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An investigation on microwave transmissivity at frequencies of 18.7 and 36.5 GHz for diverse forest types during snow season.

, , , , , , , and . Int. J. Digit. Earth, 14 (10): 1354-1379 (2021)

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Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China., , , and . Remote Sensing, 11 (24): 3037 (2019)Development of a Pixel-Wise Forest Transmissivity Model at Frequencies of 19 GHz and 37 GHz for Snow Depth Inversion in Northeast China., , , , , , , , and . Remote. Sens., 14 (21): 5483 (2022)A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China., , , , and . Int. J. Digit. Earth, 16 (1): 1164-1185 (December 2023)Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms., , , , , and . Sensors, 22 (16): 6064 (2022)A Dynamic Snow Depth Inversion Algorithm Derived From AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China., , , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2021)A Fine-Resolution Snow Depth Retrieval Algorithm From Enhanced-Resolution Passive Microwave Brightness Temperature Using Machine Learning in Northeast China., , , , , and . IEEE Geosci. Remote. Sens. Lett., (2022)An investigation on microwave transmissivity at frequencies of 18.7 and 36.5 GHz for diverse forest types during snow season., , , , , , , and . Int. J. Digit. Earth, 14 (10): 1354-1379 (2021)An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China., , , , , , and . Remote. Sens., 14 (6): 1480 (2022)Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms., , , and . Sensors, 20 (23): 6980 (2020)