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.

2018

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.

2017

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.

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.

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.

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. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 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 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.

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.