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

2018. L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska. Remote Sensing 10(1). Art. #150. doi: http://dx.doi.org/10.3390/rs10010150.

Amani, Meisam, et al. 2018. Contemporaneous estimation of Leaf Area Index and soil moisture using the red-NIR spectral . Remote Sensing Letters 9(3): 264-273. doi: http://dx.doi.org/10.1080/2150704X.2017.1415472.

Blankenship, Clay, et al. 2018. Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation. IEEE Geoscience and Remote Sensing Letters 15(4): 498-502. doi: http://dx.doi.org/10.1109/LGRS.2018.2805259.

Chan, S. K., et al. 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment 204: 931-941. doi: http://dx.doi.org/10.1016/j.rse.2017.08.025.

Cui, Chenyang, et al. 2018. Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales. Remote Sensing 10(1). Art. #33. doi: http://dx.doi.org/10.3390/rs10010033.

Dong, Jianzhi, Wade T. Crow, and Rajat Bindlish. 2018. The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals. Geophysical Research Letters 45(2): 758-765. doi: http://dx.doi.org/10.1002/2017GL075656.

Kim, Hyunglok, et al. 2018. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment 204: 260-275. doi: http://dx.doi.org/10.1016/j.rse.2017.10.026.

Kim, Seokhyeon, et al. 2018. Building a Flood-Warning Framework for Ungauged Locations Using Low Resolution, Open-Access Remotely Sensed Surface Soil Moisture, Precipitation, Soil, and Topographic Information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(2): 375-387. doi: http://dx.doi.org/10.1109/JSTARS.2018.2790409.

Li, Chengwei, et al. 2018. The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution. Remote Sensing 10(4). Art. #535. doi: http://dx.doi.org/10.3390/rs10040535.

Lievens, H., et al. 2018. Joint Sentinel‐1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters 44(12): 6145-6153. doi: http://dx.doi.org/10.1002/2017GL073904.

Montzka, Carsten, et al. 2018. A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability. Remote Sensing 10(3). Art. #427. doi: http://dx.doi.org/10.3390/rs10030427.

Santi, E., et al. 2018. On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture. International Journal of Applied Earth Observation and Geoinformation 65: 114-123.. doi: http://dx.doi.org/10.1016/j.jag.2017.10.010.

Shellito, Peter J., Eric E. Small, and Ben Livneh. 2018. Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model. Hydrology and Earth System Sciences 22(3): 1649-1663. doi: http://dx.doi.org/10.5194/hess-22-1649-2018.

Xiong, Lihua, et al. 2018. Evaluating Consistency between the Remotely Sensed Soil Moisture and the Hydrological Model-Simulated Soil Moisture in the Qujiang Catchment of China. Water 10(3). Art. # 291. doi: http://dx.doi.org/10.3390/w10030291.

2017

Li, Y., et al. 2017. Decomposition of the SMAP radar channels and relation to surface soil moisture and vegetation. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 1989-1991. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127371.

Al Bitar, Ahmad, et al. 2017. The global SMOS Level 3 daily soil moisture and brightness temperature maps. Earth System Science Data 9(1): 293-315. doi: http://dx.doi.org/10.5194/essd-9-293-2017.

Al-Yaari, A., et al. 2017. Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment 193: 257-273. doi: http://dx.doi.org/10.1016/j.rse.2017.03.010.

Alemohammad, Seyed Hamed, et al. 2017. Statistical downscaling of remotely-sensed soil moisture. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2511 - 2514. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127505.

Burgin, Mariko S., et al. 2017. A Comparative Study of the SMAP Passive Soil Moisture Product With Existing Satellite-Based Soil Moisture Products. IEEE Transactions on Geoscience and Remote Sensing 55(5): 2959-2971. doi: http://dx.doi.org/10.1109/TGRS.2017.2656859.

Cai, Xitian, et al. 2017. Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper-resolution model. Water Resources Research 53(4): 3013–3028. doi: http://dx.doi.org/10.1002/2016WR019967.

Chaubell, J., et al. 2017. Backus-gilbert optimal interpoaltion applied to enhance SMAP data: Implementation and assessment. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2531-2534 . doi: http://dx.doi.org/10.1109/IGARSS.2017.8127510.

Chen, Fan, et al. 2017. Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(2): 489 - 502. doi: http://dx.doi.org/10.1109/JSTARS.2016.2569998.

Chen, Nengcheng, Yuqi He, and Xiang Zhang. 2017. NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia. Remote Sensing 9(1). Art. #51. doi: http://dx.doi.org/10.3390/rs9010051.

Chen, Quan, et al. 2017. Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study Using Ground-Based Observations Over the Little Washita Watershed. IEEE Transactions on Geoscience and Remote Sensing 56(3): 1394-1408. doi: http://dx.doi.org/10.1109/TGRS.2017.2762462.

Chen, Yingying, et al. 2017. Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau. Journal of Geophysical Research - Atmospheres 122(11): 5780-5792. doi: http://dx.doi.org/10.1002/2016JD026388.

Chew, Clara, et al. 2017. SMAP radar receiver measures land surface freeze/thaw state through capture of forward-scattered L-band signals. Remote Sensing of Environment 198: 333-344. doi: http://dx.doi.org/10.1016/j.rse.2017.06.020.

Colliander, Andreas, et al. 2017. An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites. Remote Sensing of Environment 207: 65-70. doi: http://dx.doi.org/10.1016/j.rse.2018.02.006.

Colliander, Andreas, et al. 2017. Spatial Downscaling of SMAP Soil Moisture Using MODIS Land Surface Temperature and NDVI During SMAPVEX15. IEEE Geoscience and Remote Sensing Letters 14(11): 2107-2111. doi: http://dx.doi.org/10.1109/LGRS.2017.2753203.

Colliander, Andreas, et al. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment 191: 215-231. doi: http://dx.doi.org/10.1016/j.rse.2017.01.021.

Crow, Wade T., et al. 2017. L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting. Geophysical Research Letters 44(11): 5495-5503. doi: http://dx.doi.org/10.1002/2017GL073642.

Cui, Huizhen, et al. 2017. Evaluation and analysis of AMSR-2, SMOS, and SMAP soil moisture products in the Genhe area of China. Journal of Geophysical Research - Atmospheres 122(16): 8650–8666. doi: http://dx.doi.org/10.1002/2017JD026800.

Derksen, C., et al. 2017. Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and radiometer measurements. Remote Sensing of Environment 194: 48-62. doi: http://dx.doi.org/10.1016/j.rse.2017.03.007.

Entekhabi, Dara, et al. 2017. Smap-based retrieval of vegetation opacity and albedo. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ),2554 - 2556. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127516.

Fayne, Jessica, et al. 2017. Optical and Physical Methods for Mapping Flooding with Satellite Imagery. Remote Sensing of Hydrological Extremes: 83-103. Zurich: Springer.

He, Liming, et al. 2017. Assessment of SMAP soil moisture for global simulation of gross primary production. Journal of Geophysical Research - Biogeosciences 122(7): 1549-1563. doi: http://dx.doi.org/10.1002/2016JG003603.

Hu, Lei, et al. 2017. Developing geospatial Web service and system for SMAP soil moisture monitoring. Agro-Geoinformatics, 2017 6th International Conference on. doi: http://dx.doi.org/10.1109/Agro-Geoinformatics.2017.8047066.

Jiang, Hongtao, et al. 2017. Generation of SMAP 9 KM soil moisture using a spatio-temporal information fusion model. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2008-2011. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127376.

Jin, Mengjie, et al. 2017. Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China. Remote Sensing 9(4). Art. #387. doi: http://dx.doi.org/10.3390/rs9040387.

Karthikeyan, L., et al. 2017. Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons. Advances in Water Resources 109: 236-252. doi: http://dx.doi.org/10.1016/j.advwatres.2017.09.010.

Kharuk, Viacheslav I., et al. 2017. Climate-induced mortality of Siberian pine and fir in the Lake Baikal Watershed, Siberia. Forest Ecology and Management 384: 191-199. doi: http://dx.doi.org/10.1016/j.foreco.2016.10.050.

Kim, Jin-woo, et al. 2017. Characterizing hydrologic changes of the Great Dismal Swamp using SAR/InSAR. Remote Sensing of Environment 198: 187-202. doi: http://dx.doi.org/10.1016/j.rse.2017.06.009.

Kimball, J. S., et al. 2017. Monitoring ecosystem-atmosphere co2 exchange respose to recent (2015–2016) climate variability using the smap l4 carbon product. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2557 - 2560. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127517.

Knipper, Kyle R., et al. 2017. Downscaling SMAP and SMOS soil moisture with moderate-resolution imaging spectroradiometer visible and infrared products over southern Arizona. Journal of Applied Remote Sensing 11(12). Art. #020621. doi: http://dx.doi.org/10.1117/1.JRS.11.026021.

Kolassa, J., et al. 2017. Data Assimilation to Extract Soil Moisture Information from SMAP Observations. Remote Sensing 9(11). Art. #1179. doi: http://dx.doi.org/10.3390/rs9111179.

Konings, Alexandra G., et al. 2017. L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment 198: 460-470. doi: http://dx.doi.org/10.1016/j.rse.2017.06.037.

Koster, Randal D., Rolf H. Reichle, and Sarith P. P. Mahanama. 2017. A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture . Journal of Hydrometeorology 18(3): 837–843. doi: http://dx.doi.org/10.1175/JHM-D-16-0285.1.

Kumar, Sujay V., et al. 2017. Information theoretic evaluation of satellite soil moisture retrievals. Remote Sensing of Environment 204: 392-400. doi: http://dx.doi.org/10.1016/j.rse.2017.10.016.

Lawston, Patricia, Joseph A. Santanello Jr, and Sujay V. Kumar. 2017. Irrigation Signals Detected From SMAP Soil Moisture Retrievals. Geophysical Research Letters 44(23): 11,860-11,867. doi: http://dx.doi.org/10.1002/2017GL075733.

Liu, Zhiqu, Pingxiang Li, and Jie Yang. 2017. Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal. Remote Sensing 9(11). Art. #1197. doi: http://dx.doi.org/10.3390/rs9111197.

Lu, Hui, et al. 2017. Improving satellite rainfall estimates over Tibetan plateau using in situ soil moisture observation and SMAP retrievals. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2004-2007. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127375.

Lu, Yang, et al. 2017. Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data. Water Resources Research 53(12): 10858-10877. doi: http://dx.doi.org/10.1002/2017WR021415.

Ma, Chunfeng, et al. 2017. Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed . Remote Sensing 9(4). Art. #327. doi: http://dx.doi.org/10.3390/rs9040327.

Madani, Nima, et al. 2017. Global Analysis of Bioclimatic Controls on Ecosystem Productivity Using Satellite Observations of Solar-Induced Chlorophyll Fluorescence. Remote Sensing 9(6). Art. #530. doi: http://dx.doi.org/10.3390/rs9060530.

Martínez, Justino, et al. 2017. Blended SMOS-SMAP SSS product in marginal seas. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2931-2934. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127612.

McColl, Kaighin A., et al. 2017. The global distribution and dynamics of surface soil moisture. Nature Geoscience 10: 100-104. doi: http://dx.doi.org/10.1038/ngeo2868.

McColl, Kaighin A., et al. 2017. Global characterization of surface soil moisture drydowns. Geophysical Research Letters 44(8): 3682–3690. doi: http://dx.doi.org/10.1002/2017GL072819.

Mishra, Ashok, et al. 2017. Drought monitoring with soil moisture active passive (SMAP) measurements. Journal of Hydrology 552: 620-632. doi: http://dx.doi.org/10.1016/j.jhydrol.2017.07.033.

Ouellette, Jeffrey D., et al. 2017. A Time-Series Approach to Estimating Soil Moisture From Vegetated Surfaces Using L-Band Radar Backscatter. IEEE Transactions on Geoscience and Remote Sensing 55(6): 3186 - 3193. doi: http://dx.doi.org/10.1109/TGRS.2017.2663768.

Pierdicca, Nazzareno, et al. 2017. Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(10): 4522-4530. doi: http://dx.doi.org/10.1109/JSTARS.2017.2714025.

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.

Reichle, Rolf H., et al. 2017. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements . Journal of Hydrometeorology 18(10): 2621–2645. doi: http://dx.doi.org/10.1175/JHM-D-17-0063.1.

Sabaghy, Sabah, et al. 2017. Comparison of downscaling techniques for high resolution soil moisture mapping. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 2523-2526. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127508.

Senanayake, I. P., et al. 2017. Downscaling SMAP and SMOS soil moisture retrievals over the Goulburn River Catchment, Australia. 22nd International Congress on Modelling and Simulation. In Syme, G., Hatton MacDonald, D., Fulton, B. and Piantadosi, J. (eds) MODSIM2017, 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2017, 1055-1061. . (PDF File,

Sure, Anudeep, Divyesh Varade, and Onkar Dikshit. 2017. Factors determining spatio-temporal variations of soil moisture using microwave data. Emerging Trends in Computing and Communication Technologies (ICETCCT), International Conference on. doi: http://dx.doi.org/10.1109/ICETCCT.2017.8280301.

Wrona, Elizabeth, et al. 2017. Validation of the Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval in an Arctic tundra environment. Geophysical Research Letters 44(9): 4152-4158. doi: http://dx.doi.org/10.1002/2017GL072946.

Zeng, Jiangyuan, et al. 2017. Covariation of SMAP active and passive measurements with respect to vegetation and surface roughness. 2017 IEEE International Geoscience and Remote Sensing Symposium. New York: Institute of Electrical and Electronics Engineers ( IEEE ), 4166-4169. doi: http://dx.doi.org/10.1109/IGARSS.2017.8127919.

Zhang, Lanhui, Chansheng He, Orcid and Mingmin Zhang. 2017. Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China. Remote Sensing 9(11). Art. #1111. doi: http://dx.doi.org/10.3390/rs9111111.

Zhang, Xuefei, et al. 2017. Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements. Remote Sensing 9(2). Art. #104. doi: http://dx.doi.org/10.3390/rs9020104.

2016

Akbar, R., et al. 2016. Synergistic use of AirMOSS P-band SAR with the SMAP L-band radar-radiometer for soil moisture retrieval. 2016 International Conference on Electromagnetics in Advanced Applications (ICEAA): 793-795.

Al-Yaari, A., et al. 2016. First Application of Regression Analysis to Retrieve Soil Moisture from SMAP Brightness Temperature Observations Consistent with SMOS. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 1633-1636. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729417.

Al-Yaari, A., et al. 2016. First Application of Regression Analysis to Retrieve Soil Moisture. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 1633-1636. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729417.

Chan, Steven J., et al. 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Transactions on Geoscience and Remote Sensing 54(8): 4994 - 5007. doi: http://dx.doi.org/10.1109/TGRS.2016.2561938.

Fascetti, F., et al. 2016. An assessment of SMOS version 6.20 products through Triple and Quadruple Collocation techniques considering ASCAT, ERA/Interim LAND, ISMNand SMAP soil moisture dat. 2016 14th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad) . Lemesos: IEEE, 91 - 94. doi: http://dx.doi.org/10.1109/MICRORAD.2016.7530511.

Fascetti, F., et al. 2016. SMOS, ASCAT, SMAP and ERA soil moisture comparison through the Triple and Quadruple Collocation technique. Proceedings of SPIE 10003. Art. #100030H. doi: http://dx.doi.org/10.1117/12.2244615.

Fournier, S., et al. 2016. SMAP observes flooding from land to sea: The Texas event of 2015. Geophysical Research Letters 43(19): 10338-10346. doi: http://dx.doi.org/10.1002/2016GL070821.

Huntemann, M., C. Patilea, and G. Heygster. 2016. Thickness of thin sea ice retrieved from SMOS and SMAP. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi: http://dx.doi.org/10.1109/IGARSS.2016.7730367.

Jones, L. A., et al. 2016. The SMAP level 4 carbon product for monitoring terrestrial ecosystem-atmosphere CO2 exchange. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) : 139-142. doi: http://dx.doi.org/10.1109/IGARSS.2016.7729027.

Koster, Randal D., et al. 2016. Precipitation estimation using L-band and C-band soil moisture retrievals. Water Resources Research 52(9): 7213-7225. doi: http://dx.doi.org/10.1002/2016WR019024.

Mecklenberg, S., et al. 2016. ESA's Soil Moisture and Ocean Salinity mission: From science to operational applications. Remote Sensing of Environment 180: 3-18. doi: http://dx.doi.org/10.1016/j.rse.2015.12.025.

Pan, Ming, et al. 2016. An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations. Geophysical Research Letters 43(8): 9662-9668. doi: http://dx.doi.org/10.1002/2016GL069964.

Saavedra, Pablo, Clemens Simmer, and Bernd Schalge. 2016. Evaluation of Modeled High Resolution Virtual Brightness Temperatures Compared to Space-Borne Observations for the Neckar Catchment. Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 2016 14th Specialist Meeting on. Piscataway, NJ: Institute of Electrical and Electronics Engineers ( IEEE ). doi: http://dx.doi.org/10.1109/MICRORAD.2016.7530510.

Velpuri, Naga Manohar, Gabriel B. Senay and Jeffrey T. Morisette. 2016. Evaluating New SMAP Soil Moisture for Drought Monitoring in the Rangelands of the US High Plains. Rangelands 38(4): 183-190. doi: http://dx.doi.org/10.1016/j.rala.2016.06.002.

Zeng, Jiangyuan, et al. 2016. A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements . IEEE Transactions on Geoscience and Remote Sensing 54(8): 4929-4940. doi: http://dx.doi.org/10.1109/TGRS.2016.2553085.

2015

Frankenstein, Susan, Maria Stevens, and Constance Scott 2015. Ingestion of Simulated SMAP L3 Soil Moisture Data into Military Maneuver Planning. Journal of Hydrometeorology 16(1): 427-440. doi: http://dx.doi.org/10.1175/JHM-D-14-0032.1.

Kim, Seung-bum, et al. 2015. Feasibility of Inter-Comparing Airborne and Spaceborne Observations of Radar Backscattering Coefficients. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(7): 3507-3519. doi: http://dx.doi.org/10.1109/JSTARS.2015.2424715.

2014

Entekhabi, Dara et al. 2014. SMAP Handbook–Soil Moisture Active Passive: Mapping Soil Moisture and Freeze/Thaw from Space. SMAP Project, JPL CL#14-2285, Jet Propulsion Laboratory, Pasadena, CA.

Kim, Seung Hee, et al. 2014. Combined Usage of TanDEM-X and CryoSat-2 for Generating a High Resolution Digital Elevation Model of Fast Models of L-Band Radar Backscattering Coefficients Over Global Terrain for Soil Moisture Retrieval. IEEE Transactions on Geoscience and Remote Sensing 52(2): 1381-1396. doi: http://dx.doi.org/10.1109/TGRS.2013.2250980.

Kim, Seung-Bum, et al. 2014. Models of L-Band Radar Backscattering Coefficients Over Global Terrain for Soil Moisture Retrieval. IEEE Transactions on Geoscience and Remote Sensing 52(2): 1381 - 1396. doi: http://dx.doi.org/10.1109/TGRS.2013.2250980.

2013

Reichle, Rolf H., et al. 2013. Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS. Surveys in Geophysics: 577-606. doi: http://dx.doi.org/10.1007/s10712-013-9220-8.

2012

Tabatabaeenejad, A., M. Burgin, and M. Moghaddam. 2012. Potential of L-band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests. IEEE Transactions on Geoscience and Remote Sensing 50(6): 2150-2160. doi: http://dx.doi.org/10.1109/TGRS.2011.2173349.