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.

2019

Bai, X., J. Zeng, K. S. Chen, Z. Li, Y. Zeng, J. Wen, X. Wang, X. Dong, Z. Su 2019. Parameter Optimization of a Discrete Scattering Model by Integration of Global Sensitivity Analysis Using SMAP Active and Passive Observations. IEEE Transactions on Geoscience and Remote Sensing 57(2): 1084-1099. IEEE Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/TGRS.2018.2864689.

Chen, Xiyu and Liu, Lin and Bartsch, Annett. 2019. Detecting soil freeze/thaw onsets in Alaska using SMAP and ASCAT data. Remote Sensing of Environment 220: 59-70. doi: https://doi.org/10.1016/j.rse.2018.10.010.

Jagdhuber, Thomas; Baur, Martin; Akbar, Ruzbeh; Das, Narendra N.; Link, Moritz; He, Lian; Entekhabi, Dara. 2019. Estimation of active-passive microwave covariation using SMAP and Sentinel-1 data. Remote Sensing of Environment 225: 458-468. doi: https://doi.org/10.1016/j.rse.2019.03.021.

K. Fang, M. Pan and C. Shen. 2019. The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning. IEEE Transactions on Geoscience and Remote Sensing 57(4): 2221-2233. doi: https://doi.org/10.1109/TGRS.2018.2872131.

Navid Jadidoleslam, Ricardo Mantilla, Witold F. Krajewski, Michael H. Cosh 2019. Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture. Journal of Hydrology 576: 85-97. doi: https://doi.org/10.1016/j.jhydrol.2019.06.026.

Navid Jadidoleslam, Ricardo Mantilla, Witold F. Krajewski, Radoslaw Goska 2019. Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region. Journal of Hydrology 124210. doi: https://doi.org/10.1016/j.jhydrol.2019.124210.

Pațilea, C.; Heygster, G.; Huntemann, M.; Spreen, G. 2019. Combined SMAP–SMOS thin sea ice thickness retrieval. The Cryosphere 13: 675-691. doi: https://doi.org/10.5194/tc-13-675-2019.

Tavakol, Ameneh; Rahmani, Vahid; Quiring, Steven M.; Kumar, Sujay V. 2019. Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States. Remote Sensing of Environment 229: 234-246. doi: https://doi.org/10.1016/j.rse.2019.05.006.

Wei, Zushuai; Meng, Yizhuo; Zhang, Wen; Peng, Jian; Meng, Lingkui. 2019. Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau. Remote Sensing of Environment 225: 30-44. doi: https://doi.org/10.1016/j.rse.2019.02.022.

Ye, N.; Walker, J.P.; Bindlish, R.; Chaubell, J.; Das, N.N.; Gevaert, A.I.; Jackson, T.J.; Rüdiger, C. 2019. Evaluation of SMAP downscaled brightness temperature using SMAPEx-4/5 airborne observations. Remote Sensing of Environment 221: 363-372. doi: https://doi.org/10.1016/j.rse.2018.11.033.

Zhang, Runze; Kim, Seokhyeon; Sharma, Ashish. 2019. A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote Sensing of Environment 223: 82-94. doi: https://doi.org/10.1016/j.rse.2019.01.015.

2018

Akbar, R.; Short Gianotti, D.; Haghighi, E.; Entekhabi, D.; McColl, K.A.; Salvucci, G.D. 2018. Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation. Water Resources Research 54(3). doi: https://doi.org/10.1002/2017WR021508.

Akbar, R.; Short Gianotti, D.J.; McColl, K.A.; Haghighi, E.; Salvucci, G.D.; Entekhabi, D. 2018. Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture. Journal of Hydrometeorology 19 (5): 871-889. doi: https://doi.org/10.1175/JHM-D-17-0200.1.

Alemohammad, S. H.; Kolassa, J.; Prigent, C.; Aires, F.; Gentine, P. 2018. Global downscaling of remotely sensed soil moisture using neural networks. Hydrology and Earth System Sciences. doi: https://doi.org/10.5194/hess-22-5341-2018.

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: https://doi.org/10.1080/2150704X.2017.1415472.

Babaeian, E.; Sadeghi, M.; Franz, T.E.; Jones, S.; Tuller, M. 2018. Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sensing of Environment 211: 425-440. doi: https://doi.org/10.1016/j.rse.2018.04.029.

Bai, J.; Cui, Q.; Chen, D.; Yu, H.; Mao, X.; Meng, L.; Cai, Y. 2018. Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China. Remote Sensing. doi: https://doi.org/10.3390/rs10081302.

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: https://doi.org/10.1109/LGRS.2018.2805259.

Brocca, L.; Tarpanelli, A.; Filippucci, P.; Dorigo, W.; Zaussinger, F.; Gruber, A.; Fernández-Prieto, D. 2018. How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. International Journal of Applied Earth Observations and Geoinformation. doi: https://doi.org/10.1016/j.jag.2018.08.023.

Champagne, C.; Zhang, Y.; Cherneski, P.; Hadwen, T. 2018. Estimating Regional Scale Hydroclimatic Risk Conditions from the Soil Moisture Active-Passive (SMAP) Satellite. Geosciences. doi: https://doi.org/10.3390/geosciences8040127.

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: https://doi.org/10.1016/j.rse.2017.08.025.

Chaparro, D.; Piles, M.; Vall-Llossera, M.; Camps, A.; Konings, A.G.; Entekhabi, D. 2018. L-band vegetation optical depth seasonal metrics for crop yield assessment. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.04.049.

Chen, F.; Crow, W.T.; Bindlish, R.; Colliander, A.; Burgin, M.S.; Asanuma, J.; Aida, K. 2018. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.05.008.

Chew, C. C.; Small, E. E. 2018. Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture. Geophysical Research Letters 45(9): 4049. doi: https://doi.org/10.1029/2018GL077905.

Crow, W. T.; Chen, F.; Reichle, R. H.; Xia, Y.; Liu, Q. 2018. Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models. Geophysical Research Letters. doi: https://doi.org/10.1029/2018GL077193.

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: https://doi.org/10.3390/rs10010033.

Das, N.N., et al. 2018. The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.04.011.

Dirmeyer, P.A.; Norton, H.E. 2018. Indications of Surface and Sub-Surface Hydrologic Properties from SMAP Soil Moisture Retrievals. Journal of Hydrology.

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: https://doi.org/10.1002/2017GL075656.

Du, J.; Kimball, J.S.; Galantowicz, J.; Kim, S-B.; Chan, S.K.; Reichle, R.; Jones, L.A.; Watts, J.D. 2018. Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.04.054.

Ebrahimi-Khusfi, M.; Alavipanah, S.K.; Hamzeh, S.; Amiraslani, F.; Samany, N.N.; Wigneron, J-P. 2018. Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC. International Journal of Applied Earth Observation & Geoinformation 67: 148-160. doi: https://doi.org/10.1016/j.jag.2017.12.005.

Fan, S-D.; Hu, Y-M.; Wang, L.; Liu, Z-H.; Shi, Z.; Wu, W-B.; Pan, Y-C.; Wang, G-X.; Zhu, A-X.; Li, B. 2018. Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products. Sustainability 10(10): 3459. doi: https://doi.org/10.3390/su10103459.

Fang, B.; Lakshmi, V.; Bindlish, R.; Jackson, T.J. 2018. Downscaling of SMAP Soil Moisture Using Land Surface Temperature and Vegetation Data. Vadose Zone Journal 17(1). doi: https://doi.org/10.2136/vzj2017.11.0198.

Feldman, Andrew F.; Akbar, Ruzbeh; Entekhabi, Dara 2018. Characterization of higher-order scattering from vegetation with SMAP measurements. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.10.022.

Gao, Y.; Walker, J.P.; Ye, N.; Pancera, R.; Monerris, A.; Ryu, D.; Rudiger, C.; Jackson, T.J. 2018. Evaluation of the Tau–Omega Model for Passive Microwave Soil Moisture Retrieval Using SMAPEx Datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing: 888. doi: https://doi.org/10.1109/JSTARS.2018.2796546.

Hajj, M. El; Baghdadi, N.; Zribi, M.; Rodríguez-Fernández, N.; Wigneron, J.P.; Al-Yaari, A.; Bitar, A.A.; Albergel, C.; Calvet, J-C. 2018. Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France. Remote Sensing 10(4): 569. doi: https://doi.org/10.3390/rs10040569.

He, L.; Hong, Y.; Wu, X.; Ye, N.; Walker, J.P.; Chen, X. 2018. Investigation of SMAP Active–Passive Downscaling Algorithms Using Combined Sentinel-1 SAR and SMAP Radiometer Data. IEEE Transactions on Geoscience and Remote Sensing: 4906. doi: https://doi.org/10.1109/TGRS.2018.2842153.

Kim, H.; Lakshmi, V. 2018. Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture. Geophysical Research Letters 45(16): 8272. doi: https://doi.org/10.1029/2018GL078923.

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: https://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: https://doi.org/10.1109/JSTARS.2018.2790409.

Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.; Aida, K.; Asanuma, J.; Bircher, S.; Caldwell, T.; Colliander, A.; Cosh, M.; Collins, C. Holifield; Jackson, T. J.; Martínez-Fernández, J.; McNairn, H.; Pacheco, A.; Thibeault, M.; Walk 2018. Estimating surface soil moisture from SMAP observations using a Neural Network technique. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2017.10.045.

Koster, R.D.; Crow, W.T.; Reichle, R.H.; Mahanama, S.P. 2018. Estimating Basin‐Scale Water Budgets With SMAP Soil Moisture Data. Water Resources Research. doi: https://doi.org/10.1029/2018WR022669.

Koster, R.D.; Liu, Q.; Mahanama, S.P.P.; Reichle, R.H. 2018. Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation. Journal of Hydrometeorology. doi: https://doi.org/10.1175/JHM-D-17-0228.1.

Kraatz, S.; Jacobs, J.M.; Schröder, R.; Cho, E.; Cosh, M.; Seyfried, M.; Prueger, J.; Livingston, S. 2018. Evaluation of SMAP Freeze/Thaw Retrieval Accuracy at Core Validation Sites in the Contiguous United States. Remote Sensing 10(9): 1483. doi: https://doi.org/10.3390/rs10091483.

Li, C.; Lu, H.; Yang, K.; Han, M.; Wright, J.S.; Chen, Y.; Yu, L.; Xu, S.; Huang, X.; Gong, W. 2018. The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution Model Simulations and In-Situ Observations on the Tibetan Plateau. Remote Sensing 10(4): 535. doi: https://doi.org/10.3390/rs10040535.

Li, Chengwei, et al. 2018. The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution. Remote Sensing 10(4). Art. #535. doi: https://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: https://doi.org/10.1002/2017GL073904.

Lv, S.; Zeng, Y.; Wen, J.; Zhao, H.; Su, Z. 2018. Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry. Remote Sensing 10(4). doi: https://doi.org/10.3390/rs10040519.

Lyu, H.; Mccoll, K.; Li, X.; Derksen, C.; Berg, A.; Andrew Black, T.; Euskirchen, E.; Loranty, M.; Pulliainen, J.; Rautiainen, K.; Rowlandson, T.; Roy, A.; Royer, A.; Langlois, A.; Stephens, J.; Lu, H.; Entekhabi, D. 2018. Validation of the SMAP freeze/thaw product using categorical triple collocation. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2017.12.007.

Majdar, H.A.; Vafakhah, M.; Sharifikia, M.; Ghorbani, A. 2018. Spatial and temporal variability of soil moisture in relation with topographic and meteorological factors in south of Ardabil Province, Iran. Environmental Monitoring and Assessment. doi: https://doi.org/10.1007/s10661-018-6887-9.

Mishra, V.; Ellenburg, W. L.; Griffin, R.E.; Mecikalski, J.R.; Cruise, J.F.; Hain, C.R.; Anderson, M.C. 2018. An initial assessment of a SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States. International Journal of Applied Earth Observations and Geoinformation 68: 92-104. doi: https://doi.org/10.1016/j.jag.2018.02.005.

Molan, Y.E., et al. 2018. L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska. Remote Sensing 10(1). Art. #150. doi: https://doi.org/10.3390/rs10010150.

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: https://doi.org/10.3390/rs10030427.

Pablos, M.; González-Zamora, A.; Sánchez, N.; Martínez-Fernández, J. 2018. Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations. Remote Sensing 10(7): 981. doi: https://doi.org/10.3390/rs10070981.

Purdy, A.J.; Fisher, J.B.; Goulden, M.L.; Colliander, A.; Halverson, G.; Tu, K.; Famiglietti, J.S. 2018. SMAP soil moisture improves global evapotranspiration. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.09.023.

Rains, D.; De Lannoy, G. J. M.; Lievens, H.; Walker, J.P.; Verhoest, N.E.C. 2018. SMOS and SMAP Brightness Temperature Assimilation Over the Murrumbidgee Basin. IEEE Geoscience and Remote Sensing Letters 15(99): 1. doi: https://doi.org/10.1109/LGRS.2018.2855188.

Rajasekaran, E.; Das, N.; Poulsen, C.; Behrangi, A.; Swigart, J.; Svoboda, M.; Entekhabi, D.; Yueh, S.; Doorn, B.; Entin, J. 2018. SMAP Soil Moisture Change as an Indicator of Drought Conditions. Remote Sensing. doi: https://doi.org/10.3390/rs10050788.

Rigden, A.J.; Salvucci, G.D.; Entekhabi, D.; Short Gianotti, D.J. 2018. Geophysical Research Letters. 45 (18). doi: https://doi.org/10.1029/2018GL079121.

Sadri, S.; Wood, E.F.; Pan, M. 2018. A SMAP-Based Drought Monitoring Index for the United States. Hydrology & Earth System Sciences Discussions. doi: https://doi.org/10.5194/hess-2018-182.

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: https://doi.org/10.1016/j.jag.2017.10.010.

Santi, E.; Paloscia, S.; Pettinato, S.; Brocca, L.; Ciabatta, L.; Entekhabi, D. 2018. Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.04.039.

Sazib, N.; Mladenova, I.; Bolten, J. 2018. Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. Remote Sensing 10(8): 1265. doi: https://doi.org/10.3390/rs10081265.

Schmitt, A.U.; Kaleschke, L. 2018. A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications. Remote Sensing of Environment. doi: https://doi.org/10.3390/rs10040553.

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: https://doi.org/10.5194/hess-22-1649-2018.

Soldo, Y.; Le Vine, D.; Bringer, A.; Matthaeis, P. de; Olive, R.; Johnson, J.; Piepmeier, J. 2018. Location of Radio-Frequency Interference Sources Using the SMAP L-Band Radiometer. IEEE Transactions on Geoscience and Remote Sensing: 1. doi: https://doi.org/10.1109/TGRS.2018.2844127.

Stillman, S.; Zeng, X. 2018. Evaluation of SMAP Soil Moisture Relative to Five Other Satellite Products Using the Climate Reference Network Measurements Over USA. IEEE Transactions on Geoscience and Remote Sensing. doi: https://doi.org/10.1109/TGRS.2018.2835316.

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: https://doi.org/10.3390/w10030291.

Xu, H.; Yuan, Q.; Li, T.; Shen, H.; Zhang, L.; Jiang, H. 2018. Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S. Remote Sensing 10(9): 1351. doi: https://doi.org/10.3390/rs10091351.

Xu, Y.; Wang, L.; Ross, K.W.; Liu, C.; Berry, K. 2018. Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States. Remote Sensing 10(2): 301. doi: https://doi.org/10.3390/rs10020301.

Yin, J.; Zhan, X. 2018. Impact of Bias-Correction Methods on Effectiveness of Assimilating SMAP Soil Moisture Data into NCEP Global Forecast System Using the Ensemble Kalman Filter. IEEE Geoscience & Remote Sensing Letters. doi: https://doi.org/10.1109/LGRS.2018.2806092.

Zhao, W.; Sánchez, N.; Lu, H.; Li, A. 2018. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology. doi: https://doi.org/10.1016/j.jhydrol.2018.06.081.

Zheng, D.; van der Velde, R.; Wen, J.; Wang, X.; Ferrazzoli, P.; Schwank, M.; Colliander, A.; Bindlish, R.; Su, Z. 2018. Assessment of the SMAP Soil Emission Model and Soil Moisture Retrieval Algorithms for a Tibetan Desert Ecosystem. IEEE Transactions on Geoscience and Remote Sensing. doi: https://doi.org/10.1109/TGRS.2018.2811318.

Zheng, D.; Wang, X.; van der Velde, R.; Ferrazzoli, P.; Wen, J.; Wang, Z.; Schwank, M.; Colliander, A.; Bindlish, R.; Su, Z. 2018. Impact of surface roughness, vegetation opacity and soil permittivity on L-band microwave emission and soil moisture retrieval in the third pole environment. Remote Sensing of Environment. doi: https://doi.org/10.1016/j.rse.2018.03.011.

Zwieback, S.; Colliander, A.; Cosh, M. H.; Martínez-Fernández, J.; McNairn, H.; Starks, P. J.; Thibeault, M.; Berg, A. 2018. Estimating time-dependent vegetation biases in the SMAP soil moisture product. Hydrology and Earth System Sciences. doi: https://doi.org/10.5194/hess-22-4473-2018.

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: https://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: https://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: https://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: https://doi.org/10.1109/IGARSS.2017.8127505.

Bhagat, V. 2017. Space-borne Active Microwave Remote Sensing of Soil Moisture: A Review. Remote Sensing of Land 1(1): 53-86. doi: https://doi.org/10.21523/gcj1.17010104.

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: https://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: https://doi.org/10.1002/2016WR019967.

Carreno-Luengo, H.; Lowe, S.; Zuffada, C.; Esterhuizen, S.; Oveisgharan, S. 2017. Spaceborne GNSS-R from the SMAP Mission: First Assessment of Polarimetric Scatterometry over Land and Cryosphere. Remote Sensing 9(4): 1-23. doi: https://doi.org/10.3390/rs9040362.

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: https://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: https://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: https://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: https://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: https://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: https://doi.org/10.1016/j.rse.2017.06.020.

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

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: https://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: https://doi.org/10.1109/LGRS.2017.2753203.

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: https://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: https://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: https://doi.org/10.1016/j.rse.2017.03.007.

Ebrahimi, M.; Alavipanah, S.K.; Hamzeh, S.; Amiraslani, F.; Samany, N.N.; Wigneron, J-P. 2017. Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions. Journal of Hydrology. doi: https://doi.org/10.1016/j.jhydrol.2017.12.051.

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: https://doi.org/10.1109/IGARSS.2017.8127516.

Fang, K.; Shen, C.; Kifer, D.; Yang, X. 2017. Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network. Geophysical Research Letters. doi: https://doi.org/10.1002/2017GL075619.

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, L.; Chen, J.M.; Chen, K. 2017. Simulation and SMAP Observation of Sun-Glint Over the Land Surface at the L-Band. IEEE Transactions on Geoscience and Remote Sensing. doi: https://doi.org/10.1109/TGRS.2017.2648502.

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: https://doi.org/10.1002/2016JG003603.

Pages