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Refinement of NOAA AMSR-2 Soil Moisture Data Product: 1. Intercomparisons of the Commonly Used Machine-Learning Models.

, , , , , and . IEEE Trans. Geosci. Remote. Sens., (2023)

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Impact of Bias-Correction Methods on Effectiveness of Assimilating SMAP Soil Moisture Data into NCEP Global Forecast System Using the Ensemble Kalman Filter., and . IEEE Geosci. Remote. Sens. Lett., 15 (5): 659-663 (2018)Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations., and . Remote Sensing, 12 (7): 1169 (2020)A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology., , , and . Remote. Sens., 14 (7): 1700 (2022)NOaa Soil Moisture Operational Product System (SMOPS) and its validations., , , , , , and . IGARSS, page 3477-3480. IEEE, (2016)A preliminary assessment of the impact of SMAP Soil Moisture on numerical weather Forecasts from GFS and NUWRF models., , , , , , and . IGARSS, page 5229-5232. IEEE, (2016)A Study of Two Impactful Heavy Rainfall Events in the Southern Appalachian Mountains during Early 2020, Part I; Societal Impacts, Synoptic Overview, and Historical Context., , , , , , and . Remote. Sens., 13 (13): 2452 (2021)Refinement of NOAA AMSR-2 Soil Moisture Data Product - Part 2: Development With the Optimal Machine Learning Model., , , , , and . IEEE Trans. Geosci. Remote. Sens., (2023)NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture., , and . Remote. Sens., 12 (17): 2861 (2020)Refinement of NOAA AMSR-2 Soil Moisture Data Product: 1. Intercomparisons of the Commonly Used Machine-Learning Models., , , , , and . IEEE Trans. Geosci. Remote. Sens., (2023)An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches., , , , , and . Remote Sensing, 10 (4): 625 (2018)