On Wednesday, August 23, from 10:30 a.m. to 12:00 p.m. (noon) (USA Mountain Time), AMSR-E, Aquarius, IceBridge, ICESat/GLAS, MEaSUREs, MODIS, NISE, SMAP, and VIIRS data will not be available due to system maintenance.

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.


Baldwin, D., et al. 2017. Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States. Journal of Hydrology 546: 393-404. doi: http://dx.doi.org/10.1016/j.jhydrol.2017.01.020.

Cai, Shanshan, et al. 2017. Examination of the impacts of vegetation on the correlation between snow water equivalent and passive microwave brightness temperature. Remote Sensing of Environment 193: 244-256. doi: http://dx.doi.org/10.1016/j.rse.2017.03.006.

Du, Jinyang, et al. 2017. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. The Cryosphere 11(1): 47–63. doi: http://dx.doi.org/10.5194/tc-11-47-2017.

Ermida, S. L., et al. 2017. Inversion of AMSR-E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature. Journal of Geophysical Research - Atmospheres 122(6): 3348–3360. doi: http://dx.doi.org/10.1002/2016JD026148.

Langlois, A., et al. 2017. Detection of rain-on-snow (ROS) events and ice layer formation using passive microwave radiometry: A context for Peary caribou habitat in the Canadian Arctic. Remote Sensing of Environment 189: 84-95. doi: http://dx.doi.org/10.1016/j.rse.2016.11.006.

Larue, Fanny, et al. 2017. Validation of GlobSnow-2 snow water equivalent over Eastern Canada. Remote Sensing of Environment 194: 264-277. doi: http://dx.doi.org/10.1016/j.rse.2017.03.027.

Lu, Xiaomei, et al. 2017. Observations of Arctic snow and sea ice cover from CALIOP lidar measurements. Remote Sensing of Environment 194: 248-263. doi: http://dx.doi.org/10.1016/j.rse.2017.03.046.

Marbà, Núria, et al. 2017. Climate change stimulates the growth of the intertidal macroalgae Ascophyllum nodosum near the northern distribution limit. Ambio 46(Supp1): 119-131. doi: http://dx.doi.org/10.1007/s13280-016-0873-7.

Ray, R. L. 2017. Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S.. Water 9(6). Art. #372. doi: http://dx.doi.org/10.3390/w9060372.

Ryu, Dongok, Sug-Whan Kim, and Robert P. Breault. 2017. New earth system model for optical performance evaluation of space instruments. Optics Express 25(5): 4926-4944. doi: http://dx.doi.org/10.1364/OE.25.004926.

Spreen, Gunnar, and Stefan Kern. 2017. Methods of satellite remote sensing of sea ice. Sea Ice. Somerset, NJ: Wiley, edited by David N. Thomas, 239-260.

Stammerjohn, Sharon, and Ted Maksym. 2017. Gaining (and Losing) Antarctic sea ice: variability, trends, and mechanisms. Sea Ice. Somerset, NJ: Wiley, edited by David N. Thomas, 261-289.

van der Schalie, R., et al. 2017. The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sensing of Environment 189: 180–193. doi: http://dx.doi.org/10.1016/j.rse.2016.11.026.

Vuyovich, Carrie M., et al. 2017. Effect of spatial variability of wet snow on modeled and observed microwave emissions. Remote Sensing of Environment 198: 310-320. doi: http://dx.doi.org/10.1016/j.rse.2017.06.016.

Yao, Panpan, et al. 2017. Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing 9(1). Art. #35. doi: http://dx.doi.org/10.3390/rs9010035.

Zhang, Lifu, et al. 2017. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment 190: 96-106. doi: http://dx.doi.org/\10.1016/j.rse.2016.12.010.

Zhang, Shugang, et al. 2017. A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery. Journal of Geoscience and Environment Protection, 5: 15-25. doi: http://dx.doi.org/10.4236/gep.2017.56003.

Zhuo, Lu, and Dawei Han. 2017. Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions. Advances in Meteorology 2017. Art. #1086456. doi: http://dx.doi.org/10.1155/2017/1086456.


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.

Baghdadi, Nicolas, and Mehrez Zribi. 2016. Land Surface Remote Sensing in Continental Hydrology. . San Diego: Elsevier; London: ISTE Press.

Boehme, Lars, et al. 2016. Bimodal Winter Haul-Out Patterns of Adult Weddell Seals (Leptonychotes weddellii) in the Southern Weddell Sea. PLOSone 11(5). Art. #e0155817. doi: http://dx.doi.org/10.1371/ journal.pone.0155817.

Bookhagen, Bodo. 2016. Glaciers and monsoon systems. Monsoon and Climate Change. New York, NY: Springer International Publishing, 225-249. doi: http://dx.doi.org/10.1007/978-3-319-21650-8_11.

Boori, Mukesh Singh, et al. 2016. Use of AMSR-E microwave satellite data for land surface characteristics and snow cover variation. Data in Brief 9: 1077–1089. doi: http://dx.doi.org/10.1016/j.dib.2016.11.006.

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.

Champagne, Catherine, et al. 2016. Satellite surface soil moisture from SMOS and Aquarius: Assessment for applications in agricultural landscapes. International Journal of Applied Earth Observation and Geoinformation 45B: 143-154. doi: http://dx.doi.org/10.1016/j.jag.2015.09.004.

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.

Coopersmith, Evan J., et al. 2016. Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation. Advances in Water Resources 98: 122-131. doi: http://dx.doi.org/10.1016/j.advwatres.2016.10.007.

Cui, Yaokui, et al. 2016. Evaluation of the FY-3B/MWRI soil moisture product on the central Tibetan Plateau. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 1655-1658. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729423.

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, et al. 2016. Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS. Remote Sensing of Environment 184: 469–4819. doi: http://dx.doi.org/10.1016/j.rse.2016.07.02.

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.

Han, Menglei, Hui Lui, and Kun Yang. 2016. Development of Passive Microwave Retrieval Algorithm for Estimation of Surface Soil Temperature from AMSR-E Data. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 1671-1674. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729427.

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.

Holland, Paul R., and Noriaki Kimura. 2016. Observed Concentration Budgets of Arctic and Antarctic Sea Ice. Journal of Climate 29(14): 5241–5249. doi: http://dx.doi.org/10.1175/JCLI-D-16-0121.1.

Holmes, Thomas R. H., et al. 2016. Cloud tolerance of remote-sensing technologies to measure land surface temperature. Hydrology and Earth System Sciences 20(8): 3263-3275. doi: http://dx.doi.org/10.5194/hess-20-3263-2016.

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.

Jeong, Dae Il, Laxmi Sushama, and M. Naveed Khaliq. 2016. Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects. Climate Dynamics 48(11-12): 3645-3658. doi: http://dx.doi.org/10.1007/s00382-016-3291-4.

Jia, Binghao, and Z. Xie. 2016. Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach. Applied Mathematics and Mechanics (English Edition). doi: http://dx.doi.org/10.1007/s10483-016-2103-6.

Kang, Do-Hyuk, Ana P. Barros, and Edward J. Kim. 2016. Evaluating Multispectral Snowpack Reflectivity With Changing Snow Correlation Lengths. IEEE Transactions on Geoscience and Remote Sensing 54(12): 7378-7384. doi: http://dx.doi.org/10.1109/TGRS.2016.2600958.

Karthikeyan, L., and Kumar, D. Nagesh. 2016. A novel approach to validate satellite soil moisture retrievals using precipitation data. Journal of Geophysical Research - Atmospheres 121(19): 11,516-11,535. doi: http://dx.doi.org/10.1002/2016JD024829.

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.

Kern, Stefan, et al. 2016. The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations. Cryosphere 10(5): 2217-2239. doi: http://dx.doi.org/10.5194/tc-10-2217-2016.

Kerr, Y. H., et al. 2016. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment 180: 40-63. doi: http://dx.doi.org/10.1016/j.rse.2016.02.042.

Khvorostovsky, Kirill, and Pierre Rampal. 2016. On retrieving sea ice freeboard from ICESat laser altimeter. The Cryosphere 10(5): 2329-2346. doi: http://dx.doi.org/10.5194/tc-10-2329-2016.

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.

Li, Lele, Haihua Chen, and Lei Guan. 2016. Study on the Retrieval of Snow Depth from FY3B/MWRI in the Atctic [sic]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8: 513-520. doi: http://dx.doi.org/10.5194/isprsarchives-XLI-B8-513-2016.

Lu, H., K. Yang, and J. Shi. 2016. Constraining the water imbalance in a land data assimilation system through a recursive assimilation scheme. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ) 2993-2996,. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729773.

Luojus, Kari, et al. 2016. Assessing global satellite-based snow water equivalent datasets in ESA SnowPEx project. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 5284-5287. doi: http://dx.doi.org/10.1109/IGARSS.2016.7730376.

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.

Mattar, Cristian, et al. 2016. The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance. Data 1(1). Art. #6. doi: http://dx.doi.org/10.3390/data1010006.

Michel, D., et al. 2016. The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms . Hydrology and Earth System Sciences 20: 803-822. doi: http://dx.doi.org/10.5194/hess-20-803-2016.

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.

Naud, C., J. Booth, and A. Del Genio. 2016. The Relationship between Boundary Layer Stability and Cloud Cover in the Post-Cold-Frontal Region. . Journal of Climate 29(11): 8129-8149. doi: http://dx.doi.org/10.1175/JCLI-D-15-0700.1.

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.

Parinussa, Robert M., et al. 2016. A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input. Climate 4(4). Art. #50. doi: http://dx.doi.org/10.3390/cli4040050.

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. L. 2016. Moisture Stress Indicators in Giant Sequoia Groves in the Southern Sierra Nevada of California, USA. Vadose Zone Journal 15(10). doi: http://dx.doi.org/10.2136/vzj2016.03.0018.

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.

Rodríguez-Fernández, Nemesio J., et al. 2016. Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data. Remote Sensing 8(11). Art. #959. doi: http://dx.doi.org/10.3390/rs8110959.

Ruan, Yongjian, et al. 2016. Passive microwave remote sensing of lake freeze-thaw over High Mountain Asia. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 2818-2821 . doi: http://dx.doi.org/10.1109/IGARSS.2016.7729728.

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.

Scott, M. Chance, and Shouraseni Sen Roy. 2016. Global Emissivity Distribution and Change from 2003 to 2007. The Professional Geographer 68(3): 368-379. doi: http://dx.doi.org/10.1080/00330124.2015.1089131.

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.

Su, Yanjun, et al. 2016. Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures. International Journal of Digital Earth. doi: http://dx.doi.org/10.1080/17538947.2016.1247472.

Tanaka, Yasuhiro, et al. 2016. Estimation of melt pond fraction over high-concentration Arctic sea ice using AMSR-E passive microwave data. Journal of Geophysical Research - Oceans 121(9): 7056-7072. doi: http://dx.doi.org/10.1002/2016JC011876.

Tang, Yi, Yonghong Yi, and Wenjiang Zhang. 2016. Land surface temperature retrieval using AMSR-E data in the Central Tibetan Plateau. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 4886-4889. doi: http://dx.doi.org/10.1109/IGARSS.2016.7730275.

Tedesco, Marco, and Jeyavinoth Jeyaratnam. 2016. A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures. Remote Sensing 8(12): Art. #1037. doi: http://dx.doi.org/10.3390/rs8121037.

Thiruvengadam, P., and Y. S. Rao. 2016. Spatio-temporal variation of soil moisture and drought monitoring using passive microwave remote sensing. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 3126-3129. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729808.

van Beest, Floris M., et al. 2016. Spatiotemporal variation in home range size of female polar bears and correlations with individual contaminant load. Polar Biology 39(8): 1479-1489. doi: http://dx.doi.org/10.1007/s00300-015-1876-8.

Wang, Wei, et al. 2016. Accuracy analysis for an improved daily cloud-free snow cover product on Tibetan Plateau. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 4909-4912 . doi: http://dx.doi.org/10.1109/IGARSS.2016.7730281.

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.

Yao, Yunjun, et al. 2016. Satellite evidence for no change in terrestrial latent heat flux in the Three-River Headwaters region of China over the past three decades. Journal of Earth System Science 125(6): 1245–1253. doi: http://dx.doi.org/10.1007/s12040-016-0732-8.

Yi, Donghui, et al. 2016. Antarctic sea-ice freeboard and estimated thickness from NASA's ICESat and IceBridge observations. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 7682-7685. doi: http://dx.doi.org/10.1109/IGARSS.2016.7731003.

Yuan, Jian 2016. Variability of oceanic deep convective system vertical structures observed by CloudSat in Indo-Pacific regions associated with the Madden-Julian oscillation. Journal of Geophysical Research - Atmospheres 121(18): 10,761–10,785. doi: http://dx.doi.org/10.1002/2016JD025262.

Zhang, W., et al. 2016. A new algorithm for soil moisture retrieval using C and K-band Radiometer channels of ocean salinity satellite. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 2997-3000. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729774.

Zhao, Long, Zong-Liang Yang, and Timothy J. Hoar. 2016. Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System. Journal of Hydrometeorology 17(9): 2431–2454. doi: http://dx.doi.org/10.1175/JHM-D-15-0218.1.

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.

Zheng, Wei, D. Sun, and S. Li. 2016. Coastal flood monitoring based on AMSR-E data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ), 4399-4401. doi: http://dx.doi.org/10.1109/IGARSS.2016.7730146.


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.