Evaluation and improvement of temporal robustness and transfer performance of surface soil moisture estimated by machine learning regression algorithms
•Upper and lower limits on the accuracy of soil moisture estimation are evaluated using advanced regression models with multi-source data.•The three proposed strategies can improve the temporal transfer performance of soil moisture.•Soil parameters are crucial for the temporal robustness and transfe...
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| Vydáno v: | Computers and electronics in agriculture Ročník 217; s. 108518 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.02.2024
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| Témata: | |
| ISSN: | 0168-1699, 1872-7107 |
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| Abstract | •Upper and lower limits on the accuracy of soil moisture estimation are evaluated using advanced regression models with multi-source data.•The three proposed strategies can improve the temporal transfer performance of soil moisture.•Soil parameters are crucial for the temporal robustness and transferability of soil moisture estimation.•Factors affecting the accuracy of soil moisture in heterogeneous regions are analyzed.
The accurate monitoring of the spatio-temporal distribution of surface soil moisture (SSM) is crucial to understanding Earth's water cycle. The machine learning regression (MLR) algorithm has brought a new breakthrough to SSM remote sensing estimation due to its powerful nonlinear fitting ability. However, the high-precision estimation of temporal SSM still faces challenges. In this study, we evaluated the temporal robustness and transfer performance of six MLR algorithms for SSM estimation and conducted a universality experiment in agricultural areas with abundant land cover types. First, the factors affecting the estimation accuracy of SSM were analyzed from the perspectives of dynamic variables (backscattering, multi-spectrum, and brightness temperature [TB]) and steady variables (soil texture [ST] and soil roughness [SR]). Results showed that radar incidence angle (RIA) is the key factor for estimating SSM accurately from dual polarization backscattering (dual-pol σ) data. The introduction of multi-spectrum data can considerably improve the estimation accuracy of SSM and is better than that of TB data. The physical measures (ST) or geometric measures (SR) can be used as auxiliary features for multi-spectrum or TB data, which improve the accuracy of SSM estimated from dual-pol σ data, with similar improvement effects. Multi-source data can effectively reduce the adverse effects of surface heterogeneity on SSM estimation. Ensemble learning and kernel learning algorithms have similar performance in the case of low-dimensional features and small samples. However, the neural network algorithms can correctly reflect the temporal variation of SSM only in the case of multi-dimensional features and large samples. The optimal accuracy of multi-temporal SSM is estimated by the Gaussian process regression algorithm (RMSE = 0.028 cm3/cm3). Moreover, to address poor transfer performance of temporal SSM in MLR models (RMSE > 0.060 cm3/cm3), this study proposed three strategies including the constraint of target domain samples, scattering model, and clustering model. The optimal error (RMSE) of multi-temporal SSM estimated by the three strategies is 0.032, 0.045, and 0.045 cm3/cm3. Overall, the strategies proposed can mitigate the issue of overestimation or underestimation resulting from the inconsistent distribution of SSM and RIAs at various phases. The soil parameters, acting as the medium across different phases, can notably enhance the temporal transfer performance of SSM regression models. This study introduces a novel framework for achieving high estimation accuracy and transfer performance of temporal SSM at a regional scale. |
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| AbstractList | •Upper and lower limits on the accuracy of soil moisture estimation are evaluated using advanced regression models with multi-source data.•The three proposed strategies can improve the temporal transfer performance of soil moisture.•Soil parameters are crucial for the temporal robustness and transferability of soil moisture estimation.•Factors affecting the accuracy of soil moisture in heterogeneous regions are analyzed.
The accurate monitoring of the spatio-temporal distribution of surface soil moisture (SSM) is crucial to understanding Earth's water cycle. The machine learning regression (MLR) algorithm has brought a new breakthrough to SSM remote sensing estimation due to its powerful nonlinear fitting ability. However, the high-precision estimation of temporal SSM still faces challenges. In this study, we evaluated the temporal robustness and transfer performance of six MLR algorithms for SSM estimation and conducted a universality experiment in agricultural areas with abundant land cover types. First, the factors affecting the estimation accuracy of SSM were analyzed from the perspectives of dynamic variables (backscattering, multi-spectrum, and brightness temperature [TB]) and steady variables (soil texture [ST] and soil roughness [SR]). Results showed that radar incidence angle (RIA) is the key factor for estimating SSM accurately from dual polarization backscattering (dual-pol σ) data. The introduction of multi-spectrum data can considerably improve the estimation accuracy of SSM and is better than that of TB data. The physical measures (ST) or geometric measures (SR) can be used as auxiliary features for multi-spectrum or TB data, which improve the accuracy of SSM estimated from dual-pol σ data, with similar improvement effects. Multi-source data can effectively reduce the adverse effects of surface heterogeneity on SSM estimation. Ensemble learning and kernel learning algorithms have similar performance in the case of low-dimensional features and small samples. However, the neural network algorithms can correctly reflect the temporal variation of SSM only in the case of multi-dimensional features and large samples. The optimal accuracy of multi-temporal SSM is estimated by the Gaussian process regression algorithm (RMSE = 0.028 cm3/cm3). Moreover, to address poor transfer performance of temporal SSM in MLR models (RMSE > 0.060 cm3/cm3), this study proposed three strategies including the constraint of target domain samples, scattering model, and clustering model. The optimal error (RMSE) of multi-temporal SSM estimated by the three strategies is 0.032, 0.045, and 0.045 cm3/cm3. Overall, the strategies proposed can mitigate the issue of overestimation or underestimation resulting from the inconsistent distribution of SSM and RIAs at various phases. The soil parameters, acting as the medium across different phases, can notably enhance the temporal transfer performance of SSM regression models. This study introduces a novel framework for achieving high estimation accuracy and transfer performance of temporal SSM at a regional scale. |
| ArticleNumber | 108518 |
| Author | Jie, Yang Jiaxin, Qian Lingli, Zhao Weidong, Sun Lei, Shi Chaoya, Dang |
| Author_xml | – sequence: 1 givenname: Qian surname: Jiaxin fullname: Jiaxin, Qian organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China – sequence: 2 givenname: Yang surname: Jie fullname: Jie, Yang organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China – sequence: 3 givenname: Sun surname: Weidong fullname: Weidong, Sun email: widensun2012@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China – sequence: 4 givenname: Zhao surname: Lingli fullname: Lingli, Zhao organization: College of Remote Sensing Information Engineering, Wuhan University, Wuhan 430070, China – sequence: 5 givenname: Shi surname: Lei fullname: Lei, Shi organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China – sequence: 6 givenname: Dang surname: Chaoya fullname: Chaoya, Dang organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China |
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| Cites_doi | 10.3390/rs13112099 10.1109/TGE.1978.294586 10.1016/j.rse.2019.111402 10.1016/j.jhydrol.2017.11.023 10.1109/TGRS.2018.2858004 10.3390/rs14163858 10.1029/2021GL092856 10.1016/0034-4257(90)90087-3 10.3390/rs12142303 10.1007/978-3-540-28650-9_4 10.1109/TGRS.1981.350328 10.1109/JSTARS.2021.3096063 10.1109/TGRS.2002.807587 10.1016/j.rse.2019.111215 10.3390/rs13152856 10.1145/3136625 10.1016/j.rse.2020.111958 10.1109/TGRS.2020.2976661 10.1109/JSTARS.2020.3043628 10.1016/j.advwatres.2017.09.010 10.1016/j.rse.2018.12.026 10.3390/s16081308 10.1016/j.rse.2022.113059 10.1016/j.pce.2015.02.009 10.1109/TGRS.2020.3033887 10.1016/j.rse.2021.112485 10.1109/TPAMI.2005.159 10.1109/LGRS.2022.3168982 10.1109/TGRS.2018.2848285 10.1016/j.rse.2016.04.006 10.1016/j.rse.2019.111380 10.1109/TGRS.2011.2168962 10.1371/journal.pone.0169748 10.1016/j.rse.2020.111954 10.1029/JC087iC13p11229 10.1145/2939672.2939785 10.1002/hyp.3360070205 10.1016/j.rse.2016.01.027 10.1016/j.rse.2017.07.015 10.3390/rs10091327 10.1109/TGRS.2014.2364913 10.1016/j.jmp.2018.03.001 10.1016/j.isprsjprs.2021.05.013 10.1016/j.jhydrol.2021.126698 10.1016/j.jhydrol.2013.11.018 10.1109/36.917912 10.1016/j.rse.2020.111716 10.1117/1.JRS.15.018503 10.1016/j.asr.2006.02.032 10.1007/BFb0020217 10.1016/j.advwatres.2017.09.006 10.1016/j.jhydrol.2019.124351 10.1007/s10994-006-6226-1 10.1016/j.catena.2005.08.005 10.1016/j.isprsjprs.2022.01.005 10.1109/JSTARS.2021.3058325 10.1080/15481603.2020.1857123 10.1109/JSTARS.2015.2469717 10.1016/j.rse.2017.06.014 10.1109/36.942542 10.1016/j.rse.2021.112706 10.1016/j.isprsjprs.2022.06.012 10.3390/rs9121292 10.1109/LGRS.2011.2106109 10.1109/JSTARS.2021.3098513 10.1016/j.isprsjprs.2019.06.012 10.1016/j.jhydrol.2015.06.058 10.1109/72.97934 10.2136/sssaj1984.03615995004800010026x 10.1016/S0034-4257(03)00051-8 10.1023/A:1025667309714 10.1109/TGE.1979.294626 10.1111/gcb.16043 10.1109/LGRS.2020.2965558 10.1109/36.628792 10.1016/j.rse.2022.112900 10.1007/s11707-009-0023-7 10.3390/rs14081863 10.1080/01431160500239032 10.3390/rs13193889 10.1016/j.rse.2017.08.025 10.1016/j.earscirev.2010.02.004 10.1016/j.rse.2022.113132 10.1016/j.compeleceng.2013.11.024 10.1016/j.rse.2018.08.003 10.1080/15481603.2018.1489943 10.1109/TGRS.2019.2959239 10.1002/2017GL073904 10.1186/s40537-016-0043-6 10.1016/j.rse.2022.113334 |
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| References | He (b0135) 2021; 14 Jackson (b0165) 1993; 7 Baghdadi, Holah, Zribi (b0005) 2006; 27 Dang (b0080) 2021; 28 Hengl (b0145) 2017; 12 Schölkopf, B., Smola, A. and Müller, K.-R., 1997. Kernel principal component analysis, International conference on artificial neural networks. Springer, pp. 583-588. McNairn (b0275) 2014; 53 Ma, Li, McCabe (b0240) 2020; 12 Ma, Wang, Zhao, Ma (b0245) 2021; 13 He, Zhang, Ren, Sun (b0140) 2016 Yue, Tian, Tian, Xu, Xu (b0490) 2019; 154 Feng (b0110) 2021; 13 Gao (b0115) 2022; 277 Ge, Hang, Liu, Liu (b0120) 2018; 10 Lee, Sohn, Park, Jang (b0195) 2019; 56 Manns, Berg (b0260) 2014; 516 El Hajj, Baghdadi, Zribi, Bazzi (b0105) 2017; 9 Mo, Choudhury, Schmugge, Wang, Jackson (b0290) 1982; 87 Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794. Nicolai-Shaw, Zscheischler, Hirschi, Gudmundsson, Seneviratne (b0295) 2017; 203 Chan (b0035) 2018; 204 Ulaby, F.T., Moore, R.K., Fung, A.K., 1986. Radar remote sensing and surface scattering and emission theory. O'Neill, Jackson (b0300) 1990; 31 Owe, de Jeu, Walker (b0305) 2001; 39 Bauer-Marschallinger (b0015) 2018; 57 Dobson, Ulaby (b0095) 1981; 1 Li, W., et al., 2021b. Revisiting global vegetation controls using multi‐layer soil moisture. Geophys. Res. Lett., 48(11), e2021GL092856. Ma (b0235) 2019; 231 Huang (b0155) 2015; 9 Snoek, J., Larochelle, H., Adams, R.P., 2012. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25. Wang, Azzari, Lobell (b0435) 2019; 222 Chandrashekar, Sahin (b0040) 2014; 40 Zhao (b0515) 2022; 14 Chang, Shoshany, Oh (b0045) 2018; 56 Shi (b0375) 2021; 261 Yu, Zhao (b0475) 2010; 35 Wang (b0430) 2023; 284 McNairn (b0280) 2017 Zhang, Zhou (b0505) 2016; 16 Shi, Wang, Hsu, O'Neill, Engman (b0380) 1997; 35 Bao, Lin, Wu, Deng, Petropoulos (b0010) 2018; 72 Zhang (b0495) 2022; 279 Ulaby, Bradley, Dobson (b0405) 1979; 17 Chen (b0055) 2003; 41 Verrelst, Alonso, Camps-Valls, Delegido, Moreno (b0420) 2012; 50 Yuan (b0480) 2020; 241 Manns, Berg, Colliander (b0265) 2015; 528 Ranjbar (b0330) 2021; 15 Huang, Dumitru, Pan, Lei, Datcu (b0160) 2020; 18 Weiss, Jacob, Duveiller (b0450) 2020; 236 Wigneron (b0465) 2003; 85 Zhang, Liang, Zhu, Ma, He (b0500) 2022; 185 Karthikeyan, Pan, Wanders, Kumar, Wood (b0185) 2017; 109 Wang, Qu (b0445) 2009; 3 Li (b0200) 2017; 50 Weiss, Khoshgoftaar, Wang (b0455) 2016; 3 Bouman, C.A., Shapiro, M., Cook, G., Atkins, C.B, Cheng, H., 1997. Cluster: An unsupervised algorithm for modeling Gaussian mixtures. Dabrowska-Zielinska, Inoue, Kowalik, Gruszczynska (b0075) 2007; 39 Lievens (b0215) 2017; 44 El Hajj (b0100) 2016; 176 Specht (b0395) 1991; 2 Veloso (b0415) 2017; 199 Chen (b0060) 2021; 104 Schulz, Speekenbrink, Krause (b0365) 2018; 85 Chaubell (b0050) 2020; 58 Parrens (b0315) 2016; 181 De Roo, Du, Ulaby, Dobson (b0090) 2001; 39 Xue, Zhang, Zhang, Li (b0470) 2022; 60 Liu, Qian, Yue (b0225) 2020; 14 Wen (b0460) 2022; 190 Karthikeyan, Pan, Wanders, Kumar, Wood (b0190) 2017; 109 McNairn, H., Gottfried, K., Powers, J., 2018. SMAPVEX16 manitoba land cover classification map. Version 1, Proc. NASA Nat. Snow Ice Data Center Distrib. Act. Arch. Center. Shirazi, Boersma (b0385) 1984; 48 Bhogapurapu (b0020) 2021; 178 Bhogapurapu (b0025) 2022; 271 Mao (b0270) 2022; 14 Ranjbar, Akhoondzadeh, Brisco, Amani, Hosseini (b0335) 2021; 14 Li (b0205) 2021; 600 Mandal (b0255) 2020; 58 Das (b0085) 2019; 233 Petropoulos, Ireland, Barrett (b0325) 2015; 83 Wang (b0425) 2021; 58 Lievens, Verhoest (b0220) 2011; 8 Palmisano (b0310) 2021; 59 Jarray, Abbes, Farah (b0170) 2022; 19 Greifeneder, Notarnicola, Wagner (b0130) 2021; 13 Mandal (b0250) 2020; 247 Holzman, Rivas, Piccolo (b0150) 2014; 28 Peng, Long, Ding (b0320) 2005; 27 Karthikeyan, Mishra (b0180) 2021; 266 Wang, Magagi, Goïta (b0440) 2018; 217 Zhao (b0510) 2020; 248 Liu, Qian, Yue (b0230) 2021; 14 Sagi, Rokach (b0355) 2018; 8 Robnik-Šikonja, Kononenko (b0345) 2003; 53 Ulaby, Batlivala, Dobson (b0400) 1978; 16 Jester, Klik (b0175) 2005; 64 Geurts, Ernst, Wehenkel (b0125) 2006; 63 Yuan, Xu, Li, Shen, Zhang (b0485) 2020; 580 Rasmussen, C.E., 2003. Gaussian processes in machine learning, Summer school on machine learning. Springer, pp. 63-71. Rowlandson (b0350) 2018; 556 Cloude (b0070) 2007; 644 Seneviratne (b0370) 2010; 99 Karthikeyan (10.1016/j.compag.2023.108518_b0180) 2021; 266 Liu (10.1016/j.compag.2023.108518_b0225) 2020; 14 Chen (10.1016/j.compag.2023.108518_b0060) 2021; 104 Wang (10.1016/j.compag.2023.108518_b0435) 2019; 222 Palmisano (10.1016/j.compag.2023.108518_b0310) 2021; 59 Yuan (10.1016/j.compag.2023.108518_b0480) 2020; 241 Chandrashekar (10.1016/j.compag.2023.108518_b0040) 2014; 40 Wang (10.1016/j.compag.2023.108518_b0445) 2009; 3 Ma (10.1016/j.compag.2023.108518_b0235) 2019; 231 Weiss (10.1016/j.compag.2023.108518_b0455) 2016; 3 Chen (10.1016/j.compag.2023.108518_b0055) 2003; 41 Peng (10.1016/j.compag.2023.108518_b0320) 2005; 27 Manns (10.1016/j.compag.2023.108518_b0260) 2014; 516 Mao (10.1016/j.compag.2023.108518_b0270) 2022; 14 Dang (10.1016/j.compag.2023.108518_b0080) 2021; 28 Robnik-Šikonja (10.1016/j.compag.2023.108518_b0345) 2003; 53 Cloude (10.1016/j.compag.2023.108518_b0070) 2007; 644 Lievens (10.1016/j.compag.2023.108518_b0220) 2011; 8 Zhang (10.1016/j.compag.2023.108518_b0500) 2022; 185 Ma (10.1016/j.compag.2023.108518_b0240) 2020; 12 El Hajj (10.1016/j.compag.2023.108518_b0105) 2017; 9 Li (10.1016/j.compag.2023.108518_b0200) 2017; 50 Ge (10.1016/j.compag.2023.108518_b0120) 2018; 10 10.1016/j.compag.2023.108518_b0065 10.1016/j.compag.2023.108518_b0340 Yu (10.1016/j.compag.2023.108518_b0475) 2010; 35 Bhogapurapu (10.1016/j.compag.2023.108518_b0020) 2021; 178 Veloso (10.1016/j.compag.2023.108518_b0415) 2017; 199 Huang (10.1016/j.compag.2023.108518_b0155) 2015; 9 Feng (10.1016/j.compag.2023.108518_b0110) 2021; 13 Nicolai-Shaw (10.1016/j.compag.2023.108518_b0295) 2017; 203 Zhang (10.1016/j.compag.2023.108518_b0505) 2016; 16 Jarray (10.1016/j.compag.2023.108518_b0170) 2022; 19 Chan (10.1016/j.compag.2023.108518_b0035) 2018; 204 Wang (10.1016/j.compag.2023.108518_b0430) 2023; 284 10.1016/j.compag.2023.108518_b0210 O'Neill (10.1016/j.compag.2023.108518_b0300) 1990; 31 He (10.1016/j.compag.2023.108518_b0135) 2021; 14 Shi (10.1016/j.compag.2023.108518_b0375) 2021; 261 Wang (10.1016/j.compag.2023.108518_b0425) 2021; 58 Verrelst (10.1016/j.compag.2023.108518_b0420) 2012; 50 Dabrowska-Zielinska (10.1016/j.compag.2023.108518_b0075) 2007; 39 El Hajj (10.1016/j.compag.2023.108518_b0100) 2016; 176 Specht (10.1016/j.compag.2023.108518_b0395) 1991; 2 Baghdadi (10.1016/j.compag.2023.108518_b0005) 2006; 27 Geurts (10.1016/j.compag.2023.108518_b0125) 2006; 63 He (10.1016/j.compag.2023.108518_b0140) 2016 Yue (10.1016/j.compag.2023.108518_b0490) 2019; 154 Ulaby (10.1016/j.compag.2023.108518_b0405) 1979; 17 Ranjbar (10.1016/j.compag.2023.108518_b0330) 2021; 15 Seneviratne (10.1016/j.compag.2023.108518_b0370) 2010; 99 Hengl (10.1016/j.compag.2023.108518_b0145) 2017; 12 Karthikeyan (10.1016/j.compag.2023.108518_b0185) 2017; 109 10.1016/j.compag.2023.108518_b0285 Parrens (10.1016/j.compag.2023.108518_b0315) 2016; 181 Wen (10.1016/j.compag.2023.108518_b0460) 2022; 190 Greifeneder (10.1016/j.compag.2023.108518_b0130) 2021; 13 Rowlandson (10.1016/j.compag.2023.108518_b0350) 2018; 556 Shirazi (10.1016/j.compag.2023.108518_b0385) 1984; 48 Huang (10.1016/j.compag.2023.108518_b0160) 2020; 18 Mo (10.1016/j.compag.2023.108518_b0290) 1982; 87 Schulz (10.1016/j.compag.2023.108518_b0365) 2018; 85 10.1016/j.compag.2023.108518_b0390 Bauer-Marschallinger (10.1016/j.compag.2023.108518_b0015) 2018; 57 Manns (10.1016/j.compag.2023.108518_b0265) 2015; 528 McNairn (10.1016/j.compag.2023.108518_b0275) 2014; 53 Ma (10.1016/j.compag.2023.108518_b0245) 2021; 13 Bao (10.1016/j.compag.2023.108518_b0010) 2018; 72 10.1016/j.compag.2023.108518_b0030 Chang (10.1016/j.compag.2023.108518_b0045) 2018; 56 Jackson (10.1016/j.compag.2023.108518_b0165) 1993; 7 Das (10.1016/j.compag.2023.108518_b0085) 2019; 233 Karthikeyan (10.1016/j.compag.2023.108518_b0190) 2017; 109 Chaubell (10.1016/j.compag.2023.108518_b0050) 2020; 58 Shi (10.1016/j.compag.2023.108518_b0380) 1997; 35 Mandal (10.1016/j.compag.2023.108518_b0255) 2020; 58 Xue (10.1016/j.compag.2023.108518_b0470) 2022; 60 Ulaby (10.1016/j.compag.2023.108518_b0400) 1978; 16 Gao (10.1016/j.compag.2023.108518_b0115) 2022; 277 De Roo (10.1016/j.compag.2023.108518_b0090) 2001; 39 Weiss (10.1016/j.compag.2023.108518_b0450) 2020; 236 Ranjbar (10.1016/j.compag.2023.108518_b0335) 2021; 14 Sagi (10.1016/j.compag.2023.108518_b0355) 2018; 8 Jester (10.1016/j.compag.2023.108518_b0175) 2005; 64 Lievens (10.1016/j.compag.2023.108518_b0215) 2017; 44 McNairn (10.1016/j.compag.2023.108518_b0280) 2017 Bhogapurapu (10.1016/j.compag.2023.108518_b0025) 2022; 271 Holzman (10.1016/j.compag.2023.108518_b0150) 2014; 28 Liu (10.1016/j.compag.2023.108518_b0230) 2021; 14 Zhao (10.1016/j.compag.2023.108518_b0515) 2022; 14 Yuan (10.1016/j.compag.2023.108518_b0485) 2020; 580 10.1016/j.compag.2023.108518_b0410 Zhang (10.1016/j.compag.2023.108518_b0495) 2022; 279 Wigneron (10.1016/j.compag.2023.108518_b0465) 2003; 85 Lee (10.1016/j.compag.2023.108518_b0195) 2019; 56 Wang (10.1016/j.compag.2023.108518_b0440) 2018; 217 Mandal (10.1016/j.compag.2023.108518_b0250) 2020; 247 Li (10.1016/j.compag.2023.108518_b0205) 2021; 600 Petropoulos (10.1016/j.compag.2023.108518_b0325) 2015; 83 Dobson (10.1016/j.compag.2023.108518_b0095) 1981; 1 Owe (10.1016/j.compag.2023.108518_b0305) 2001; 39 Zhao (10.1016/j.compag.2023.108518_b0510) 2020; 248 10.1016/j.compag.2023.108518_b0360 |
| References_xml | – volume: 28 start-page: 181 year: 2014 end-page: 192 ident: b0150 article-title: Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 203 start-page: 216 year: 2017 end-page: 225 ident: b0295 article-title: A drought event composite analysis using satellite remote-sensing based soil moisture publication-title: Remote Sens. Environ. – volume: 41 start-page: 90 year: 2003 end-page: 101 ident: b0055 article-title: Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 28 start-page: 2066 year: 2021 end-page: 2080 ident: b0080 article-title: Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence publication-title: Glob. Chang. Biol – volume: 99 start-page: 125 year: 2010 end-page: 161 ident: b0370 article-title: Investigating soil moisture–climate interactions in a changing climate: a review publication-title: Earth Sci. Rev. – volume: 31 start-page: 175 year: 1990 end-page: 182 ident: b0300 article-title: Observed effects of soil organic matter content on the microwave emissivity of soils publication-title: Remote Sens. Environ. – volume: 72 start-page: 76 year: 2018 end-page: 85 ident: b0010 article-title: Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 87 start-page: 11229 year: 1982 end-page: 11237 ident: b0290 article-title: A model for microwave emission from vegetation-covered fields publication-title: J. Geophys. Res. Oceans – volume: 64 start-page: 174 year: 2005 end-page: 192 ident: b0175 article-title: Soil surface roughness measurement—methods, applicability, and surface representation publication-title: Catena – volume: 190 start-page: 252 year: 2022 end-page: 266 ident: b0460 article-title: Mapping corn dynamics using limited but representative samples with adaptive strategies publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 10 start-page: 1327 year: 2018 ident: b0120 article-title: Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations publication-title: Remote Sens. (Basel) – volume: 14 start-page: 7448 year: 2021 end-page: 7465 ident: b0230 article-title: Comprehensive evaluation of Sentinel-2 red edge and shortwave-infrared bands to estimate soil moisture publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 59 start-page: 7308 year: 2021 end-page: 7321 ident: b0310 article-title: Sentinel-1 Sensitivity to Soil Moisture at High Incidence Angle and the Impact on Retrieval Over Seasonal Crops publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 14 start-page: 3858 year: 2022 ident: b0270 article-title: A spatial downscaling method for remote sensing soil moisture based on random forest considering soil moisture memory and mass conservation publication-title: Remote Sens. (Basel) – volume: 19 start-page: 1 year: 2022 end-page: 5 ident: b0170 article-title: A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 58 start-page: 48 year: 2021 end-page: 67 ident: b0425 article-title: Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands publication-title: GIScience & Remote Sensing – volume: 199 start-page: 415 year: 2017 end-page: 426 ident: b0415 article-title: Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications publication-title: Remote Sens. Environ. – volume: 104 year: 2021 ident: b0060 article-title: Convolutional neural network model for soil moisture prediction and its transferability analysis based on laboratory Vis-NIR spectral data publication-title: Int. J. Appl. Earth Obs. Geoinf. – reference: Rasmussen, C.E., 2003. Gaussian processes in machine learning, Summer school on machine learning. Springer, pp. 63-71. – volume: 2 start-page: 568 year: 1991 end-page: 576 ident: b0395 article-title: A general regression neural network publication-title: IEEE Trans. Neural Netw. – volume: 14 start-page: 2833 year: 2021 end-page: 2844 ident: b0135 article-title: An improved method for soil moisture monitoring with ensemble learning methods over the Tibetan plateau publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – reference: Li, W., et al., 2021b. Revisiting global vegetation controls using multi‐layer soil moisture. Geophys. Res. Lett., 48(11), e2021GL092856. – volume: 600 year: 2021 ident: b0205 article-title: Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning publication-title: J. Hydrol. – volume: 15 start-page: 018503 year: 2021 ident: b0330 article-title: Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model publication-title: J. Appl. Remote Sens. – volume: 85 start-page: 1 year: 2018 end-page: 16 ident: b0365 article-title: A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions publication-title: J. Math. Psychol. – volume: 231 year: 2019 ident: b0235 article-title: Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: a comprehensive assessment using global ground-based observations publication-title: Remote Sens. Environ. – volume: 109 start-page: 236 year: 2017 end-page: 252 ident: b0190 article-title: Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons publication-title: Adv. Water Resour. – volume: 40 start-page: 16 year: 2014 end-page: 28 ident: b0040 article-title: A survey on feature selection methods publication-title: Comput. Electr. Eng. – volume: 39 start-page: 1643 year: 2001 end-page: 1654 ident: b0305 article-title: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index publication-title: IEEE Trans. Geosci. Remote Sens. – reference: Snoek, J., Larochelle, H., Adams, R.P., 2012. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25. – volume: 35 start-page: 318 year: 2010 end-page: 321 ident: b0475 article-title: A new method for soil moisture inversion by synthetic aperture radar publication-title: Geomatics Inform Sci. Wuhan University – volume: 16 start-page: 1308 year: 2016 ident: b0505 article-title: Estimation of soil moisture from optical and thermal remote sensing: A review publication-title: Sensors – volume: 53 start-page: 2784 year: 2014 end-page: 2801 ident: b0275 article-title: The soil moisture active passive validation experiment 2012 (SMAPVEX12): Prelaunch calibration and validation of the SMAP soil moisture algorithms publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 53 start-page: 23 year: 2003 end-page: 69 ident: b0345 article-title: Theoretical and Empirical Analysis of ReliefF and RReliefF publication-title: Mach. Learn. – volume: 83 start-page: 36 year: 2015 end-page: 56 ident: b0325 article-title: Surface soil moisture retrievals from remote sensing: Current status, products & future trends publication-title: Phys. Chem. the Earth, Parts A/B/C – reference: Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794. – volume: 241 year: 2020 ident: b0480 article-title: Deep learning in environmental remote sensing: Achievements and challenges publication-title: Remote Sens. Environ. – volume: 57 start-page: 520 year: 2018 end-page: 539 ident: b0015 article-title: Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 556 start-page: 349 year: 2018 end-page: 358 ident: b0350 article-title: Temporal transferability of soil moisture calibration equations publication-title: J. Hydrol. – volume: 13 start-page: 2099 year: 2021 ident: b0130 article-title: A machine learning-based approach for surface soil moisture estimations with google earth engine publication-title: Remote Sens. (Basel) – volume: 18 start-page: 107 year: 2020 end-page: 111 ident: b0160 article-title: Classification of large-scale high-resolution SAR images with deep transfer learning publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 580 year: 2020 ident: b0485 article-title: Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental US publication-title: J. Hydrol. – volume: 7 start-page: 139 year: 1993 end-page: 152 ident: b0165 article-title: III. Measuring surface soil moisture using passive microwave remote sensing publication-title: Hydrol. Process. – volume: 13 start-page: 3889 year: 2021 ident: b0245 article-title: Global sensitivity analysis of a water cloud model toward soil moisture retrieval over vegetated agricultural fields publication-title: Remote Sens. (Basel) – volume: 154 start-page: 216 year: 2019 end-page: 230 ident: b0490 article-title: Development of soil moisture indices from differences in water absorption between shortwave-infrared bands publication-title: ISPRS J. Photogramm. Remote Sens. – year: 2017 ident: b0280 article-title: SMAPVEX16 Database Report – volume: 48 start-page: 142 year: 1984 end-page: 147 ident: b0385 article-title: A unifying quantitative analysis of soil texture publication-title: Soil Sci. Soc. Am. J. – volume: 56 start-page: 7102 year: 2018 end-page: 7108 ident: b0045 article-title: Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems publication-title: IEEE Trans. Geosci. Remote Sens. – reference: Schölkopf, B., Smola, A. and Müller, K.-R., 1997. Kernel principal component analysis, International conference on artificial neural networks. Springer, pp. 583-588. – volume: 16 start-page: 286 year: 1978 end-page: 295 ident: b0400 article-title: Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part I-bare soil publication-title: IEEE Trans. Geosci. Electron. – volume: 248 year: 2020 ident: b0510 article-title: Soil moisture retrievals using L-band radiometry from variable angular ground-based and airborne observations publication-title: Remote Sens. Environ. – volume: 12 start-page: 2303 year: 2020 ident: b0240 article-title: Retrieval of high-resolution soil moisture through combination of Sentinel-1 and Sentinel-2 data publication-title: Remote Sens. (Basel) – volume: 181 start-page: 122 year: 2016 end-page: 136 ident: b0315 article-title: Global-scale surface roughness effects at L-band as estimated from SMOS observations publication-title: Remote Sens. Environ. – volume: 63 start-page: 3 year: 2006 end-page: 42 ident: b0125 article-title: Extremely randomized trees publication-title: Mach Learn – volume: 279 year: 2022 ident: b0495 article-title: A machine learning method trained by radiative transfer model inversion for generating seven global land and atmospheric estimates from VIIRS top-of-atmosphere observations publication-title: Remote Sens. Environ. – volume: 50 start-page: 1 year: 2017 end-page: 45 ident: b0200 article-title: Feature selection: A data perspective publication-title: ACM Computing Surveys (CSUR) – volume: 204 start-page: 931 year: 2018 end-page: 941 ident: b0035 article-title: Development and assessment of the SMAP enhanced passive soil moisture product publication-title: Remote Sens. Environ. – volume: 3 start-page: 1 year: 2016 end-page: 40 ident: b0455 article-title: A survey of transfer learning publication-title: J. Big data – volume: 178 start-page: 20 year: 2021 end-page: 35 ident: b0020 article-title: Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 27 start-page: 1226 year: 2005 end-page: 1238 ident: b0320 article-title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 516 start-page: 297 year: 2014 end-page: 303 ident: b0260 article-title: Importance of soil organic carbon on surface soil water content variability among agricultural fields publication-title: J. Hydrol. – volume: 261 year: 2021 ident: b0375 article-title: Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques publication-title: Remote Sens. Environ. – volume: 8 start-page: 740 year: 2011 end-page: 744 ident: b0220 article-title: On the retrieval of soil moisture in wheat fields from L-band SAR based on water cloud modeling, the IEM, and effective roughness parameters publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 3 start-page: 237 year: 2009 end-page: 247 ident: b0445 article-title: Satellite remote sensing applications for surface soil moisture monitoring: A review publication-title: Front Earth Sci. China – volume: 85 start-page: 489 year: 2003 end-page: 506 ident: b0465 article-title: Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans publication-title: Remote Sens. Environ. – volume: 8 start-page: e1249 year: 2018 ident: b0355 article-title: Ensemble learning: A survey publication-title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery – volume: 13 start-page: 2856 year: 2021 ident: b0110 article-title: Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI publication-title: Remote Sens. (Basel) – volume: 644 start-page: 2 year: 2007 ident: b0070 article-title: The dual polarization entropy/alpha decomposition: A PALSAR case study publication-title: Sci. Appl. SAR Polarimetry Polarimetric Interferometry – volume: 35 start-page: 1254 year: 1997 end-page: 1266 ident: b0380 article-title: Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 271 year: 2022 ident: b0025 article-title: Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data publication-title: Remote Sens. Environ. – volume: 1 start-page: 51 year: 1981 end-page: 61 ident: b0095 article-title: Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part III-soil tension publication-title: IEEE Trans. Geoscience and Remote Sensing – reference: Bouman, C.A., Shapiro, M., Cook, G., Atkins, C.B, Cheng, H., 1997. Cluster: An unsupervised algorithm for modeling Gaussian mixtures. – volume: 17 start-page: 33 year: 1979 end-page: 40 ident: b0405 article-title: Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part II-vegetation-covered soil publication-title: IEEE Trans. Geosci. Electron. – volume: 39 start-page: 864 year: 2001 end-page: 872 ident: b0090 article-title: A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 109 start-page: 106 year: 2017 end-page: 120 ident: b0185 article-title: Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms publication-title: Adv. Water Resour. – start-page: 770 year: 2016 end-page: 778 ident: b0140 article-title: Deep residual learning for image recognition publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 56 start-page: 43 year: 2019 end-page: 67 ident: b0195 article-title: Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea publication-title: GIScience Remote Sensing – volume: 185 start-page: 32 year: 2022 end-page: 47 ident: b0500 article-title: Soil moisture content retrieval from Landsat 8 data using ensemble learning publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 277 year: 2022 ident: b0115 article-title: A deep neural network based SMAP soil moisture product publication-title: Remote Sens. Environ. – volume: 60 start-page: 1 year: 2022 end-page: 17 ident: b0470 article-title: Ensemble Learning Embedded With Gaussian Process Regression for Soil Moisture Estimation: A Case Study of the Continental US publication-title: IEEE Trans. Geosci. Remote Sens. – reference: Ulaby, F.T., Moore, R.K., Fung, A.K., 1986. Radar remote sensing and surface scattering and emission theory. – volume: 284 year: 2023 ident: b0430 article-title: 1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed publication-title: Remote Sens. Environ. – volume: 44 start-page: 6145 year: 2017 end-page: 6153 ident: b0215 article-title: Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates publication-title: Geophys. Res. Lett. – volume: 9 start-page: 1292 year: 2017 ident: b0105 article-title: Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas publication-title: Remote Sens. (Basel) – volume: 12 start-page: e0169748 year: 2017 ident: b0145 article-title: SoilGrids250m: Global gridded soil information based on machine learning publication-title: PLoS One – volume: 217 start-page: 38 year: 2018 end-page: 51 ident: b0440 article-title: Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields publication-title: Remote Sens. Environ. – volume: 9 start-page: 272 year: 2015 end-page: 284 ident: b0155 article-title: Coherent model of L-band radar scattering by soybean plants: Model development, evaluation, and retrieval publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – reference: McNairn, H., Gottfried, K., Powers, J., 2018. SMAPVEX16 manitoba land cover classification map. Version 1, Proc. NASA Nat. Snow Ice Data Center Distrib. Act. Arch. Center. – volume: 14 start-page: 7179 year: 2021 end-page: 7197 ident: b0335 article-title: Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 27 start-page: 1907 year: 2006 end-page: 1920 ident: b0005 article-title: Soil moisture estimation using multi-incidence and multi-polarization ASAR data publication-title: Int. J. Remote Sens. – volume: 266 year: 2021 ident: b0180 article-title: Multi-layer high-resolution soil moisture estimation using machine learning over the United States publication-title: Remote Sens. Environ. – volume: 236 year: 2020 ident: b0450 article-title: Remote sensing for agricultural applications: A meta-review publication-title: Remote Sens. Environ. – volume: 39 start-page: 139 year: 2007 end-page: 148 ident: b0075 article-title: Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis publication-title: Adv. Space Res. – volume: 58 start-page: 6321 year: 2020 end-page: 6335 ident: b0255 article-title: A radar vegetation index for crop monitoring using compact polarimetric SAR data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 14 start-page: 1863 year: 2022 ident: b0515 article-title: An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends publication-title: Remote Sens. (Basel) – volume: 50 start-page: 1832 year: 2012 end-page: 1843 ident: b0420 article-title: Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 528 start-page: 643 year: 2015 end-page: 651 ident: b0265 article-title: Soil organic carbon as a factor in passive microwave retrievals of soil water content over agricultural croplands publication-title: J. Hydrol. – volume: 222 start-page: 303 year: 2019 end-page: 317 ident: b0435 article-title: Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques publication-title: Remote Sens. Environ. – volume: 176 start-page: 202 year: 2016 end-page: 218 ident: b0100 article-title: Soil moisture retrieval over irrigated grassland using X-band SAR data publication-title: Remote Sens. Environ. – volume: 233 year: 2019 ident: b0085 article-title: The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product publication-title: Remote Sens. Environ. – volume: 247 year: 2020 ident: b0250 article-title: Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data publication-title: Remote Sens. Environ. – volume: 58 start-page: 3894 year: 2020 end-page: 3905 ident: b0050 article-title: Improved SMAP dual-channel algorithm for the retrieval of soil moisture publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 14 start-page: 1292 year: 2020 end-page: 1310 ident: b0225 article-title: Combined Sentinel-1A with Sentinel-2A to estimate soil moisture in farmland publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 13 start-page: 2099 issue: 11 year: 2021 ident: 10.1016/j.compag.2023.108518_b0130 article-title: A machine learning-based approach for surface soil moisture estimations with google earth engine publication-title: Remote Sens. (Basel) doi: 10.3390/rs13112099 – volume: 16 start-page: 286 issue: 4 year: 1978 ident: 10.1016/j.compag.2023.108518_b0400 article-title: Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part I-bare soil publication-title: IEEE Trans. Geosci. Electron. doi: 10.1109/TGE.1978.294586 – volume: 236 year: 2020 ident: 10.1016/j.compag.2023.108518_b0450 article-title: Remote sensing for agricultural applications: A meta-review publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111402 – volume: 556 start-page: 349 year: 2018 ident: 10.1016/j.compag.2023.108518_b0350 article-title: Temporal transferability of soil moisture calibration equations publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2017.11.023 – volume: 57 start-page: 520 issue: 1 year: 2018 ident: 10.1016/j.compag.2023.108518_b0015 article-title: Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2858004 – volume: 14 start-page: 3858 issue: 16 year: 2022 ident: 10.1016/j.compag.2023.108518_b0270 article-title: A spatial downscaling method for remote sensing soil moisture based on random forest considering soil moisture memory and mass conservation publication-title: Remote Sens. (Basel) doi: 10.3390/rs14163858 – ident: 10.1016/j.compag.2023.108518_b0410 – volume: 104 year: 2021 ident: 10.1016/j.compag.2023.108518_b0060 article-title: Convolutional neural network model for soil moisture prediction and its transferability analysis based on laboratory Vis-NIR spectral data publication-title: Int. J. Appl. Earth Obs. Geoinf. – year: 2017 ident: 10.1016/j.compag.2023.108518_b0280 – ident: 10.1016/j.compag.2023.108518_b0210 doi: 10.1029/2021GL092856 – ident: 10.1016/j.compag.2023.108518_b0285 – volume: 31 start-page: 175 issue: 3 year: 1990 ident: 10.1016/j.compag.2023.108518_b0300 article-title: Observed effects of soil organic matter content on the microwave emissivity of soils publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(90)90087-3 – volume: 12 start-page: 2303 issue: 14 year: 2020 ident: 10.1016/j.compag.2023.108518_b0240 article-title: Retrieval of high-resolution soil moisture through combination of Sentinel-1 and Sentinel-2 data publication-title: Remote Sens. (Basel) doi: 10.3390/rs12142303 – ident: 10.1016/j.compag.2023.108518_b0340 doi: 10.1007/978-3-540-28650-9_4 – volume: 1 start-page: 51 year: 1981 ident: 10.1016/j.compag.2023.108518_b0095 article-title: Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part III-soil tension publication-title: IEEE Trans. Geoscience and Remote Sensing doi: 10.1109/TGRS.1981.350328 – volume: 644 start-page: 2 year: 2007 ident: 10.1016/j.compag.2023.108518_b0070 article-title: The dual polarization entropy/alpha decomposition: A PALSAR case study publication-title: Sci. Appl. SAR Polarimetry Polarimetric Interferometry – volume: 14 start-page: 7179 year: 2021 ident: 10.1016/j.compag.2023.108518_b0335 article-title: Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2021.3096063 – volume: 41 start-page: 90 issue: 1 year: 2003 ident: 10.1016/j.compag.2023.108518_b0055 article-title: Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2002.807587 – volume: 60 start-page: 1 year: 2022 ident: 10.1016/j.compag.2023.108518_b0470 article-title: Ensemble Learning Embedded With Gaussian Process Regression for Soil Moisture Estimation: A Case Study of the Continental US publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 231 year: 2019 ident: 10.1016/j.compag.2023.108518_b0235 article-title: Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: a comprehensive assessment using global ground-based observations publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111215 – volume: 13 start-page: 2856 issue: 15 year: 2021 ident: 10.1016/j.compag.2023.108518_b0110 article-title: Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI publication-title: Remote Sens. (Basel) doi: 10.3390/rs13152856 – volume: 50 start-page: 1 issue: 6 year: 2017 ident: 10.1016/j.compag.2023.108518_b0200 article-title: Feature selection: A data perspective publication-title: ACM Computing Surveys (CSUR) doi: 10.1145/3136625 – volume: 248 year: 2020 ident: 10.1016/j.compag.2023.108518_b0510 article-title: Soil moisture retrievals using L-band radiometry from variable angular ground-based and airborne observations publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111958 – volume: 58 start-page: 6321 issue: 9 year: 2020 ident: 10.1016/j.compag.2023.108518_b0255 article-title: A radar vegetation index for crop monitoring using compact polarimetric SAR data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.2976661 – volume: 14 start-page: 1292 year: 2020 ident: 10.1016/j.compag.2023.108518_b0225 article-title: Combined Sentinel-1A with Sentinel-2A to estimate soil moisture in farmland publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2020.3043628 – volume: 109 start-page: 236 year: 2017 ident: 10.1016/j.compag.2023.108518_b0190 article-title: Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2017.09.010 – volume: 222 start-page: 303 year: 2019 ident: 10.1016/j.compag.2023.108518_b0435 article-title: Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.12.026 – volume: 16 start-page: 1308 issue: 8 year: 2016 ident: 10.1016/j.compag.2023.108518_b0505 article-title: Estimation of soil moisture from optical and thermal remote sensing: A review publication-title: Sensors doi: 10.3390/s16081308 – volume: 277 year: 2022 ident: 10.1016/j.compag.2023.108518_b0115 article-title: A deep neural network based SMAP soil moisture product publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2022.113059 – volume: 83 start-page: 36 year: 2015 ident: 10.1016/j.compag.2023.108518_b0325 article-title: Surface soil moisture retrievals from remote sensing: Current status, products & future trends publication-title: Phys. Chem. the Earth, Parts A/B/C doi: 10.1016/j.pce.2015.02.009 – volume: 59 start-page: 7308 issue: 9 year: 2021 ident: 10.1016/j.compag.2023.108518_b0310 article-title: Sentinel-1 Sensitivity to Soil Moisture at High Incidence Angle and the Impact on Retrieval Over Seasonal Crops publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.3033887 – volume: 261 year: 2021 ident: 10.1016/j.compag.2023.108518_b0375 article-title: Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112485 – volume: 27 start-page: 1226 issue: 8 year: 2005 ident: 10.1016/j.compag.2023.108518_b0320 article-title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.159 – volume: 19 start-page: 1 year: 2022 ident: 10.1016/j.compag.2023.108518_b0170 article-title: A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2022.3168982 – volume: 56 start-page: 7102 issue: 12 year: 2018 ident: 10.1016/j.compag.2023.108518_b0045 article-title: Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2848285 – volume: 181 start-page: 122 year: 2016 ident: 10.1016/j.compag.2023.108518_b0315 article-title: Global-scale surface roughness effects at L-band as estimated from SMOS observations publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.04.006 – volume: 233 year: 2019 ident: 10.1016/j.compag.2023.108518_b0085 article-title: The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111380 – start-page: 770 year: 2016 ident: 10.1016/j.compag.2023.108518_b0140 article-title: Deep residual learning for image recognition – volume: 50 start-page: 1832 issue: 5 year: 2012 ident: 10.1016/j.compag.2023.108518_b0420 article-title: Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2168962 – volume: 12 start-page: e0169748 issue: 2 year: 2017 ident: 10.1016/j.compag.2023.108518_b0145 article-title: SoilGrids250m: Global gridded soil information based on machine learning publication-title: PLoS One doi: 10.1371/journal.pone.0169748 – volume: 247 year: 2020 ident: 10.1016/j.compag.2023.108518_b0250 article-title: Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111954 – volume: 87 start-page: 11229 issue: C13 year: 1982 ident: 10.1016/j.compag.2023.108518_b0290 article-title: A model for microwave emission from vegetation-covered fields publication-title: J. Geophys. Res. Oceans doi: 10.1029/JC087iC13p11229 – ident: 10.1016/j.compag.2023.108518_b0065 doi: 10.1145/2939672.2939785 – volume: 28 start-page: 181 year: 2014 ident: 10.1016/j.compag.2023.108518_b0150 article-title: Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 7 start-page: 139 issue: 2 year: 1993 ident: 10.1016/j.compag.2023.108518_b0165 article-title: III. Measuring surface soil moisture using passive microwave remote sensing publication-title: Hydrol. Process. doi: 10.1002/hyp.3360070205 – volume: 176 start-page: 202 year: 2016 ident: 10.1016/j.compag.2023.108518_b0100 article-title: Soil moisture retrieval over irrigated grassland using X-band SAR data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.01.027 – volume: 8 start-page: e1249 issue: 4 year: 2018 ident: 10.1016/j.compag.2023.108518_b0355 article-title: Ensemble learning: A survey publication-title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery – volume: 199 start-page: 415 year: 2017 ident: 10.1016/j.compag.2023.108518_b0415 article-title: Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.07.015 – volume: 10 start-page: 1327 issue: 9 year: 2018 ident: 10.1016/j.compag.2023.108518_b0120 article-title: Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations publication-title: Remote Sens. (Basel) doi: 10.3390/rs10091327 – volume: 53 start-page: 2784 issue: 5 year: 2014 ident: 10.1016/j.compag.2023.108518_b0275 article-title: The soil moisture active passive validation experiment 2012 (SMAPVEX12): Prelaunch calibration and validation of the SMAP soil moisture algorithms publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2364913 – volume: 85 start-page: 1 year: 2018 ident: 10.1016/j.compag.2023.108518_b0365 article-title: A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions publication-title: J. Math. Psychol. doi: 10.1016/j.jmp.2018.03.001 – volume: 178 start-page: 20 year: 2021 ident: 10.1016/j.compag.2023.108518_b0020 article-title: Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2021.05.013 – volume: 600 year: 2021 ident: 10.1016/j.compag.2023.108518_b0205 article-title: Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2021.126698 – volume: 516 start-page: 297 year: 2014 ident: 10.1016/j.compag.2023.108518_b0260 article-title: Importance of soil organic carbon on surface soil water content variability among agricultural fields publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.11.018 – volume: 39 start-page: 864 issue: 4 year: 2001 ident: 10.1016/j.compag.2023.108518_b0090 article-title: A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.917912 – volume: 241 year: 2020 ident: 10.1016/j.compag.2023.108518_b0480 article-title: Deep learning in environmental remote sensing: Achievements and challenges publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111716 – volume: 15 start-page: 018503 issue: 1 year: 2021 ident: 10.1016/j.compag.2023.108518_b0330 article-title: Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model publication-title: J. Appl. Remote Sens. doi: 10.1117/1.JRS.15.018503 – volume: 39 start-page: 139 issue: 1 year: 2007 ident: 10.1016/j.compag.2023.108518_b0075 article-title: Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis publication-title: Adv. Space Res. doi: 10.1016/j.asr.2006.02.032 – ident: 10.1016/j.compag.2023.108518_b0360 doi: 10.1007/BFb0020217 – volume: 109 start-page: 106 year: 2017 ident: 10.1016/j.compag.2023.108518_b0185 article-title: Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2017.09.006 – volume: 580 year: 2020 ident: 10.1016/j.compag.2023.108518_b0485 article-title: Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental US publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2019.124351 – volume: 35 start-page: 318 issue: 3 year: 2010 ident: 10.1016/j.compag.2023.108518_b0475 article-title: A new method for soil moisture inversion by synthetic aperture radar publication-title: Geomatics Inform Sci. Wuhan University – volume: 63 start-page: 3 issue: 1 year: 2006 ident: 10.1016/j.compag.2023.108518_b0125 article-title: Extremely randomized trees publication-title: Mach Learn doi: 10.1007/s10994-006-6226-1 – ident: 10.1016/j.compag.2023.108518_b0390 – volume: 64 start-page: 174 issue: 2 year: 2005 ident: 10.1016/j.compag.2023.108518_b0175 article-title: Soil surface roughness measurement—methods, applicability, and surface representation publication-title: Catena doi: 10.1016/j.catena.2005.08.005 – volume: 185 start-page: 32 year: 2022 ident: 10.1016/j.compag.2023.108518_b0500 article-title: Soil moisture content retrieval from Landsat 8 data using ensemble learning publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2022.01.005 – volume: 14 start-page: 2833 year: 2021 ident: 10.1016/j.compag.2023.108518_b0135 article-title: An improved method for soil moisture monitoring with ensemble learning methods over the Tibetan plateau publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2021.3058325 – volume: 58 start-page: 48 issue: 1 year: 2021 ident: 10.1016/j.compag.2023.108518_b0425 article-title: Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands publication-title: GIScience & Remote Sensing doi: 10.1080/15481603.2020.1857123 – volume: 9 start-page: 272 issue: 1 year: 2015 ident: 10.1016/j.compag.2023.108518_b0155 article-title: Coherent model of L-band radar scattering by soybean plants: Model development, evaluation, and retrieval publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2015.2469717 – volume: 203 start-page: 216 year: 2017 ident: 10.1016/j.compag.2023.108518_b0295 article-title: A drought event composite analysis using satellite remote-sensing based soil moisture publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.06.014 – volume: 39 start-page: 1643 issue: 8 year: 2001 ident: 10.1016/j.compag.2023.108518_b0305 article-title: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.942542 – volume: 266 year: 2021 ident: 10.1016/j.compag.2023.108518_b0180 article-title: Multi-layer high-resolution soil moisture estimation using machine learning over the United States publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112706 – volume: 190 start-page: 252 year: 2022 ident: 10.1016/j.compag.2023.108518_b0460 article-title: Mapping corn dynamics using limited but representative samples with adaptive strategies publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2022.06.012 – volume: 9 start-page: 1292 issue: 12 year: 2017 ident: 10.1016/j.compag.2023.108518_b0105 article-title: Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas publication-title: Remote Sens. (Basel) doi: 10.3390/rs9121292 – volume: 8 start-page: 740 issue: 4 year: 2011 ident: 10.1016/j.compag.2023.108518_b0220 article-title: On the retrieval of soil moisture in wheat fields from L-band SAR based on water cloud modeling, the IEM, and effective roughness parameters publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2011.2106109 – volume: 14 start-page: 7448 year: 2021 ident: 10.1016/j.compag.2023.108518_b0230 article-title: Comprehensive evaluation of Sentinel-2 red edge and shortwave-infrared bands to estimate soil moisture publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2021.3098513 – volume: 154 start-page: 216 year: 2019 ident: 10.1016/j.compag.2023.108518_b0490 article-title: Development of soil moisture indices from differences in water absorption between shortwave-infrared bands publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.06.012 – volume: 528 start-page: 643 year: 2015 ident: 10.1016/j.compag.2023.108518_b0265 article-title: Soil organic carbon as a factor in passive microwave retrievals of soil water content over agricultural croplands publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.06.058 – volume: 2 start-page: 568 issue: 6 year: 1991 ident: 10.1016/j.compag.2023.108518_b0395 article-title: A general regression neural network publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.97934 – volume: 48 start-page: 142 issue: 1 year: 1984 ident: 10.1016/j.compag.2023.108518_b0385 article-title: A unifying quantitative analysis of soil texture publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1984.03615995004800010026x – volume: 85 start-page: 489 issue: 4 year: 2003 ident: 10.1016/j.compag.2023.108518_b0465 article-title: Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(03)00051-8 – volume: 53 start-page: 23 issue: 1 year: 2003 ident: 10.1016/j.compag.2023.108518_b0345 article-title: Theoretical and Empirical Analysis of ReliefF and RReliefF publication-title: Mach. Learn. doi: 10.1023/A:1025667309714 – volume: 17 start-page: 33 issue: 2 year: 1979 ident: 10.1016/j.compag.2023.108518_b0405 article-title: Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part II-vegetation-covered soil publication-title: IEEE Trans. Geosci. Electron. doi: 10.1109/TGE.1979.294626 – volume: 72 start-page: 76 year: 2018 ident: 10.1016/j.compag.2023.108518_b0010 article-title: Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 28 start-page: 2066 year: 2021 ident: 10.1016/j.compag.2023.108518_b0080 article-title: Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence publication-title: Glob. Chang. Biol doi: 10.1111/gcb.16043 – volume: 18 start-page: 107 issue: 1 year: 2020 ident: 10.1016/j.compag.2023.108518_b0160 article-title: Classification of large-scale high-resolution SAR images with deep transfer learning publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2020.2965558 – volume: 35 start-page: 1254 issue: 5 year: 1997 ident: 10.1016/j.compag.2023.108518_b0380 article-title: Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.628792 – volume: 271 year: 2022 ident: 10.1016/j.compag.2023.108518_b0025 article-title: Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2022.112900 – volume: 3 start-page: 237 issue: 2 year: 2009 ident: 10.1016/j.compag.2023.108518_b0445 article-title: Satellite remote sensing applications for surface soil moisture monitoring: A review publication-title: Front Earth Sci. China doi: 10.1007/s11707-009-0023-7 – volume: 14 start-page: 1863 issue: 8 year: 2022 ident: 10.1016/j.compag.2023.108518_b0515 article-title: An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends publication-title: Remote Sens. (Basel) doi: 10.3390/rs14081863 – volume: 27 start-page: 1907 issue: 10 year: 2006 ident: 10.1016/j.compag.2023.108518_b0005 article-title: Soil moisture estimation using multi-incidence and multi-polarization ASAR data publication-title: Int. J. Remote Sens. doi: 10.1080/01431160500239032 – volume: 13 start-page: 3889 issue: 19 year: 2021 ident: 10.1016/j.compag.2023.108518_b0245 article-title: Global sensitivity analysis of a water cloud model toward soil moisture retrieval over vegetated agricultural fields publication-title: Remote Sens. (Basel) doi: 10.3390/rs13193889 – volume: 204 start-page: 931 year: 2018 ident: 10.1016/j.compag.2023.108518_b0035 article-title: Development and assessment of the SMAP enhanced passive soil moisture product publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.08.025 – volume: 99 start-page: 125 issue: 3–4 year: 2010 ident: 10.1016/j.compag.2023.108518_b0370 article-title: Investigating soil moisture–climate interactions in a changing climate: a review publication-title: Earth Sci. Rev. doi: 10.1016/j.earscirev.2010.02.004 – volume: 279 year: 2022 ident: 10.1016/j.compag.2023.108518_b0495 article-title: A machine learning method trained by radiative transfer model inversion for generating seven global land and atmospheric estimates from VIIRS top-of-atmosphere observations publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2022.113132 – ident: 10.1016/j.compag.2023.108518_b0030 – volume: 40 start-page: 16 issue: 1 year: 2014 ident: 10.1016/j.compag.2023.108518_b0040 article-title: A survey on feature selection methods publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2013.11.024 – volume: 217 start-page: 38 year: 2018 ident: 10.1016/j.compag.2023.108518_b0440 article-title: Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.08.003 – volume: 56 start-page: 43 issue: 1 year: 2019 ident: 10.1016/j.compag.2023.108518_b0195 article-title: Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea publication-title: GIScience Remote Sensing doi: 10.1080/15481603.2018.1489943 – volume: 58 start-page: 3894 issue: 6 year: 2020 ident: 10.1016/j.compag.2023.108518_b0050 article-title: Improved SMAP dual-channel algorithm for the retrieval of soil moisture publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2959239 – volume: 44 start-page: 6145 issue: 12 year: 2017 ident: 10.1016/j.compag.2023.108518_b0215 article-title: Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates publication-title: Geophys. Res. Lett. doi: 10.1002/2017GL073904 – volume: 3 start-page: 1 issue: 1 year: 2016 ident: 10.1016/j.compag.2023.108518_b0455 article-title: A survey of transfer learning publication-title: J. Big data doi: 10.1186/s40537-016-0043-6 – volume: 284 year: 2023 ident: 10.1016/j.compag.2023.108518_b0430 article-title: 1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2022.113334 |
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| SubjectTerms | Machine learning regression SMAPVEX16-MB Surface soil moisture Temporal robustness Transfer learning |
| Title | Evaluation and improvement of temporal robustness and transfer performance of surface soil moisture estimated by machine learning regression algorithms |
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