Published Research

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

2018

Bai, Heming, et al. 2018. Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites. Atmospheric Chemistry and Physics 18(3): 1763-1783. doi: http://dx.doi.org/10.5194/acp-18-1763-2018.

Cameron, Michael F., et al. 2018. Habitat selection and seasonal movements of young bearded seals (Erignathus barbatus) in the Bering Sea. PLOSone 13(2). Art. # e0192743. doi: http://dx.doi.org/10.1371/journal.pone.0192743.

Iupikov, Oleg A., et al. 2018. Multibeam Focal Plane Arrays With Digital Beamforming for High Precision Space-Borne Ocean Remote Sensing. IEEE Transactions on Antennas and Propagation 66(2): 737-748. doi: http://dx.doi.org/10.1109/TAP.2017.2763174.

Lam, Hoi Ming, et al. 2018. Erroneous sea-ice concentration retrieval in the East Antarctic. Annals of Glaciology: 1-12. doi: http://dx.doi.org/10.1017/aog.2018.1.

Mao, Kebiao, et al. 2018. Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network. Chinese Geographical Science 28(1): 1-11. doi: http://dx.doi.org/10.1007/s11769-018-0930-1.

Nielsen-Englyst. Pia, et al. 2018. Optimal Estimation of Sea Surface Temperature from AMSR-E. Remote Sensing 10(2): 10.3390/rs10020229. Art. #229.

Shi, Yaya, et al. 2018. Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data. Remote Sensing 10(2). Art. #309. doi: http://dx.doi.org/10.3390/rs10020309.

Tian, Hui and Mudassar Iqbal. 2018. Utilizing a new soil effective temperature scheme and archived satellite microwave brightness temperature data to estimate surface soil moisture in the Nagqu region, Tibetan Plateau of China. Journal of Arid Land 10(1): 84-100. doi: http://dx.doi.org/10.1007/s40333-017-0075-6.

Toyota, Takenobu and Noriaki Kimura. 2018. An Examination of the Sea Ice Rheology for Seasonal Ice Zones Based on Ice Drift and Thickness Observations. Journal of Geophysical Research - Oceans 123(2): 1406-1428. doi: http://dx.doi.org/10.1002/2017JC013627.

Trishchenko, Alexander P., and Shusen Wang. 2018. Variations of Climate, Surface Energy Budget, and Minimum Snow/Ice Extent over Canadian Arctic Landmass for 2000–16. Journal of Climate 31(3): 1155-1172. doi: http://dx.doi.org/10.1175/JCLI-D-17-0198.1.

Tuttle, Samuel, et al. 2018. Intercomparison of snow water equivalent observations in the Northern Great Plains. Hydrological Processes 32(6): 817-829. doi: http://dx.doi.org/10.1002/hyp.11459.

Wang, Kuo-Nung, et al. 2018. Correcting negatively biased refractivity below ducts in GNSS radio occultation: an optimal estimation approach towards improving planetary boundary layer (PBL) characterization. Atmospheric Measurement Techniques 10(12): 4761-4776. doi: http://dx.doi.org/10.5194/amt-10-4761-2017.

Yackel, John J., et al. 2018. A spectral mixture analysis approach to quantify Arctic first-year sea ice melt pond fraction using QuickBird and MODIS reflectance data. Remote Sensing of Environment 204: 704-716. doi: http://dx.doi.org/10.1016/j.rse.2017.09.030.

Zhang, Guosheng and William Perrie. 2018. Effects of Asymmetric Secondary Eyewall on Tropical Cyclone Evolution in Hurricane Ike (2008) . Geophysical Research Letters 45(3): 1676-1683. doi: http://dx.doi.org/10.1002/2017GL076988.

Zhang, Lei, et al. 2018. Estimate of HurricaneWind Speed from AMSR-E Low-Frequency Channel Brightness Temperature Data. Atmosphere 9(1). Art. #34. doi: http://dx.doi.org/10.3390/atmos9010034.

2017

Li, Yunqing, et al. 2017. Retrieve vegetation effective optical depth using time-series AMSR-E brightness temperature data at C band — A case study. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . New York: Institute of Electrical and Electronics Engineers ( IEEE ), 6225 - 6228. doi: http://dx.doi.org/10.1109/IGARSS.2017.8128431.

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.

Ardyna, Mathieu, et al. 2017. Delineating environmental control of phytoplankton biomass and phenology in the Southern Ocean. Geophysical Research Letters 44(10): 5016-5024. doi: http://dx.doi.org/10.1002/2016GL072428.

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.

Cho, Eunsang, Samuel E. Tuttle, and Jennifer M. Jacobs. 2017. Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2. Remote Sensing 9(5). Art. #465. doi: http://dx.doi.org/10.3390/rs9050465.

De Silva, Liyanarachchi and Hajime Yamaguchi. 2017. The impact of data assimilation and atmospheric forcing data on predicting short-term sea ice distribution along the Northern sea route. Okhotsk Sea and Polar Oceans Research 1: 1-6.

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.

Du, Jinyang, et al. 2017. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth System Science Data 9(2): 791-808. doi: http://dx.doi.org/10.5194/essd-9-791-2017.

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.

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.

Hu, Tongxi, et al. 2017. High-Resolution Mapping of Freeze/Thaw Status in China via Fusion of MODIS and AMSR2 Data. Remote Sensing 9(12). Art. #1339. doi: http://dx.doi.org/10.3390/rs9121339.

Huang, Xiaodong, et al. 2017. Impact of climate and elevation on snow cover using integrated remote sensing snow products in Tibetan Plateau. Remote Sensing of Environment 190: 274–288. doi: http://dx.doi.org/10.1016/j.rse.2016.12.028.

Jeong, Dae Il, Laxmi Sushama, and M. Naveed Khaliq. 2017. 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.

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.

Jiménez, C., et al. 2017. Applying multiple land surface temperature products to derive heat fluxes over a grassland site. Remote Sensing Applications 6: 15-24. doi: http://dx.doi.org/10.1016/j.rsase.2017.01.002.

Koenig, Zoé , et al. 2017. The Yermak Pass Branch: A Major Pathway for the Atlantic Water North of Svalbard?. Journal of Geophysical Research - Oceans 122(12): 9332-9349. doi: http://dx.doi.org/10.1002/2017JC013271.

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.

Kwon, Yonghwan 2017. Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale. Ph. D. University of Texas at Austin.

Kwon, Yonghwan, et al. 2017. Improving the Radiance Assimilation Performance in Estimating Snow Water Storage across Snow and Land-Cover Types in North America. Journal of Hydrometeorology 18(3): 651–668. doi: http://dx.doi.org/10.1175/JHM-D-16-0102.1.

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.

Lecomte, Olivier. 2017. Influence of snow processes on sea ice : a model study. Ph. D. Université Catholique de Louvain.

Li, Lele, Haihua Chen, and Lei Guan. 2017. Retrieval of snow depth on sea ice in the arctic from FY3B/MWRI. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York: Institute of Electrical and Electronics Engineers ( IEEE ), 4976 - 4979. doi: http://dx.doi.org/10.1109/IGARSS.2017.8128120.

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 and Ashok K.Mishra. 2017. Performance of AMSR_E soil moisture data assimilation in CLM4.5 model for monitoring hydrologic fluxes at global scale. Journal of Hydrology 547: 67-79. doi: http://dx.doi.org/10.1016/j.jhydrol.2017.01.036.

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.

Magagi, Ramata, Yann Kerr, and Jean-Pierre Wigneron. 2017. 2 – Estimation of Soil Water Conditions Using Passive Microwave Remote Sensing. Land Surface Remote Sensing in Continental Hydrology. Edited by ed. Nicolas Baghdadi and Mehrez Zribi. Elsevier Ltd.. Amsterdam: Elsevier, 41-78. doi: http://dx.doi.org/10.1016/B978-1-78548-104-8.50002-4.

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.

Nihashi, Sohey, 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.

Petrou, Zisis I. and YingLi Tian. 2017. Prediction of sea ice motion with recurrent neural networks. 2017 IEEE International Geoscience and Remote Sensing Symposium . New York: Institute of Electrical and Electronics Engineers ( IEEE ), 5422 - 5425. doi: http://dx.doi.org/10.1109/IGARSS.2017.8128230.

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.

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.

Su, Yanjun, et al. 2017. Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures. International Journal of Digital Earth 10(7): 687-700. doi: http://dx.doi.org/10.1080/17538947.2016.1247472.

Tsutsui, Hiroyuki, and Takashi Maeda. 2017. Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring. Remote Sensing 9(6). Art. #523. doi: http://dx.doi.org/10.3390/rs9060523.

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..

Wrzesien, Melissa L., et al. 2017. Comparison of Methods to Estimate Snow Water Equivalent at the Mountain Range Scale: A Case Study of the California Sierra Nevada. Journal of Hydrometeorology 18(4): 1101-1119. doi: http://dx.doi.org/10.1175/JHM-D-16-0246.1.

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, Ruan-yu, et al. 2017. Rainfall retrieval of tropical cyclones using FY-3B microwave radiation imager (MWRI). 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York: Institute of Electrical and Electronics Engineers ( IEEE ), 550 - 553. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127012.

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.

Zhao, Xiaoyi. 2017. Studies of Atmospheric Ozone and Related Constituents in the Arctic and at Mid-latitudes. : 234 p. Ph. D. University of Toronto.

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, Fang-Cheng, et al. 2017. Retrieving K-Band Instantaneous Microwave Land Surface Emissivity Based on Passive Microwave Brightness Temperature and Atmospheric Precipitable Water Vapor Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(12): 5608-5617. doi: http://dx.doi.org/10.1109/JSTARS.2017.2763167.

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.

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