1. [1] Pathe C, Wagner W, Sabel D, Doubkova M, Basara JB, "Using ENVISAT ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA", IEEE Transactions on Geoscience and Remote Sensing, 3;47(2):468-80, 2009. [DOI:10.1109/TGRS.2008.2004711]

2. [2] Western AW, Grayson RB, "The Tarrawarra data set: Soil moisture patterns, soil characteristics, and hydrological flux measurements", Water Resources Research, 34(10):2765-8, 1998. [DOI:10.1029/98WR01833]

3. [3] Lunt IA, Hubbard SS, Rubin Y, "Soil moisture content estimation using ground-penetrating radar reflection data", Journal of hydrology, 9;307(1-4):254-69, 2005. [DOI:10.1016/j.jhydrol.2004.10.014]

4. [4] Narayan U, Lakshmi V, "High resolution change detection using TMI-PR and AMSR-E soil moisture data", Water Resources Res, 44: W06425, 2008. [DOI:10.1029/2006WR005817]

5. [5] Njoku EG, Jackson TJ, Lakshmi V, Chan TK, Nghiem SV, "Soil moisture retrieval from AMSR-E", IEEE transactions on Geoscience and remote sensing, 41(2):215-29, 2003. [DOI:10.1109/TGRS.2002.808243]

6. [6] Baup F, Mougin E, De Rosnay P, Timouk F, Chênerie I. Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data. Remote Sensing of Environment, 109(4):473-81, 2007. [DOI:10.1016/j.rse.2007.01.015]

7. [7] Behari J. "Microwave dielectric behaviour of wet soils", Springer Science & Business Media; 2006. [DOI:10.1007/1-4020-3288-9]

8. [8] Zhuo, L.; Han, D, "The relevance of soil moisture by remote sensing and hydrological modelling", Procedia Eng, 154, 1368-1375, 2016. [DOI:10.1016/j.proeng.2016.07.499]

9. [9] Schmugge TJ. "Remote sensing of soil moisture: Recent advances. IEEE Transactions on Geoscience and Remote Sensing, (3):336-44, 1983. [DOI:10.1109/TGRS.1983.350563]

10. [10] Rawat KS, Sehgal VK, Pradhan S, Ray SS, "Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)", Journal of Earth System Science, 1;127(2):18, 2018. [DOI:10.1007/s12040-018-0919-2]

11. [11] Das NN, Mohanty BP, Cosh MH, Jackson TJ, "Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04", Remote Sensing of Environment, 15;112(2):415-29, 2008. [DOI:10.1016/j.rse.2006.10.027]

12. [12] Zhang T, Jiang L, Zhao S, Chai L, Li Y, Pan Y, "Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil", Remote Sensing, 11(17):2028, 2019. [DOI:10.3390/rs11172028]

13. [13] Shi J, Wang J, Hsu AY, O'Neill PE, Engman ET, "Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data", IEEE Transactions on Geoscience and Remote Sensing, 35(5):1254-66, 1997. [DOI:10.1109/36.628792]

14. [14] Li J, Wang S. "Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval". Remote Sensing. 10(9):1370, 2018. [DOI:10.3390/rs10091370]

15. [15] Baghdadi N, Choker M, Zribi M, Hajj M, Paloscia S, Verhoest N, Lievens H, Baup F, Mattia F. "A new empirical model for radar scattering from bare soil surfaces". Remote Sensing. 8(11):920, 2016. [DOI:10.3390/rs8110920]

16. [16] Woodhouse IH, Hoekman DH. "A model-based determination of soil moisture trends in Spain with the ERS-scatterometer". IEEE transactions on geoscience and remote sensing. 38(4):1783-93, 2000. [DOI:10.1109/36.851762]

17. [17] Adeyemi O, Grove I, Peets S, Domun Y, Norton T. "Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling". Sensors. 18(10):3408, 2018. [DOI:10.3390/s18103408]

18. [18] Hsu KL, Gupta HV, Sorooshian S. "Artificial neural network modeling of the rainfall‐runoff process". Water resources research. 31(10):2517-30, 1995. [DOI:10.1029/95WR01955]

19. [19] Sharaf El Din E, Zhang Y, Suliman A. "Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework". International journal of remote sensing. 38(4):1023-42, 2017. [DOI:10.1080/01431161.2016.1275056]

20. [20] Feng Y, Cui N, Hao W, Gao L, Gong D. "Estimation of soil temperature from meteorological data using different machine learning models". Geoderma. 338:67-77, 2019. [DOI:10.1016/j.geoderma.2018.11.044]

21. [21] Chang FJ, Chen YC. "A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction". Journal of hydrology. 245(1-4):153-64, 2001. [DOI:10.1016/S0022-1694(01)00350-X]

22. [22] Abu Qdais H, Shatnawi N. "Assessing and predicting landfill surface temperatura using remote sensing and an artificial neural network". International Journal of Remote Sensing. 2019. [DOI:10.1080/01431161.2019.1633703]

23. [23] Holloway J, Mengersen K. "Statistical machine learning methods and remote sensing for sustainable development goals: A review". Remote Sensing. 10(9):1365, 2018. [DOI:10.3390/rs10091365]

24. [24] Haykin S. "Neural networks: a comprehensive foundation. Prentice" Hall PTR, 1994.

25. [25] Gao Q, Zribi M, Escorihuela M, Baghdadi N. "Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution". Sensors. Sep;17(9):1966. [DOI:10.3390/s17091966]

26. [26] Jiang H, Rusuli Y, Amuti T, He Q, "Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network", International journal of remote sensing, 40(1):284-306, 2019. [DOI:10.1080/01431161.2018.1513180]

27. [27] Gill MK, Asefa T, Kemblowski MW, McKee M. "Soil moisture prediction using support vector machines 1". JAWRA Journal of the American Water Resources Association. Aug;42(4):1033-46, 2006. [DOI:10.1111/j.1752-1688.2006.tb04512.x]

28. [28] Ayehu G, Tadesse T, Gessesse B, Yigrem Y. "Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia". Remote Sensing. Jan;11(2):125, 2019. [DOI:10.3390/rs11020125]

29. [29] Kalra A, Ahmad S. "Using oceanic‐atmospheric oscillations for long lead time streamflow forecasting". Water Resources Research. Mar;45(3), 2009. [DOI:10.1029/2008WR006855]

30. [30] Khalil AF, McKee M, Kemblowski M, Asefa T, Bastidas L. "Multiobjective analysis of chaotic dynamic systems with sparse learning machines". Advances in Water Resources. Jan 1;29(1):72-88, 2006. [DOI:10.1016/j.advwatres.2005.05.011]

31. [31] Raczko E, Zagajewski B. "Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images". European Journal of Remote Sensing. Jan 1;50(1):144-54, 2017. [DOI:10.1080/22797254.2017.1299557]

32. [32] Tripathi S, Srinivas VV, Nanjundiah RS. "Downscaling of precipitation for climate change scenarios: a support vector machine approach", Journal of hydrology. Nov 15;330(3-4):621-40, 2006. [DOI:10.1016/j.jhydrol.2006.04.030]

33. [33] Zeng W, Zhang D, Fang Y, Wu J, Huang J. "Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data", Journal of Applied Remote Sensing. Jan;12(2):022204, 2018. [DOI:10.1117/1.JRS.12.022204]

34. [34] Aboutalebi M, Allen LN, Torres-Rua AF, McKee M, Coopmans C. "Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery. InAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping", International Society for Optics and Photonics IV, (Vol. 11008, p. 110080S), 2019. [DOI:10.1117/12.2519743]

35. [35] Lin JY, Cheng CT, Chau KW. "Using support vector machines for long-term discharge prediction", Hydrological Sciences Journal. 51(4):599-612, 2006. [DOI:10.1623/hysj.51.4.599]

36. [36] Roodposhti MS, Safarrad T, Shahabi H. "Drought sensitivity mapping using two one-class support vector machine algorithms". Atmospheric research, 193:73-82, 2017. [DOI:10.1016/j.atmosres.2017.04.017]

37. [37] Were K, Bui DT, Dick ØB, Singh BR. "A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape". Ecological Indicators. 52:394-403, 2015. [DOI:10.1016/j.ecolind.2014.12.028]

38. [38] Wang H, Magagi R, Goïta K, Trudel M, McNairn H, Powers J. "Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm". Remote Sensing of Environment. Sep 15; 231:111234, 2019. [DOI:10.1016/j.rse.2019.111234]

39. [39] McNairn, H., K. Gottfried, and J. Powers. SMAPVEX16 Manitoba Core-Based in Situ Soil Moisture Data, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. 2018. doi: [DOI:10.5067/D4YA3124Y3BR. [Date Accessed].]

40. [40] Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press; 1992. [DOI:10.7551/mitpress/1090.001.0001]

41. [41] Bandyopadhyay S, Maulik U, Mukhopadhyay A, "Multiobjective genetic clustering for pixel classification in remote sensing imagery", IEEE transactions on Geoscience and Remote Sensing, 45(5):1506-11, 2007. [DOI:10.1109/TGRS.2007.892604]

42. [42] Ghamisi P, Benediktsson JA. Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geoscience and remote sensing letters, 12(2):309-13, 2014. [DOI:10.1109/LGRS.2014.2337320]

43. [43] Ulaby, F. T., Batlivala, P. P., & Dobson, M. C. "Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: part I-bare soil", IEEE Transactions on Geoscience Electronics, 16(4), 286-295, 1978. [DOI:10.1109/TGE.1978.294586]