22.2.15.1 Soil Moisture, GNSS-R

Chapter Contents (Back)
Moisture. GNSS.
See also Soil Moisture, Radar, SAR, X-Band.

Botteron, C.[Cyril], Dawes, N.[Nicholas], Leclère, J.[Jérôme], Skaloud, J.[Jan], Weijs, S.V.[Steven V.], Farine, P.A.[Pierre-André],
Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives,
RS(5), No. 7, 2013, pp. 3516-3543.
DOI Link 1308
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Sánchez, N.[Nilda], Alonso-Arroyo, A.[Alberto], Martínez-Fernández, J.[José], Piles, M.[María], González-Zamora, Á.[Ángel], Camps, A.[Adriano], Vall-llosera, M.[Mercè],
On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation,
RS(7), No. 8, 2015, pp. 9954.
DOI Link 1509
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Carreno-Luengo, H.[Hugo], Lowe, S.[Stephen], Zuffada, C.[Cinzia], Esterhuizen, S.[Stephan], Oveisgharan, S.[Shadi],
Spaceborne GNSS-R from the SMAP Mission: First Assessment of Polarimetric Scatterometry over Land and Cryosphere,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
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Han, M.[Mutian], Zhu, Y.L.[Yun-Long], Yang, D.K.[Dong-Kai], Hong, X.B.[Xue-Bao], Song, S.H.[Shu-Hui],
A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
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Jing, L.[Lili], Yang, L.[Lei], Yang, W.T.[Wen-Tao], Xu, T.H.[Tian-He], Gao, F.[Fan], Lu, Y.L.[Yi-Lin], Sun, B.[Bo], Yang, D.K.[Dong-Kai], Hong, X.B.[Xue-Bao], Wang, N.Z.[Na-Zi], Ruan, H.L.[Hong-Liang], Darrozes, J.[José],
Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data: A Dual-Band Data Fusion Approach,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Camps, A.[Adriano], Vall-Llossera, M.[Mercedes], Park, H.[Hyuk], Portal, G.[Gerard], Rossato, L.[Luciana],
Sensitivity of TDS-1 GNSS-R Reflectivity to Soil Moisture: Global and Regional Differences and Impact of Different Spatial Scales,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Jia, Y.[Yan], Jin, S.G.[Shuang-Gen], Savi, P.[Patrizia], Gao, Y.[Yun], Tang, J.[Jing], Chen, Y.X.[Yi-Xiang], Li, W.[Wenmei],
GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
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Chang, X.[Xin], Jin, T.Y.[Tao-Yong], Yu, K.[Kegen], Li, Y.W.[Yun-Wei], Li, J.C.[Jian-Cheng], Zhang, Q.A.[Qi-Ang],
Soil Moisture Estimation by GNSS Multipath Signal,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
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Calabia, A.[Andres], Molina, I.[Iñigo], Jin, S.G.[Shuang-Gen],
Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Wu, X.R.[Xue-Rui], Ma, W.X.[Wen-Xiao], Xia, J.M.[Jun-Ming], Bai, W.H.[Wei-Hua], Jin, S.G.[Shuang-Gen], Calabia, A.[Andrés],
Spaceborne GNSS-R Soil Moisture Retrieval: Status, Development Opportunities, and Challenges,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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Dong, Z.[Zhounan], Jin, S.G.[Shuang-Gen],
Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
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Lv, J.C.[Ji-Chao], Zhang, R.[Rui], Tu, J.S.[Jin-Sheng], Liao, M.J.[Ming-Jie], Pang, J.[Jiatai], Yu, B.[Bin], Li, K.[Kui], Xiang, W.[Wei], Fu, Y.[Yin], Liu, G.X.[Guo-Xiang],
A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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Shi, Y.J.[Ya-Jie], Ren, C.[Chao], Yan, Z.H.[Zhi-Heng], Lai, J.M.[Jian-Min],
High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
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Munoz-Martin, J.F.[Joan Francesc], Onrubia, R.[Raul], Pascual, D.[Daniel], Park, H.[Hyuk], Pablos, M.[Miriam], Camps, A.[Adriano], Rüdiger, C.[Christoph], Walker, J.[Jeffrey], Monerris, A.[Alessandra],
Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
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Munoz-Martin, J.F.[Joan Francesc], Llaveria, D.[David], Herbert, C.[Christoph], Pablos, M.[Miriam], Park, H.[Hyuk], Camps, A.[Adriano],
Soil Moisture Estimation Synergy Using GNSS-R and L-Band Microwave Radiometry Data from FSSCat/FMPL-2,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
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Xu, H.Z.[Hong-Zhang], Yuan, Q.Q.[Qiang-Qiang], Li, T.W.[Tong-Wen], Shen, H.F.[Huan-Feng], Zhang, L.P.[Liang-Pei], Jiang, H.T.[Hong-Tao],
Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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Edokossi, K.[Komi], Calabia, A.[Andres], Jin, S.G.[Shuang-Gen], Molina, I.[Iñigo],
GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Azemati, A.[Amir], Melebari, A.[Amer], Campbell, J.D.[James D.], Walker, J.P.[Jeffrey P.], Moghaddam, M.[Mahta],
GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Roberts, T.M.[Thomas Maximillian], Colwell, I.[Ian], Chew, C.[Clara], Lowe, S.[Stephen], Shah, R.[Rashmi],
A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Jia, Y.[Yan], Jin, S.G.[Shuang-Gen], Savi, P.[Patrizia], Yan, Q.Y.[Qing-Yun], Li, W.[Wenmei],
Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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Yin, C.[Cong], Huang, F.X.[Fei-Xiong], Xia, J.M.[Jun-Ming], Bai, W.H.[Wei-Hua], Sun, Y.Q.[Yue-Qiang], Yang, G.[Guanglin], Zhai, X.C.[Xiao-Chun], Xu, N.[Na], Hu, X.Q.[Xiu-Qing], Zhang, P.[Peng], Wang, J.S.[Jin-Song], Du, Q.F.[Qi-Fei], Wang, X.Y.[Xian-Yi], Cai, Y.R.[Yue-Rong],
Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Melebari, A.[Amer], Campbell, J.D.[James D.], Hodges, E.[Erik], Moghaddam, M.[Mahta],
Improved Geometric Optics with Topography (IGOT) Model for GNSS-R Delay-Doppler Maps Using Three-Scale Surface Roughness,
RS(15), No. 7, 2023, pp. 1880.
DOI Link 2304
Global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) BibRef

Munoz-Martin, J.F.[Joan Francesc], Rodriguez-Alvarez, N.[Nereida], Bosch-Lluis, X.[Xavier], Oudrhiri, K.[Kamal],
Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals,
RS(15), No. 8, 2023, pp. 2013.
DOI Link 2305
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Zhang, T.L.[Tian-Long], Yang, L.[Lei], Nan, H.T.[Hong-Tao], Yin, C.[Cong], Sun, B.[Bo], Yang, D.K.[Dong-Kai], Hong, X.[Xuebao], Lopez-Baeza, E.[Ernesto],
In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Ding, Q.[Qin], Liang, Y.[Yueji], Liang, X.Y.[Xing-Yong], Ren, C.[Chao], Yan, H.B.[Hong-Bo], Liu, Y.[Yintao], Zhang, Y.[Yan], Lu, X.J.[Xian-Jian], Lai, J.M.[Jian-Min], Hu, X.M.[Xin-Miao],
Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection,
RS(15), No. 13, 2023, pp. 3218.
DOI Link 2307
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Liu, Q.[Qi], Zhang, S.C.[Shuang-Cheng], Li, W.Q.[Wei-Qiang], Nan, Y.[Yang], Peng, J.[Jilun], Ma, Z.M.[Zhong-Min], Zhou, X.[Xin],
Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data,
RS(15), No. 14, 2023, pp. 3669.
DOI Link 2307
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Luo, Q.[Qidi], Liang, Y.[Yueji], Guo, Y.[Yue], Liang, X.Y.[Xing-Yong], Ren, C.[Chao], Yue, W.T.[Wei-Ting], Zhu, B.L.[Bing-Lin], Jiang, X.[Xueyu],
Enhancing Spatial Resolution of GNSS-R Soil Moisture Retrieval through XGBoost Algorithm-Based Downscaling Approach: A Case Study in the Southern United States,
RS(15), No. 18, 2023, pp. 4576.
DOI Link 2310
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Hu, Q.F.[Qing-Feng], Li, Y.F.[Yi-Fan], Liu, W.K.[Wen-Kai], Lu, W.Q.[Wei-Qiang], Hai, H.X.[Hong-Xin], He, P.P.[Pei-Pei], Liu, X.L.[Xian-Lin], Ma, K.F.[Kai-Feng], Zhu, D.T.[Dan-Tong], Wang, P.[Peng], Kou, Y.C.[Ying-Chao],
Research on Soil Moisture Inversion Method for Canal Slope of the Middle Route Project of the South to North Water Transfer Based on GNSS-R and Deep Learning,
RS(15), No. 17, 2023, pp. 4340.
DOI Link 2310
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Wei, H.H.[Hao-Han], Yang, X.F.[Xiao-Feng], Pan, Y.W.[Yu-Wei], Shen, F.[Fei],
GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects,
RS(15), No. 22, 2023, pp. 5381.
DOI Link 2311
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Zhou, X.[Xin], Zhang, S.C.[Shuang-Cheng], Zhang, Q.[Qin], Liu, Q.[Qi], Ma, Z.M.[Zhong-Min], Wang, T.[Tao], Tian, J.[Jing], Li, X.R.[Xin-Rui],
Research of Deformation and Soil Moisture in Loess Landslide Simultaneous Retrieved with Ground-Based GNSS,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Lwin, A., Yang, D., Hong, X., Shamsabadi, S.C.[S. Cheraghi], Ahmed, W.A.,
Spaceborne GNSS-R Retrieving on Global Soil Moisture Approached By Support Vector Machine Learning,
ISPRS20(B3:605-610).
DOI Link 2012
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Soil Moisture, Sentinel 1, 2, 3, Data .


Last update:Jan 30, 2024 at 20:33:16