Reference

Abbaszadeh, P., Moradkhani, H., Zhan, X. Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method.Water Resour. Res. 2019, 55 , pp.324-344.
Babaeian, E., Sadeghi, M., Franz, T.E., et al. Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sens. Environ. 2018, 211 , pp.425-440.
Bateni, S.M., Entekhabi, D. Relative efficiency of land surface energy balance components. Water Resour. Res. 2012, 48 , pp.W04510.
Chauhan, N.S., Miller, S., Ardanuy, P. Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. Int. J. Remote Sens. 2003, 24 , pp.4599-4622.
Chen, S., She, D., Zhang, L., et al. Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application.Water 2019, 11 , pp.1401.
Djamai, N., Magagi, R., Goïta, K., et al. A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days. Remote Sens. Environ. 2016, 184 , pp.1-14.
Dobriyal, P., Qureshi, A., Badola, R., Hussain, S.A. A review of the methods available for estimating soil moisture and its implications for water resource management. J. Hydrol. 2012, 458-459 , pp.110-117.
Dorigo, W., Wagner, W., Albergel, C., et al. ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions. Remote Sens. Environ. 2017, 203 , pp.185-213.
Duan, S.B., Li, Z.L. Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: Case study in northern China. IEEE Trans. Geosci. Remote Sens. 2016, 54 , pp.6458-6469.
Ge, Y., Jin, Y., Stein, A., et al. Principles and methods of scaling geospatial Earth science data. Earth Sci. Rev. 2019, 197 , pp.102897.
Gerber, F., De Jong, R., Schaepman, M.E., et al. Predicting missing values in spatio-temporal remote sensing data. IEEE Trans. Geosci. Remote Sens. 2018, 56 , pp.2841-2853.
Goovaerts, P. Kriging and semivariogram deconvolution in the presence of irregular geographical units. Math. Geosci. 2008, 40 , pp.101-128.
Im, J., Park, S., Rhee, J., et al. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environ. Earth Sci. 2016, 75 , pp.1120.
Jin, Y., Ge, Y., Wang, J.H., et al. Downscaling AMSR-2 soil moisture data with geographically weighted area-to-area regression kriging.IEEE Trans. Geosci. Remote Sens. 2018, 56 , pp.2362-2376.
Kaheil, Y.H., Gill, M.K., McKee, M., et al. Downscaling and assimilation of surface soil moisture using ground truth measurements. IEEE Trans. Geosci. Remote Sens. 2008, 46 , pp.1375-1384.
Kerkez, B., Glaser, S.D., Bales, R.C., Meadows, M.W. Design and performance of a wireless sensor network for catchment‐scale snow and soil moisture measurements. Water Resour. Res. 2012, 48 , pp.W09515.
Knipper, K.R., Hogue, T.S., Franz, K.J., Scott, R.L. Downscaling SMAP and SMOS soil moisture with moderate-resolution imaging spectroradiometer visible and infrared products over southern Arizona.Int. J. Remote Sens. 2017, 11 , pp.026021.
Krishnan, P., Black, T.A., Grant, N.J., et al. Impact of changing soil moisture distribution on net ecosystem productivity of a boreal aspen forest during and following drought. Agric. For. Meteorol. 2006,139 , pp.208-223.
Kyriakidis, P.C. A geostatistical framework for area-to-point spatial interpolation. Geogr. Anal. 2004, 36 , pp.259-289.
Liu, Y., Yang, Y., Jing, W., Yue, X. Comparison of different machine learning approaches for monthly satellite-based soil moisture downscaling over Northeast China. Remote Sens. 2018, 10 , pp.31.
Malbéteau, Y., Merlin, O., Molero, B., et al. DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia. Int. J. Appl. Earth Obs. Geoinf. 2016, 45 , pp.221-234.
Meissner, T., Wentz, F.J., Vine, D.M.L. The salinity retrieval algorithms for the NASA Aquarius version 5 and SMAP version 3 releases.Remote Sens. 2018, 10, pp.1121.
Merlin, O., Malbéteau, Y., Notfi, Y., et al. Performance metrics for soil moisture downscaling methods: Application to DISPATCH data in central Morocco. Remote Sens. 2015, 7 , pp.3783-3807.
Meyer, D., Dimitriadou, E., Hornik, K., et al. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071). TU Wien. R package version 1.7-3, 2015.
Mukherjee, S., Joshi, P.K., Garg, R.D. Regression-Kriging technique to downscale satellite-derived land surface temperature in heterogeneous agricultural landscape. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8 , pp.1245-1250.
Njoku, E.G., Jackson, T.J., Lakshmi, V., et al. Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens. 2003, 41 , pp.215-229.
Parinussa, R.M., Wang, G., Holmes, T.R.H., et al. Global surface soil moisture from the microwave radiation imager onboard the Fengyun-3b satellite. Int. J. Remote Sens. 2014, 35 , pp.7007-7029.
Peng, J., Loew, A., Merlin, O., et al. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 2017,55 , pp.341-366.
Petropoulos, G.P., Ireland, G., Barrett, B. Surface soil moisture retrievals from remote sensing: current status, products & future trends. Phys. Chem. Earth 2015, 83-84 , pp.36-56.
Piles, M., Entekhabi, D., Camps, A. A change detection algorithm for retrieving high-resolution soil moisture from SMAP radar and radiometer observations. IEEE Trans. Geosci. Remote Sens. 2009, 47 , pp.4125-4131.
Piles, M., Sánchez, N., Vall-llossera, M., et al. A downscaling approach for SMOS land observations: Evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7 , pp.3845-3857.
Seneviratne, S.I., Corti, T., Davin, E. L., et al. Investigating soil moisture–climate interactions in a changing climate: a review.Earth Sci. Rev. 2010, 99 , pp.125-161.
Smola A.J., Schölkopf, B. A tutorial on support vector regression.Stat. Comput. 2004, 14 , pp.199-222.
Song C., Jia L. A method for downscaling Fengyun-3b soil moisture based on apparent thermal inertia. Remote Sens. 2016, 8 , pp.703.
Song, P., Huang, J., Mansaray, L.R. An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression. Agric. For. Meteorol. 2019, 275 , pp.146-158.
Srivastava, P.K., Han, D., Ramirez, M.R., Islam, T. Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour. Manag. 2013, 27 , pp.3127-3144.
Sujay, R.N., Deka, P.C. Support vector machine applications in the field of hydrology: a review. Appl. Soft Comput. 2014, 19 , pp.372-386.
van der Velde, R., Salama, M.S., Eweys, O.A., et al. Soil moisture mapping using combined active/passive microwave observations over the east of the Netherlands. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8 , pp.4355-4372.
Vapnik, V., Golowich, S.E., Smola, A.J. Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems 1997, pp.281-287.
Wagner, W., Dorigo, W., de Jeu, R., et al. Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture. ISPRS Ann. Photogramm. Remote. Sens. Spat. Inform. Sci. 2012, 7 , pp.315-321.
Wang, H., Vicente-Serrano, S.M., Tao, F., et al. Monitoring winter wheat drought threat in northern china using multiple climate-based drought indices and soil moisture during 2000–2013. Agric. For. Meteorol. 2016, 228-229 , pp.1-12.
Wei, Z.S., Meng, Y.Z, Zhang, W., et al. Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau. Remote Sens. Environ. 2019, 225 , pp.30-44.
Wu, X., Walker, J.P., Rüdiger, C., et al. Medium-Resolution Soil Moisture Retrieval Using the Bayesian Merging Method. IEEE Trans. Geosci. Remote Sens. 2017, 55 , pp.6482-6493.
Yang, K., Qin, J., Zhao, L., et al. A multiscale soil moisture and freeze–thaw monitoring network on the third pole. Bull. Amer. Meteorol. Soc. 2013, 94 , pp.1907-1916.
Yang, Y., Guan, H., Long, D., et al. Estimation of surface soil moisture from thermal infrared remote sensing using an improved trapezoid method.Remote Sens. 2015, 7 , pp.8250-8270.
Zhan, X., Houser, P.R., Walker, J.P., Crow, W.T. A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and radar observations. IEEE Trans. Geosci. Remote Sens. 2006,44 , pp.1534-1544.
Zhao, W., Sánchez, N., Lu, H., Li, A. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. J. Hydrol. 2018, 563 , pp.1009-1024.
Zhu, X.C., Shao, M.A., Zeng, C., et al. Application of cosmic-ray neutron sensing to monitor soil water content in an alpine meadow ecosystem on the northern Tibetan plateau. J. Hydrol. 2016,536 , pp.247-254.
Zreda, M., Shuttleworth, W.J., Zeng, X., et al. COSMOS: the COsmic-ray Soil Moisture Observing System. Hydrol. Earth Syst. Sci. 2012,16 , pp.1-21.