Published Research

The following references cite studies that used AMSRE data from NSIDC. Please contact User Services if you have a reference you would like to share on this page.

2016

Al-Yaari, A., et al. 2016. Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations. Remote Sensing of Environment 180: 453–464. doi: http://dx.doi.org/10.1016/j.rse.2015.11.022.

Chakraborty, Abhishek, M. V. R. Seshasai, and V. K. Dadhwal. 2016. Assessing crop water stress during late kharif season using Normalized Diurnal Difference Vegetation Water Content (nddVWC) of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E). Natural Hazards. doi: http://dx.doi.org/10.1007/s11069-016-2438-2.

Che, Tao, et al. 2016. Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China. Remote Sensing of Environment 183: 334-349. doi: http://dx.doi.org/10.1016/j.rse.2016.06.005.

Colón-González, Felipe J., et al. 2016. Assessing the Effects of Air Temperature and Rainfall on Malaria Incidence: An Epidemiological Study Across Rwanda and Uganda. Geospatial Health 11(1s). doi: http://dx.doi.org/10.4081/gh.2016.379.

Dolant, Caroline, et al. 2016. Development of a rain-on-snow detection algorithm using passive microwave radiometry. Hydrological Processes. doi: http://dx.doi.org/10.1002/hyp.10828.

Du, Jinyang, J. S. Kimball, and L. A. Jones. 2016. Passive Microwave Remote Sensing of Soil Moisture Based on Dynamic Vegetation Scattering Properties for AMSR-E. IEEE Transactions on Geoscience and Remote Sensing 54(1): 597-608. doi: http://dx.doi.org/10.1109/TGRS.2015.2462758.

Dziubanski, David J., and Kristie J. Franz. 2016. Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model. Journal of Hydrology 540: 26-39. doi: http://dx.doi.org/10.1016/j.jhydrol.2016.05.046.

Eastman, Ryan, and Robert Wood. 2016. Factors Controlling Low-Cloud Evolution over the Eastern Subtropical Oceans: A Lagrangian Perspective Using the A-Train Satellites. Journal of the Atmospheric Sciences 73(1): 331-351. doi: http://dx.doi.org/10.1175/JAS-D-15-0193.1.

Gevaert, A. I., et al. 2016. Spatio-temporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth. International Journal of Applied Earth Observation and Geoinformation 45(pt.B): 235-244. doi: http://dx.doi.org/10.1016/j.jag.2015.08.006.

Hirano, Daisuke, et al. 2016. A wind-driven, hybrid latent and sensible heat coastal polynya off Barrow, Alaska. Journal of Geophysical Research - Oceans 121(1): 980-997. doi: http://dx.doi.org/10.1002/2015JC011318.

Ivanova, Natalia, Pierre Rampal, and Sylvain Bouillon. 2016. Error assessment of satellite-derived lead fraction in the Arctic. The Cryosphere 10: 585-595. doi: http://dx.doi.org/10.5194/tc-10-585-2016.

Kern, Stefan, and Burcu Ozsoy-Çiçek. 2016. Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches. Remote Sensing 8(6). Art. #450. doi: http://dx.doi.org/10.3390/rs8060450.

Kou, Xiaokang, et al. 2016. Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sensing 8(2). Art. #105. doi: http://dx.doi.org/10.3390/rs8020105.

Lalande, Catherine, et al. 2016. Lateral supply and downward export of particulate matter from upper waters to the seafloor in the deep eastern Fram Strait. Deep-Sea Research Part I - Oceanographic Research Papers 114: 78–89. doi: http://dx.doi.org/10.1016/j.dsr.2016.04.014.

Lee, Yoon Chang, et al. 2016. Taxonomic variability of phytoplankton and relationship with production of CDOM in the polynya of the Amundsen Sea, Antarctica. Deep-Sea Research Part II - Topical Studies in Oceanography 123: 30-41. doi: http://dx.doi.org/10.1016/j.dsr2.2015.09.002.

Mace, G. G., and A. C. Abernathy. 2016. Observational evidence for aerosol invigoration in shallow cumulus downstream of Mount Kilauea. Geophysical Research Letters 43(6): 2981–2988. doi: http://dx.doi.org/10.1002/2016GL067830.

Mai, Mingrun, et al. 2016. Application of AMSR-E and AMSR2 Low-Frequency Channel Brightness Temperature Data for Hurricane Wind Retrievals. IEEE Transactions on Geoscience and Remote Sensing 54(8): 4501 - 4512. doi: http://dx.doi.org/10.1109/TGRS.2016.2543502.

Miralles, D. G., et al. 2016. The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets. Hydrology and Earth System Sciences 20: 823-842. doi: http://dx.doi.org/10.5194/hess-20-823-2016.

Ohshima, Kay I., Sohey Nihashi, and Katsushi Iwamoto. 2016. Global view of sea-ice production in polynyas and its linkage to dense/bottom water formation. Geoscience Letters 3. Art. #13. doi: http://dx.doi.org/10.1186/s40562-016-0045-4.

Rajib, Mohammad Adrian, Venkatesh Merwadea, and Zhiqiang Yub. 2016. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture. Journal of Hydrology 536: 192-207. doi: http://dx.doi.org/10.1016/j.jhydrol.2016.02.037.

Ray, R., et al. 2016. Integrating Runoff Generation and Flow Routing in Susquehanna River Basin to Characterize Key Hydrologic Processes Contributing to Maximum Annual Flood Events. Journal of Hydrologic Engineering 4. Art. #04016026. doi: http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0001389.

Santi, Emanuele, et al. 2016. Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation 48(1): 61-73. doi: http://dx.doi.org/10.1016/j.jag.2015.08.002.

Santi, Emanuele, et al. 2016. Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(6): 2478 - 2492. doi: http://dx.doi.org/10.1109/JSTARS.2016.2575361.

Shwetha, H. R., and D. Nagesh Kumar. 2016. Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave. ISPRS Journal of Photogrammetry and Remote Sensing 117: 40–55. doi: http://dx.doi.org/10.1016/j.isprsjprs.2016.03.011.

Smith, Taylor, and Bodo Bookhagen. 2016. Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia. Remote Sensing of Environment 181: 174-185. doi: http://dx.doi.org/10.1016/j.rse.2016.03.037.

Stillman, Susan, and Xubin Zeng. 2016. Evaluation of 22 Precipitation and 23 Soil Moisture Products over a Semiarid Area in Southeastern Arizona. Journal of Hydrometeorology 17(1): 211-230. doi: http://dx.doi.org/10.1175/JHM-D-15-0007.1.

Wu, Bingfang, et al. 2016. An Improved Method for Deriving Daily Evapotranspiration Estimates From Satellite Estimates on Cloud-Free Days. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(4): 1323-1330. doi: http://dx.doi.org/10.1109/JSTARS.2015.2514121.

Xie, Hongjie, et al. 2016. Remote sensing mapping and modeling of snow cover parameters and applications. Remote sensing of water resources, disasters, and urban studies. Boca Raton, FL: CRC Press, 259-288.

Yang, Kun, et al. 2016. Land surface model calibration through microwave data assimilation for improving soil moisture simulations. Journal of Hydrology 533: 266-276. doi: http://dx.doi.org/10.1016/j.jhydrol.2015.12.018.

Zhao, X., et al. 2016. A case study of a transported bromine explosion event in the Canadian high arctic. Journal of Geophysical Research - Atmospheres 121(1): 457-477. doi: http://dx.doi.org/10.1002/2015JD023711.

2015

Al Jassar, Hala Khalid, and and Kota Sivasankara Rao. 2015. AMSR-E remote sensing data analysis over Kuwait desert. Kuwait Journal of Science 42(2): 250-260.

Beighley, R. E., et al. 2015. A hydrologic routing model suitable for climate-scale simulations of arctic rivers: application to the Mackenzie River Basin. Hydrological Processes 29(12): 2751-2768. doi: http://dx.doi.org/10.1002/hyp.10398.

Beitsch, A., S. Kern, and L. Kaleschke. 2015. Comparison of SSM/I and AMSR-E Sea Ice Concentrations With ASPeCt Ship Observations Around Antarctica. IEEE Transactions on Geoscience and Remote Sensing 53(4): 1985-1996. doi: http://dx.doi.org/10.1109/TGRS.2014.2351497.

Berezowski, Tomasz, Jarosław Chormańskia, and Okke Batelaan 2015. Skill of remote sensing snow products for distributed runoff prediction. Journal of Hydrology 524: 718-732. doi: http://dx.doi.org/10.1016/j.jhydrol.2015.03.025.

Bhagat, Vijay S. 2015. Space-borne Passive Microwave Remote Sensing of Soil Moisture: A Review . Recent Progress in Space Technology 4: 119-150.

Bi, Haiyun, Jianwen Ma, and Fangjian Wang. 2015. An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(2): 447-459. doi: http://dx.doi.org/10.1109/JSTARS.2014.2322096.

Boccolari, Mauro, and and Flavio Parmiggiani. 2015. On the measure of sea ice area from sea ice concentration data sets. Proc. SPIE 9638, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2015 9638. Art. #963804. Bellingham, WA: SPIE. doi: http://dx.doi.org/10.1117/12.2194087.

Boori, Mukesh, and Ralph Ferraro. 2015. Global Land Cover Classification Based on Microwave Polarization and Gradient Ratio (MPGR). Geoinformatics for Intelligent Transportation. New York, NY: Springer International Publishing, 17-37. doi: http://dx.doi.org/10.1007/978-3-319-11463-7_2.

Bradley, Alice C., et al. 2015. Air-Deployed Microbuoy Measurement of Temperatures in the Marginal Ice Zone Upper Ocean during the MIZOPEX Campaign. Journal of Atmospheric and Oceanic Technology 32(5): 1058-1070. doi: http://dx.doi.org/10.1175/JTECH-D-14-00209.1.

Brown, Zachary W., et al. 2015. Aspects of the marine nitrogen cycle of the Chukchi Sea shelf and Canada Basin. Deep-Sea Research Part II - Topical Studies in Oceanography 118A: 73-87. doi: http://dx.doi.org/10.1016/j.dsr2.2015.02.009.

Cho, Eunsang, Heewon Moon, and Minha Choi. 2015. First Assessment of the Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Contents in Northeast Asia. Journal of the Meteorological Society of Japan 93(1): 117-129. doi: http://dx.doi.org/10.2151/jmsj.2015-008.

Cho, Eunsang, Minha Choi, and Wolfgang Wagner. 2015. An assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast Asia. Remote Sensing of Environment 160: 166-179. doi: http://dx.doi.org/10.1016/j.rse.2015.01.013.

Dai, Liyun, Tao Che, and Yongjian Ding 2015. Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China. Remote Sensing 7(6): 7212-7230. doi: http://dx.doi.org/10.3390/rs70607212.

De Silva, Liyanarachchi, Hajime Yamaguchi, and Jun Ono. 2015. Ice - ocean coupled computations for sea-ice prediction to support ice navigation in Arctic sea routes. Polar Research 34. Art. #25008. doi: http://dx.doi.org/10.3402/polar.v34.25008.

Djamai, Najib, et al. 2015. Evaluation of SMOS soil moisture products over the CanEx-SM10 area. Journal of Hydrology 520: 254-267. doi: http://dx.doi.org/10.1016/j.jhydrol.2014.11.026.

Du, Jinyang, et al. 2015. Satellite Microwave Retrieval of Total Precipitable Water Vapor and Surface Air Temperature Over Land From AMSR2. IEEE Transactions on Geoscience and Remote Sensing 53(5): 2520-2531. doi: http://dx.doi.org/10.1109/TGRS.2014.2361344.

Duan, Si-Bo, et al. 2015. Generation of an all-weather land surface temperature product from MODIS and AMSR-E data. Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015. Art. #980816. doi: http://dx.doi.org/10.1117/12.2207848.

Dziak, Robert P., et al. 2015. Sources and Levels of Ambient Ocean Sound near the Antarctic Peninsula. PLOS One 10(4). Art. #e0123425. doi: http://dx.doi.org/10.1371/journal.pone.0123425.

Feng, Huihui, and Yuanbo Liu. 2015. Combined effects of precipitation and air temperature on soil moisture in different land covers in a humid basin. Journal of Hydrology 531(pt. 3): 1129-1140. doi: http://dx.doi.org/10.1016/j.jhydrol.2015.11.016.

Fournier, S., et al. 2015. Comparison of spaceborne measurements of sea surface salinity and colored detrital matter in the Amazon plume. JiyebK of Geophysical Research - Oceans 120(5): 3177–3192. doi: http://dx.doi.org/10.1002/2014JC010109.

Frey, Karen E., et al. 2015. Divergent patterns of recent sea ice cover across the Bering, Chukchi, and Beaufort seas of the Pacific Arctic Region. Progress in Oceanography 136: 32-49. doi: http://dx.doi.org/10.1016/j.pocean.2015.05.009.

Good, Elizabeth. 2015. Daily minimum and maximum surface air temperatures from geostationary satellite data. Journal of Geophysical Research - Atmospheres 120(6): 2306-2324. doi: http://dx.doi.org/10.1002/2014JD022438.

Guo, Bing, et al. 2015. An estimation method of soil freeze-thaw erosion in the Qinghai–Tibet Plateau. Natural Hazards : 1808-1823. doi: http://dx.doi.org/10.1007/s11069-015-1808-5.

Gupta, Mukesh, and David G. Barber. 2015. Sub-pixel evaluation of sea ice roughness using AMSR-E data. International Journal of Remote Sensing 36(3): 749-763. doi: http://dx.doi.org/10.1080/01431161.2014.1001081.

Haizhu, Pan, Wang Jian, and Li Hongyi. 2015. Accuracy validation of the MODIS snow albedo products and estimate of the snow albedo under cloud over the Qilian Mountains. Journal of Glaciology and Geocryology 37(1): 49-57. doi: http://dx.doi.org/10.7522/j.issn.1000-0240.2015.0005.

Han, Menglei, et al. 2015. An Algorithm Based on the Standard Deviation of Passive Microwave Brightness Temperatures for Monitoring Soil Surface Freeze/Thaw State on the Tibetan Plateau. IEEE Transactions on Geoscience and Remote Sensing 53(5): 2775-2783. doi: http://dx.doi.org/10.1109/TGRS.2014.2364823.

Han, Xiao-Jing, et al. 2015. Comparison of AMSR-E soil moisture product and ground-based measurement over agricultural areas in China. IGARSS 2015. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 673 - 676. doi: http://dx.doi.org/10.1109/IGARSS.2015.7325853.

Han, Xiao-Jing, et al. 2015. Evaluation of temporal variations in soil moisture based on the microwave polarization difference index using in situ data over agricultural areas in China. International Journal of Remote Sensing. doi: http://dx.doi.org/10.1080/01431161.2015.1043161.

Hao, Cui, Jiahua Zhang, and Fengmei Yao. 2015. Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation 35B: 270-283. doi: http://dx.doi.org/10.1016/j.jag.2014.09.011.

Harsem, Øistein, et al. 2015. Oil Exploration and Sea Ice Projections in the Arctic. Polar Record 51(1): 91-106. doi: http://dx.doi.org/10.1017/S0032247413000624.

He, Binbin, et al. 2015. A Global Grassland Drought Index (GDI) Product: Algorithm and Validation. Remote Sensing 7(10): 12704-12736. doi: http://dx.doi.org/10.3390/rs71012704.

Hsu, C., et al. 2015. Downscaling Advanced Microwave Scanning Radiometer (AMSR-E) Soil Moisture Retrievals Using a Multiple Time-Scale Exponential Rainfall Adjustment Technique. Journal of Geophysics and Remote Sensing 4(139): 1-15. doi: http://dx.doi.org/10.4172/2169-0049.1000139.

Hu, Guangcheng, and Li Jia. 2015. Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations. Remote Sensing 7(3): 3056-3087. doi: http://dx.doi.org/10.3390/rs70303056.

Hutchings, Jennifer K., and Donald K. Perovich. 2015. Preconditioning of the 2007 sea-ice melt in the eastern Beaufort Sea, Arctic Ocean. Annals of Glaciology 56(69): 94-98. doi: http://dx.doi.org/10.3189/2015AoG69A006.

Islam, Tanvir, et al. 2015. An introduction to factor analysis for radio frequency interference detection on satellite observations. Meteorological Applications 22(3): 436–443. doi: http://dx.doi.org/10.1002/met.1473.

Ito, Masato, et al. 2015. Observations of supercooled water and frazil ice formation in an Arctic coastal polynya from moorings and satellite imagery. Annals of Glaciology 56(69): 307-314. doi: http://dx.doi.org/10.3189/2015AoG69A839.

Ivan, Igor, et al. 2015. Geoinformatics for Intelligent Transportation. . New York, NY: Springer International Publishing. doi: http://dx.doi.org/10.1007/978-3-319-11463-7.

Ivanova, N., et al. 2015. Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations. The Cryosphere 9: 1797-1817. doi: http://dx.doi.org/10.5194/tc-9-1797-2015.

Kern, S., et al. 2015. The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise. The Cryosphere 9(1): 37-52. doi: http://dx.doi.org/10.5194/tc-9-37-2015.

Kim, Miae, et al. 2015. Landfast sea ice monitoring using multisensor fusion in the Antarctic. GIScience & Remote Sensing 52(2): 239-256. doi: http://dx.doi.org/10.1080/15481603.2015.1026050.

Kongoli, C., and and S. Helfrich. 2015. A multi-source interactive analysis approach for Northern hemispheric snow depth estimation. IGARSS 2015. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 770-773. doi: http://dx.doi.org/10.1109/IGARSS.2015.7325878.

Kostyuchenko, Yuriy V. 2015. Infrastructure Vulnerability Assessment Toward Extreme Meteorological Events Using Satellite Data. Numerical Methods for Reliability and Safety Assessment: Multiscale and Multiphysics Systems. Zurich: Springer International Publishing Switzerland, 425-438. doi: http://dx.doi.org/10.1007/978-3-319-07167-1__15.

La, Hyoung Sul, et al. 2015. High density of ice krill (Euphausia crystallorophias) in the Amundsen sea coastal polynya, Antarctica. Deep Sea Research Part I: Oceanographic Research Papers 95: 75-84. doi: http://dx.doi.org/10.1016/j.dsr.2014.09.002.

Lacava, Teodosio, et al. 2015. Integration of Optical and Passive Microwave Satellite Data for Flooded Area Detection and Monitoring. Engineering Geology for Society and Territory – Volume 3. Zurich: Springer International Publishing Switzerland, 631-635.. doi: http://dx.doi.org/10.1007/978-3-319-09054-2_126.

Lakshmi, Venkat, et al. 2015. Remote sensing of the terrestrial water cycle. . Washington, DC: AGU Pub. Committee & Wiley.

Leck, C., and and E. Svensson. 2015. Importance of aerosol composition and mixing state for cloud droplet activation over the Arctic pack ice in summer. Atmospheric Chemistry and Physics 15: 2545-2568. doi: http://dx.doi.org/10.5194/acp-15-2545-2015.

Lecomte, Olivier, et al. 2015. Interactions between wind-blown snow redistribution and melt ponds in a coupled ocean–sea ice model. Ocean Modelling 87: 67-80. doi: http://dx.doi.org/0.1016/j.ocemod.2014.12.003.

Lee, Sang-Moo, et al. 2015. Retrieving the refractive index, emissivity, and surface temperature of polar sea ice from 6.9GHz microwave measurements: A theoretical development. Journal of Geophysical Research - Atmospheres 120(6): 2293-2305. doi: http://dx.doi.org/10.1002/2014JD022481.

Lee, Yong-Keun, Cezar Kongoli, and Jeffrey Key. 2015. 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: 2319–2336. doi: http://dx.doi.org/10.1175/JTECH-D-15-0100.1.

Li, Yunqing, et al. 2015. The Development of Microwave Vegetation Indices from WindSat Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(9): 4379-4395. doi: http://dx.doi.org/10.1109/JSTARS.2015.2423153.

Liang, Jiayong, et al. 2015. Improved snow depth retrieval by integrating microwave brightness temperature and visible/infrared reflectance. Remote Sensing of Environment 158: 500-509. doi: http://dx.doi.org/10.1016/j.rse.2014.10.016.

Liu, Yuqiong, et al. 2015. Blending satellite-based snow depth products with in situ observations for streamflow predictions in the Upper Colorado River Basin. Water Resources Research 51(2): 1182-1202. doi: http://dx.doi.org/10.1002/2014WR016606.

Louvet, Samuel, et al. 2015. SMOS soil moisture product evaluation over West-Africa from local to regional scale. Remote Sensing of Environment 156: 383-394. doi: http://dx.doi.org/10.1016/j.rse.2014.10.005.

Lowry, Kate E., et al. 2015. The influence of winter water on phytoplankton blooms in the Chukchi Sea. Deep-Sea Research Part II - Topical Studies in Oceanography 118A: 53–72. doi: http://dx.doi.org/10.1016/j.dsr2.2015.06.006.

Macander, Matthew J., et al. 2015. Landsat-based snow persistence map for northwest Alaska. Remote Sensing of Environment 163: 23-31. doi: http://dx.doi.org/10.1016/j.rse.2015.02.028.

MacIntyre, Kalyn Q., et al. 2015. The relationship between sea ice concentration and the spatio-temporal distribution of vocalizing bearded seals (Erignathus barbatus) in the Bering, Chukchi, and Beaufort Seas from 2008 to 2011. Progress in Oceanography 136: 241-249. doi: http://dx.doi.org/10.1016/j.pocean.2015.05.008.

Makynen, M., et al. 2015. Thin Ice Detection in the Barents and Kara Seas With AMSR-E and SSMIS Radiometer Data. IEEE Transactions on Geoscience and Remote Sensing 53(9): 5036-5053. doi: http://dx.doi.org/10.1109/TGRS.2015.2416393.

Masina, Simona 2015. An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics. doi: http://dx.doi.org/10.1007/s00382-015-2728-5.

Maurer, Vera, Norbert Kalthoff, and Leonhard Gantner 2015. Predictability of convective precipitation for West Africa: Does the land surface influence ensemble variability as much as the atmosphere?. Atmospheric Research 157: 91-107. doi: http://dx.doi.org/10.1016/j.atmosres.2015.01.016.

McNyset, Kristina M., Carol J. Volk, and Chris E. Jordan. 2015. Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures. Water 7(12): 6827-6846. doi: http://dx.doi.org/10.3390/w7126660.

Meyers, Patrick C., Ralph R. Ferraro, and Nai-Yu Wang 2015. Updated Screening Procedures for GPROF2010 over Land: Utilization for AMSR-E. Journal of Atmospheric and Oceanic Technology 32: 1015-1028. doi: http://dx.doi.org/10.1175/JTECH-D-14-00149.1.

Mishra, Ashok K., et al. 2015. Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought . Journal of Hydrology 526: 15-29. doi: http://dx.doi.org/10.1016/j.jhydrol.2014.10.038.

Muskett, Reginald R., et al. 2015. Active-Layer Soil Moisture Content Regional Variations in Alaska and Russia by Ground-Based and Satellite-Based Methods, 2002 through 2014. International Journal of Geosciences 6(1): 12-41. doi: http://dx.doi.org/10.4236/ijg.2015.61002.

Nakata, Kazuki, et al. 2015. Variability and ice production budget in the Ross Ice Shelf Polynya based on a simplified polynya model and satellite observations. Journal of Geophysical Research - Oceans 120(9): 6234-6252. doi: http://dx.doi.org/10.1002/2015JC010894.

Nihashi, Sohey, and Kay I. Oshima. 2015. Circumpolar Mapping of Antarctic Coastal Polynyas and Landfast Sea Ice: Relationship and Variability. Journal of Climate 28(9): 3650-3670. doi: http://dx.doi.org/10.1175/JCLI-D-14-00369.1.

Norouzi, H., et al. 2015. Assessment of the consistency among global microwave land surface emissivity products. Atmospheric Measurement Techniques 8: 1197-1205. doi: http://dx.doi.org/10.5194/amt-8-1197-2015.

Norouzi, H., et al. 2015. Consistency analysis among microwave land surface emissivity products to improve GPROF precipitation estimations. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 939 - 942. doi: http://dx.doi.org/10.1109/IGARSS.2015.7325921.

Norouzi, Hamidreza, et al. 2015. Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures. Physics and Chemistry of the Earth, Parts A/B/C 83-84: 28-35. doi: http://dx.doi.org/10.1016/j.pce.2015.01.007.

Oltmanns, Marilena. 2015. Strong wind events across Greenland's coast and their influence on the ice sheet, sea ice and ocean. . MIT. Ph D.

Paloscia, Simonetta, et al. 2015. A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data. Frontiers in Earth Science 3. 00016. doi: http://dx.doi.org/10.3389/feart.2015.00016.

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