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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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,
DOI Link 2012

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