Published Research

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

2017

Ahonen, Heidi, et al. 2017. The underwater soundscape in western Fram Strait: Breeding ground of Spitsbergen's endangered bowhead whales. Marine Pollution Bulletin 123(1-2): 97-112. doi: http://dx.doi.org/10.1016/j.marpolbul.2017.09.019.

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.

Boccolari, Mauro, and Flavio Parmiggiani. 2017. Sea-ice area variability and trends in Arctic sectors of different morphology, 1996–2015. European Journal of Remote Sensing 50(1): 377-383. doi: http://dx.doi.org/10.1080/22797254.2017.1331117.

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.

Cheng, Zian, et al. 2017. Spatio-Temporal Variability and Model Parameter Sensitivity Analysis of Ice Production in Ross Ice Shelf Polynya from 2003 to 2015. Remote Sensing 9(9). Art. #934. doi: http://dx.doi.org/10.3390/rs9090934.

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.

Duan, Si-Bo, Zhao-Liang Li, and Pei Leng. 2017. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sensing of Environment 195: 107-117. doi: http://dx.doi.org/10.1016/j.rse.2017.04.008.

Eastman, Ryan, Robert Wood, and Kuan Ting O. 2017. The Subtropical Stratocumulus-Topped Planetary Boundary Layer: A Climatology and the Lagrangian Evolution . Journal of the Atmospheric Sciences 74(8): 331-351. doi: http://dx.doi.org/10.1175/JAS-D-16-0336.1.

Eastman, Ryan, Robert Wood, and Kuan Ting O. 2017. The Subtropical Stratocumulus-Topped Planetary Boundary Layer: A Climatology and the Lagrangian Evolution. Journal of the Atmospheric Sciences 74(8): 331-351. doi: http://dx.doi.org/10.1175/JAS-D-16-0336.1.

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.

Feng, Xiaoming, et al. 2017. Evaluation of AMSR-E retrieval by detecting soil moisture decrease following massive dryland re-vegetation in the Loess Plateau, China. Remote Sensing of Environment 196: 253-264. doi: http://dx.doi.org/10.1016/j.rse.2017.05.012.

Fukamachi, Yasushi, et al. 2017. Sea-ice thickness in the coastal northeastern Chukchi Sea from moored ice-profiling sonar. Journal of Glaciology 63(241): 888-898. doi: http://dx.doi.org/10.1017/jog.2017.56.

Han, Menglei, et al. 2017. A surface soil temperature retrieval algorithm based on AMSR-E multi-frequency brightness temperatures. International Journal of Remote Sensing 38(23). doi: http://dx.doi.org/10.1080/01431161.2017.1363438.

Jiménez, C., et al. 2017. Inversion of AMSR-E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature. Journal of Geophysical Research - Atmospheres 122(6): 3330–3347. doi: http://dx.doi.org/10.1002/2016JD026148.

Kolassa, J., R.H.Reichle, and C.S.Draper. 2017. Merging active and passive microwave observations in soil moisture data assimilation. Remote Sensing of Environment 191: 117-130. doi: http://dx.doi.org/10.1016/j.rse.2017.01.015.

Kou, Xiaokang, et al. 2017. Detection of land surface freeze-thaw status on the Tibetan Plateau using passive microwave and thermal infrared remote sensing data. Remote Sensing of Environment 199: 291-301. doi: http://dx.doi.org/10.1016/j.rse.2017.06.035.

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.

Li, Quinghuan, and Richard E. J. Kelly. 2017. Correcting Satellite Passive Microwave Brightness Temperatures in Forested Landscapes Using Satellite Visible Reflectance Estimates of Forest Transmissivity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(9): 3874-3883. doi: http://dx.doi.org/10.1109/JSTARS.2017.2707545.

Liu, Di, et al. 2017. Performance of SMAP, AMSR-E and LAI for weekly agricultural drought forecasting over continental United States. Journal of Hydrology 553: 88-104. doi: http://dx.doi.org/10.1016/j.jhydrol.2017.07.049.

Liu, Liyang, et al. 2017. The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sensing of Environment 199: 302-320. doi: http://dx.doi.org/10.1016/j.rse.2017.07.012.

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.

Meier, Walter N., and Alvro Ivanoff. 2017. Intercalibration of AMSR2 NASA Team 2 Algorithm Sea Ice Concentrations With AMSR-E Slow Rotation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(9): 3923-3933. doi: http://dx.doi.org/10.1109/JSTARS.2017.2719624.

Mizuochi, Hiroki, et al. 2017. Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: An integrated use of AMSR series, MODIS, and Landsat . Remote Sensing of Environment 199: 370-388. doi: http://dx.doi.org/10.1016/j.rse.2017.07.026.

Pfaffhuber, Andreas A., Jan L. Lieser, and Christian Haas. 2017. Snow thickness profiling on Antarctic sea ice with GPR—Rapid and accurate measurements with the potential to upscale needles to a haystack. Geophysical Research Letters 44(15): 7836–7844. doi: http://dx.doi.org/10.1002/2017GL074202.

Protopapadaki, Sofia E., Claudia J. Stubenrauch, and Artem G. Feofilov. 2017. Upper tropospheric cloud systems derived from IR sounders: properties of cirrus anvils in the tropics. Atmospheric Chemistry and Physics 17: 3845–3859. doi: http://dx.doi.org/10.5194/acp-17-3845-2017.

Rajib, Adnan 2017. Improved soil moisture accounting in hydrologic models. . Ph. D. Purdue University.

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.

Scarlat, Raul Cristien, Georg Heygster,and Leif Toudal Pedersen. 2017. Experiences With an Optimal Estimation Algorithm for Surface and Atmospheric Parameter Retrieval From Passive Microwave Data in the Arctic. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(9): 3934-3947. doi: http://dx.doi.org/10.1109/JSTARS.2017.2739858.

Sohey Nihashi, Kay I.Ohshima, and Sei-Ichi Saitoh. 2017. Sea-ice production in the northern Japan Sea. Deep-Sea Research Part I - Oceanographic Research Papers 127: 65-76. doi: http://dx.doi.org/10.1016/j.dsr.2017.08.003.

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.

Wang, Hui-Lin, et al. 2017. Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China. Journal of Applied Remote Sensing 11(4). Art. #045003. doi: http://dx.doi.org/10.1117/1.JRS.11.045003.

Wang, Kun, et al. 2017. Study on the Permafrost Distribution Based on RS/GIS. 2017 Asia-Pacific Engineering and Technology Conference (APETC 2017). Lancaster, PA: DEStech Publications, Inc..

Xu, Xiaoyong, et al. 2017. Comparison of X-Band and L-Band Soil Moisture Retrievals for Land Data Assimilation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(9): 3850-3860. doi: http://dx.doi.org/10.1109/JSTARS.2017.2703988.

Xue, Yuan, and Barton A. Forman. 2017. Atmospheric and Forest Decoupling of Passive Microwave Brightness Temperature Observations Over Snow-Covered Terrain in North America. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(7): 3172-3189. doi: http://dx.doi.org/10.1109/JSTARS.2016.2614158.

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.

Zhao, Enyu, et al. 2017. Land surface temperature retrieval from AMSR-E passive microwave data. Optics Express 25(20): A940-A952. doi: http://dx.doi.org/10.1364/OE.25.00A940.

Zhao, Tianjie, et al. 2017. Estimation of high-resolution near-surface freeze/thaw state by the integration of microwave and thermal infrared remote sensing data on the Tibetan Plateau. Earth and Space Science 4(8): 472–484. doi: http://dx.doi.org/10.1002/2017EA000277.

Zhong, Aifen, et al. 2017. Downscaling of passive microwave soil moisture retrievals based on spectral analysis. International Journal of Remote Sensing 39(1): 50-67. doi: http://dx.doi.org/10.1080/01431161.2017.1378456.

Zhou, Ji, et al. 2017. A Thermal Sampling Depth Correction Method for Land Surface Temperature Estimation From Satellite Passive Microwave Observation Over Barren Land. IEEE Transactions on Geoscience and Remote Sensing 55(8): 4743 - 4756. doi: http://dx.doi.org/10.1109/TGRS.2017.2698828.

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.

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.

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.

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