[[1] Callaghan MV, Head FA, Cey EE, Bentley LR. Salt leaching in fine-grained, macroporous soil: Negative effects of excessive matrix saturation. Agricult Water Manage. 2017;181:73-84. DOI: 10.1016/j.agwat.2016.11.025.10.1016/j.agwat.2016.11.025]Open DOISearch in Google Scholar
[[2] He K, Yang Y, Yang Y, Chen S, Hu Q, Liu X, et al. Hydrus simulation of sustainable brackish water irrigation in a winter wheat-summer maize rotation system in the north china plain. Water. 2017;9(7):536. DOI: 10.3390/w9070536.10.3390/w9070536]Open DOISearch in Google Scholar
[[3] Trujillo-González J, Mahecha-Pulido J, Torres-Mora M, Brevik E, Keesstra S, Jiménez-Ballesta R. Impact of potentially contaminated river water on agricultural irrigated soils in an equatorial climate. Agriculture. 2017;7(7):52. DOI: 10.3390/agriculture7070052.10.3390/7070052]Open DOISearch in Google Scholar
[[4] Li Y, Šimůnek J, Wang S, Yuan J, Zhang W. Modeling of soil water regime and water balance in a transplanted rice field experiment with reduced irrigation. Water. 2017;9(4):248. DOI: 10.3390/w9040248.10.3390/w9040248]Open DOISearch in Google Scholar
[[5] García-Garizábal I, Causapé J, Merchán D. Evaluation of alternatives for flood irrigation and water usage in spain under mediterranean climate. CATENA. 2017;155:127-134. DOI: 10.1016/j.catena.2017.02.019.10.1016/j.catena.2017.02.019]Open DOISearch in Google Scholar
[[6] Jalali V, Asadi Kapourchal S, Homaee M. Evaluating performance of macroscopic water uptake models at productive growth stages of durum wheat under saline conditions. Agricult Water Manage. 2017;180:13-21. DOI: 10.1016/j.agwat.2016.10.015.10.1016/j.agwat.2016.10.015]Open DOISearch in Google Scholar
[[7] Hassan-Esfahani L, Torres-Rua A, Jensen A, Mckee M. Spatial root zone soil water content estimation in agricultural lands using bayesian-based artificial neural networks and high-resolution visual, nir, and thermal imagery. Irrigation Drainage. 2017;66(2):273-288. DOI: 10.1002/ird.2098.10.1002/ird.2098]Open DOISearch in Google Scholar
[[8] Veihmeyer FJ, Hendrickson AH. The moisture equivalent as a measure of the field capacity of soils. Soil Sci. 1931;32(3):181-194. DOI: 10.1097/00010694-193109000-00003.10.1097/00010694-193109000-00003]Search in Google Scholar
[[9] Shepherd KD, Walsh MG. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J. 2002;66(3):988-998. DOI: DOI: 10.2136/sssaj2002.9880.10.2136/sssaj2002.9880]Open DOISearch in Google Scholar
[[10] Nanni MR, Demattê JAM. Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci Soc Am J. 2006;70:393-407. DOI: 10.2136/sssaj2003.0285.10.2136/sssaj2003.0285]Search in Google Scholar
[[11] Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, et al. An extended AVHRR 8-km NDVI dataset compatible with modis and spot vegetation NDVI data. Int J Remote Sens. 2005;26(20):4485-4498. DOI: 10.1080/01431160500168686.10.1080/01431160500168686]Search in Google Scholar
[[12] Liu G, Guo H, Yan S, Song R, Ruan Z, Lv M. Revealing the surge behaviour of the yangtze river headwater glacier during 1989-2015 with tandem-x and landsat images. J Glaciology. 2017;63(238):382-386. DOI: 10.1017/jog.2017.4.10.1017/jog.2017.4]Open DOISearch in Google Scholar
[[13] Shahtahmassebi AR, Lin Y, Lin L, Atkinson PM, Moore N, Wang K, et al. Reconstructing historical land cover type and complexity by synergistic use of landsat multispectral scanner and corona. Remote Sensing. 2017;9(7):682. DOI: 10.3390/rs9070682.10.3390/rs9070682]Open DOISearch in Google Scholar
[[14] Yu H, Kong B, Wang G, Du R, Qie G. Prediction of soil properties using a hyperspectral remote sensing method. Archives Agronomy Soil Sci. 2017:1-14. DOI: 10.1080/03650340.2017.1359416.10.1080/03650340.2017.1359416]Open DOISearch in Google Scholar
[[15] Rocha Neto O, Teixeira A, Leão R, Moreira L, Galvão L. Hyperspectral remote sensing for detecting soil salinization using prospectir-vs aerial imagery and sensor simulation. Remote Sensing. 2017;9(1):42. DOI: 10.3390/rs9010042.10.3390/rs9010042]Open DOISearch in Google Scholar
[[16] Ben-Dor E, Chabrillat S, Demattê JAM, Taylor GR, Hill J, Whiting ML, et al. Using imaging spectroscopy to study soil properties. Remote Sens Environ. 2009;113:S38-S55. DOI: 10.1016/j.rse.2008.09.019.10.1016/j.rse.2008.09.019]Open DOISearch in Google Scholar
[[17] Calzolari C, Ungaro F. Predicting shallow water table depth at regional scale from rainfall and soil data. J Hydrol. 2012;414:374-387. DOI: 10.1016/j.jhydrol.2011.11.008.10.1016/j.jhydrol.2011.11.008]Open DOISearch in Google Scholar
[[18] Vauclin M, Vieira S, Vachaud G, Nielsen D. The use of cokriging with limited field soil observations. Soil Sci Soc Am J. 1983;47(2):175-184. DOI: 10.2136/sssaj1983.03615995004700020001x.10.2136/sssaj1983.03615995004700020001x]Open DOISearch in Google Scholar
[[19] Sun RH, Liu QL, Chen LD. Study on precipitation based on the geostatistical analyst method. J China Hydrol. 2010;30(1):14-18. DOI: 10.3969/j.issn.1000-0852.2010.01.003.10.3969/j.issn.1000-0852.2010.01.003]Open DOISearch in Google Scholar
[[20] Yates SR, Warrick AW. Estimating soil water content using cokriging. Soil Sci Soc Am J. 1987;51(1):23-30. DOI: 10.2136/sssaj1987.03615995005100010005x.10.2136/sssaj1987.03615995005100010005x]Open DOISearch in Google Scholar
[[21] Ghadermazi J, Sayyad G, Mohammadi J, Moezzi A, Ahmadi F, Schulin R. Spatial prediction of nitrate concentration in drinking water using ph as auxiliary co-kriging variable. Procedia Environ Sci. 2011;3(0):130-135. DOI: 10.1016/j.proenv.2011.02.023.10.1016/j.proenv.2011.02.023]Search in Google Scholar
[[22] Regalado CM, Ritter A, Rodríguez-González RM. Performance of the commercial wet capacitance sensor as compared with time domain reflectometry in volcanic soils. Vadose Zone J. 2007;6(2):244-254. DOI: 10.2136/vzj2006.0138.10.2136/vzj2006.0138]Open DOISearch in Google Scholar
[[23] Blonquist Jr J, Jones SB, Robinson D. A time domain transmission sensor with tdr performance characteristics. J Hydrol. 2005;314(1):235-245. DOI: 10.1016/j.jhydrol.2005.04.005.10.1016/j.jhydrol.2005.04.005]Open DOISearch in Google Scholar
[[24] Manfreda S, Brocca L, Moramarco T, Melone F, Sheffield J. A physically based approach for the estimation of root-zone soil moisture from surface measurements. Hydrol Earth Syst Sci. 2014;18(3):1199-1212. DOI: 10.5194/hess-18-1199-2014.10.5194/hess-18-1199-2014]Open DOISearch in Google Scholar
[[25] Noborio K. Measurement of soil water content and electrical conductivity by time domain reflectometry: A review. Comput Electron Agr. 2001;31(3):213-237. DOI: 10.1016/S0168-1699(00)00184-8.10.1016/S0168-1699(00)00184-8]Open DOISearch in Google Scholar
[[26] Kilmer VJ, Alexander LT. Methods of making mechanical analyses of soils. Soil Sci. 1949;68(1):15-24. DOI: 10.1097/00010694-194907000-00003.10.1097/00010694-194907000-00003]Open DOISearch in Google Scholar
[[27] Geladi P, Kowalski BR. Partial least-squares regression: A tutorial. Analytica Chim Acta. 1986;185:1-17. DOI: 10.1016/0003-2670(86)80028-9.10.1016/0003-2670(86)80028-9]Open DOISearch in Google Scholar
[[28] Wold S, Ruhe A, Wold H, Dunn WJ. The collinearity problem in linear regression. The partial least squares (pls) approach to generalized inverses. SIAM J Sci Stat Computing. 1984;5(3):735-743. DOI: 10.1137/0905052.10.1137/0905052]Open DOISearch in Google Scholar
[[29] Helland IS. On the structure of partial least squares regression. Communic Statistics-Simul Comput. 1988;17(2):581-607. DOI: 10.1080/03610918808812681.10.1080/03610918808812681]Open DOISearch in Google Scholar
[[30] Abdi H. Partial least squares regression and projection on latent structure regression (pls regression). Wiley Interdisciplin Reviews: Computat Statistics. 2010;2(1):97-106. DOI: 10.1002/wics.51.10.1002/wics.51]Open DOISearch in Google Scholar
[[31] Gomez C, Viscarra Rossel RA, Mcbratney AB. Soil organic carbon prediction by hyperspectral remote sensing and field vis-nir spectroscopy: An Australian case study. Geoderma. 2008;146(3):403-411. DOI: 10.1016/j.geoderma.2008.06.011.10.1016/j.geoderma.2008.06.011]Open DOISearch in Google Scholar
[[32] Chen H, Pan T, Chen J, Lu Q. Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods. Chemometrics Intell Labor Systems. 2011;107(1):139-146. DOI: 10.1016/j.chemolab.2011.02.008.10.1016/j.chemolab.2011.02.008]Open DOISearch in Google Scholar
[[33] Tsai F, Philpot W. Derivative analysis of hyperspectral data. Remote Sens Environ. 1998;66(1):41-51. DOI: 10.1016/S0034-4257(98)00032-7.10.1016/S0034-4257(98)00032-7]Open DOISearch in Google Scholar
[[34] Van Genuchten MT. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J. 1980;44(5):892-898. DOI: 10.2136/sssaj1980.03615995004400050002x.10.2136/sssaj1980.03615995004400050002x]Open DOISearch in Google Scholar
[[35] Schaap MG, Leij FJ, Van Genuchten MT. Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J Hydrol. 2001;251(3-4):163-176. DOI: 10.1016/s0022-1694(01)00466-8.10.1016/S0022-1694(01)00466-8]Open DOISearch in Google Scholar
[[36] Hu SZ, Qiao DM, Shi HB. Analysis on root ecological and physiological characteristics of sunflower. J Arid Land Resour Environ. 2006;20(6):192-197. DOI: 10.3969/j.issn.1003-7578.2006.06.037.10.3969/j.issn.1003-7578.2006.06.037]Open DOISearch in Google Scholar
[[37] Zeng W, Xu C, Wu J, Huang J, Zhao Q, Wu M. Impacts of salinity and nitrogen on the photosynthetic rate and growth of sunflowers (Helianthus annuus l.). Pedosphere. 2014;24(5):635-644. DOI: 10.1016/S1002-0160(14)60049-7.10.1016/S1002-0160(14)60049-7]Open DOISearch in Google Scholar
[[38] Holzman ME, Rivas R, Piccolo MC. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. Int J Appl Earth Observ Geoinformation. 2014;28:181-192. DOI: 10.1016/j.jag.2013.12.006.10.1016/j.jag.2013.12.006]Open DOISearch in Google Scholar
[[39] Lobell DB, Asner GP. Moisture effects on soil reflectance. Soil Sci Soc Am J. 2002;66(3):722-727. DOI: 10.2136/sssaj2002.7220.10.2136/sssaj2002.7220]Open DOISearch in Google Scholar
[[40] Morel J, Bégué A, Todoroff P, Martiné J-F, Lebourgeois V, Petit M. Coupling a sugarcane crop model with the remotely sensed time series of fipar to optimise the yield estimation. Eur J Agron. 2014;61:60-68. DOI: 10.1016/j.eja.2014.08.004.10.1016/j.eja.2014.08.004]Open DOISearch in Google Scholar
[[41] Haubrock SN, Chabrillat S, Lemmnitz C, Kaufmann H. Surface soil moisture quantification models from reflectance data under field conditions. Int J Remote Sens. 2008;29(1):3-29. DOI: 10.1080/01431160701294695.10.1080/01431160701294695]Open DOISearch in Google Scholar
[[42] Whiting ML, Li L, Ustin SL. Predicting water content using Gaussian model on soil spectra. Remote Sens Environ. 2004;89(4):535-552. DOI: 10.1016/j.rse.2003.11.009.10.1016/j.rse.2003.11.009]Open DOISearch in Google Scholar
[[43] Diepen CV, Wolf J, Keulen HV, Rappoldt C. Wofost: A simulation model of crop production. Soil Use Manage. 1989;5(1):16-24. DOI: 10.1111/j.1475-2743.1989.tb00755.x.10.1111/j.1475-2743.1989.tb00755.x]Open DOISearch in Google Scholar
[[44] Boogaard H, Wolf J, Supit I, Niemeyer S, Van Ittersum M. A regional implementation of wofost for calculating yield gaps of autumn-sown wheat across the European Union. Field Crop Res. 2013;143:130-142. DOI: 10.1016/j.fcr.2012.11.005.10.1016/j.fcr.2012.11.005]Open DOISearch in Google Scholar
[[45] Kornelsen K C, Coulibaly P. Root-zone soil moisture estimation using data-driven methods. Water Resour Res. 2014;50(4):2946-2962. DOI: 10.1002/2013WR014127.10.1002/2013WR014127]Open DOISearch in Google Scholar
[[46] Das NN, Mohanty BP. Root zone soil moisture assessment using remote sensing and vadose zone modeling. Vadose Zone J. 2006;5(1):296-307. DOI: 10.2136/vzj2005.0033.10.2136/vzj2005.0033]Open DOISearch in Google Scholar
[[47] Zeng W, Xu C, Huang J, Wu J, Tuller M. Predicting near-surface moisture content of saline soils from near-infrared reflectance spectra with a modified Gaussian model. Soil Sci Soc America J. 2016;80(6):1496-1506. DOI: 10.2136/sssaj2016.06.0188.10.2136/sssaj2016.06.0188]Open DOISearch in Google Scholar
[[48] Wigneron JP, Olioso A, Calvet JC, Bertuzzi P. Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling. Water Resour Res. 1999;35(12):3735-3745. DOI: 10.1029/1999WR900258.10.1029/1999WR900258]Open DOISearch in Google Scholar
[[49] Li J, Islam S. Estimation of root zone soil moisture and surface fluxes partitioning using near surface soil moisture measurements. J Hydrol. 2002;259(1):1-14. DOI: 10.1016/S0022-1694(01)00589-3.10.1016/S0022-1694(01)00589-3]Open DOISearch in Google Scholar