to melt, the ice-free water would absorb the solar radiation and amplify global warming in the Arctic region. Thus, importance of sea ice monitoring is increasing. Since longer wavelength microwave can penetrate clouds, passive microwave radiometers on-board satellites are powerful tools for monitoring the global distribution of sea ice on daily basis. The Advanced Passive Microwave Scannin

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25

Brasnett, Bruce, and Dorina Surcel Colan. "Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2." Journal of Atmospheric and Oceanic Technology 33, no. 2 (2016): 361–75. http://dx.doi.org/10.1175/jtech-d-15-0093.1.

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AbstractExperiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows t

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26

Li, Xueqi, and Hailun He. "Inter-Comparison of Satellite-Based Sea Ice Concentration in the Amundsen Sea, Antarctica." Remote Sensing 15, no. 24 (2023): 5695. http://dx.doi.org/10.3390/rs15245695.

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We conducted a comparison of sea ice concentration (SIC) in the Amundsen Sea using three satellite datasets: Hadley Centre’s sea ice and sea surface temperature (HadISST1), Operational Sea Surface Temperature and Ice Analysis (OSTIA), and Advanced Microwave Scanning Radiometer 2 (AMSR2). HadISST1 has the longest time period, while AMSR2 has the shortest. In terms of grid resolution, HadISST1 has the coarsest resolution, while AMSR2 has the finest. The sea ice areas (SIAs) observed in HadISST1, OSTIA, and AMSR2 are similar. We studied the decadal variations in SICs by dividing the study period

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Chen, Y., X. Zhao, M. Qu, Z. Cheng, X. Pang, and Q. Ji. "INTER-COMPARISONS AMONG PASSIVE MICROWAVE SEA ICE CONCENTRATION PRODUCTS FROM FY-3D MWRI AND AMSR2." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 861–67. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-861-2020.

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Abstract. Passive microwave (PM) sensors on satellite can monitor sea ice distribution with their strengths of daylight- and weather-independent observations. Microwave Radiation Imager (MWRI) sensor aboard on the Chinese FengYun-3D (FY-3D) satellites was launched in 2017 and provides continuous observation for Arctic sea ice since then. In this study, sea ice concentration (SIC) product is derived from brightness temperature (TB) data of MWRI, based on an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm. Our product is inter-compared with a publishe

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Kim, Hyunglok, Muhammad Zohaib, Eunsang Cho, Yann H. Kerr, and Minha Choi. "Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave-Based Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas." Advances in Meteorology 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1917372.

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For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD) dataset by utilizing Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Land Data Assimilation System (GLDAS) soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS-) based AOD products were used as reference datasets to validate the modeled AOD (MA). The SMO

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Nelson, Robert R., Annmarie Eldering, David Crisp, Aronne J. Merrelli, and Christopher W. O'Dell. "Retrieved wind speed from the Orbiting Carbon Observatory-2." Atmospheric Measurement Techniques 13, no. 12 (2020): 6889–99. http://dx.doi.org/10.5194/amt-13-6889-2020.

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Abstract. Satellite measurements of surface wind speed over the ocean inform a wide variety of scientific pursuits. While both active and passive microwave sensors are traditionally used to detect surface wind speed over water surfaces, measurements of reflected sunlight in the near-infrared made by the Orbiting Carbon Observatory-2 (OCO-2) are also sensitive to the wind speed. In this work, retrieved wind speeds from OCO-2 glint measurements are validated against the Advanced Microwave Scanning Radiometer-2 (AMSR2). Both sensors are in the international Afternoon Constellation (A-Train), allo

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Jiao, Zhonghu. "Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data." Remote Sensing 14, no. 23 (2022): 5960. http://dx.doi.org/10.3390/rs14235960.

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Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR fro

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Zhang, Biao, Xiaotong Yu, William Perrie, and Fenghua Zhou. "Air–Sea Interface Parameters and Heat Flux from Neural Network and Advanced Microwave Scanning Radiometer Observations." Remote Sensing 14, no. 10 (2022): 2364. http://dx.doi.org/10.3390/rs14102364.

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We present a new approach, based on a multi-parameter back-propagation neural network (BPNN) model, to simultaneously retrieve sea surface wind speed, sea surface temperature, near-surface air temperature, and dewpoint temperature over the global oceans from the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission 1st-Water (GCOM-W1). The model is trained and validated with the collocations of AMSR2 multi-channel (6.9–36.5 GHz) brightness temperatures, under both clear and cloudy conditions, and National Data Buoy Center (NDBC) and Tropical Atmosphere

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You, Yalei, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud. "Daily Rainfall Estimate by Emissivity Temporal Variation from 10 Satellites." Journal of Hydrometeorology 22, no. 3 (2021): 623–37. http://dx.doi.org/10.1175/jhm-d-20-0195.1.

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AbstractRainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Spec

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Lavergne, Thomas, Montserrat Piñol Solé, Emily Down, and Craig Donlon. "Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission." Cryosphere 15, no. 8 (2021): 3681–98. http://dx.doi.org/10.5194/tc-15-3681-2021.

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Abstract. Across spatial and temporal scales, sea-ice motion has implications for ship navigation, the sea-ice thickness distribution, sea-ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Rad

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Alemu, Woubet G., and Michael C. Wimberly. "Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia." Sensors 20, no. 5 (2020): 1316. http://dx.doi.org/10.3390/s20051316.

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Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data f

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CHO, Eunsang, Heewon MOON, and Minha CHOI. "First Assessment of the Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Contents in Northeast Asia." Journal of the Meteorological Society of Japan. Ser. II 93, no. 1 (2015): 117–29. http://dx.doi.org/10.2151/jmsj.2015-008.

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Duncan, David Ian, Patrick Eriksson, and Simon Pfreundschuh. "An experimental 2D-Var retrieval using AMSR2." Atmospheric Measurement Techniques 12, no. 12 (2019): 6341–59. http://dx.doi.org/10.5194/amt-12-6341-2019.

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Abstract. A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at abou

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Yuan, Zijin, Nusseiba NourEldeen, Kebiao Mao, Zhihao Qin, and Tongren Xu. "Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa." Water 14, no. 1 (2022): 74. http://dx.doi.org/10.3390/w14010074.

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Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to co

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Zhang, Xiaohu, Jianxiu Qiu, Guoyong Leng, et al. "The Potential Utility of Satellite Soil Moisture Retrievals for Detecting Irrigation Patterns in China." Water 10, no. 11 (2018): 1505. http://dx.doi.org/10.3390/w10111505.

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Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to aid the detection of under-represented irrigation processes throughout China. The satellite retrievals used in this study include passive microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and its success

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Prigent, Catherine, Lise Kilic, Filipe Aires, Victor Pellet, and Carlos Jimenez. "Ice Concentration Retrieval from the Analysis of Microwaves: Evaluation of a New Methodology Optimized for the Copernicus Imaging Microwave Radiometer." Remote Sensing 12, no. 10 (2020): 1594. http://dx.doi.org/10.3390/rs12101594.

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A new methodology has been described in Kilic et al. (Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing 2020, 12, 1060, Part 1 of this study) to estimate Sea Ice Concentration (SIC) from satellite passive microwave observations between 6 and 36 GHz. The Ice Concentration Retrieval from the Analysis of Microwaves (IceCREAM) algorithm is based on an optimal estimation, with a simple radiative transfer model derived from satellite observations at 0% and 100% SIC. Observations at low and high freq

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Rahimzadegan, Majid, Arash Davari, and Ali Sayadi. "Estimating Regional Soil Moisture with Synergistic Use of AMSR2 and MODIS Images." Photogrammetric Engineering & Remote Sensing 87, no. 9 (2021): 649–60. http://dx.doi.org/10.14358/pers.20-00085.

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Soil moisture content (SMC), product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. The aim of this study is to introduce a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with a higher accuracy on a regional scale. Two MODIS products, including daily reflectance (MYD021) and nighttime land surface temperature (LST) products were used. In 2015, 1442 in situ SMC measurements from six stations in Iran were used as ground-truth data. Twenty mode

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Cai, Yu, Claude R. Duguay, and Chang-Qing Ke. "A 41-year (1979–2019) passive-microwave-derived lake ice phenology data record of the Northern Hemisphere." Earth System Science Data 14, no. 7 (2022): 3329–47. http://dx.doi.org/10.5194/essd-14-3329-2022.

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Abstract. Seasonal ice cover is one of the important attributes of lakes in middle- and high-latitude regions. The annual freeze-up and breakup dates as well as the duration of ice cover (i.e., lake ice phenology) are sensitive to the weather and climate; hence, they can be used as an indicator of climate variability and change. In addition to optical, active microwave, and raw passive microwave data that can provide daily observations, the Calibrated Enhanced-Resolution Brightness Temperature (CETB) dataset available from the National Snow and Ice Data Center (NSIDC) provides an alternate sou

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Wang, Jian, Lingmei Jiang, Kimmo Rautiainen, et al. "Daily High-Resolution Land Surface Freeze/Thaw Detection Using Sentinel-1 and AMSR2 Data." Remote Sensing 14, no. 12 (2022): 2854. http://dx.doi.org/10.3390/rs14122854.

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High-resolution surface freeze/thaw (F/T) information is valuable for hydrological, frost creep and gelifluction/solifluction, and climate prediction studies. Currently, large-scale, high-resolution F/T detection is restricted by low spatial resolution of passive microwave remote sensing sensors or low temporal resolution of synthetic aperture radar (SAR) data. In this study, we propose a new method for detecting daily land surface F/T state at 1 km spatial resolution by combining the Sentinel-1 radar and the Advanced Microwave Scanning Radiometer 2 (AMSR2) with leaf area index (LAI) data. A n

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Hoshino, Seita, Kazutaka Tateyama, and Koh Izumiyama. "Classification of Ice in Lützow-Holm Bay, East Antarctica, Using Data from ASCAT and AMSR2." Remote Sensing 12, no. 19 (2020): 3179. http://dx.doi.org/10.3390/rs12193179.

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This paper presents an ice classification algorithm based on combined active and passive microwave radiometer data in Lützow-Holm Bay (LHB), East Antarctica. The ice classification algorithm is developed based on the threshold values of an advanced scatterometer (ASCAT) and advanced microwave scanning radiometer 2 (here, AMSR2). These values are calculated via the features of various ice types, including open ice, first-year (FY) ice, multi-year (MY) ice, MY ice including icebergs (MY IB), ice shelves, coastal ice sheets, and inland ice sheets. To verify the validity of the ice classification

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Prasad, Siva, Igor Zakharov, Peter McGuire, Desmond Power, and Martin Richard. "Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST." Cryosphere 12, no. 12 (2018): 3949–65. http://dx.doi.org/10.5194/tc-12-3949-2018.

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Abstract. A multi-category numerical sea ice model CICE was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay and Labrador Sea. The assimilation of ice concentration was performed using the data derived from the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2). The model uses a mixed-layer slab ocean parameterization to compute the sea surface temperature (SST) and thereby to compute the freezing and melting potential of ice. The data from Advanced Very High Resolution Radiometer (AVHRR-only optimum interpolation analysis) were used to assimila

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Mu, Longjiang, Xi Liang, Qinghua Yang, Jiping Liu, and Fei Zheng. "Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017." Journal of Glaciology 65, no. 253 (2019): 813–21. http://dx.doi.org/10.1017/jog.2019.55.

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AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather

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Fan, Jiazhi, Man Luo, Qinzhe Han, Fulai Liu, Wanhua Huang, and Shiqi Tan. "Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China." PLOS ONE 17, no. 4 (2022): e0266091. http://dx.doi.org/10.1371/journal.pone.0266091.

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Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (

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Meissner, Thomas, and Andrew Manaster. "SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures." Remote Sensing 13, no. 24 (2021): 5120. http://dx.doi.org/10.3390/rs13245120.

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Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA/Remote Sensing Systems (RSS) SMAP SSS retrieval algorithm in regards to obtaining accurate SSS measurements in the polar oceans. Our analysis finds a strong correlation between 8-day averaged SMAP L-band brightness temperature (TB) bias and TB measurements from the Advanced Microwave Scanning Radiometer (AMSR2) in the C-through Ka-band frequency range f

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Tan, Jiancan, Nusseiba NourEldeen, Kebiao Mao, et al. "Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China." Sensors 19, no. 13 (2019): 2987. http://dx.doi.org/10.3390/s19132987.

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A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface

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Gao, Shuo, Zhen Li, Quan Chen, Wu Zhou, Mingsen Lin, and Xiaobin Yin. "Inter-Sensor Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface and First Result for Snow Water Equivalent Retrieval." Sensors 19, no. 22 (2019): 5023. http://dx.doi.org/10.3390/s19225023.

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The self-designed HaiYang-2B (HY-2B) satellite was launched on 24 October 2018 in China at 22:57 UT in a 99.34° inclination sun-synchronous orbit. The Scanning Microwave Radiometer (SMR) on the core observatory has the capability to provide near-real-time multi-channel brightness temperature (Tb) observations, which are designed mainly for improving the level of marine forecasting and monitoring, serving the development and utilization of marine resources. After internal calibration and ocean calibration, the first effort to retrieve land surface snow parameters was performed in this study, wh

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Zhao, Yili, Ping Liu, and Wu Zhou. "Inter-Comparison of SST Products from iQuam, AMSR2/GCOM-W1, and MWRI/FY-3D." Remote Sensing 16, no. 11 (2024): 2034. http://dx.doi.org/10.3390/rs16112034.

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Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the Microwave Radiation Imager (MWRI) aboard the Chinese Fengyun-3D satellite are intercompared utilizing extended triple collocation (ETC) and direct comparison methods. Additionally, error characteristic variations with respect to time, latitude, SST, sea surface wind speed, columnar wa

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