Estimating Root Zone Moisture from Surface Soil Using Limited Data

Zeng, Wen-Zhi; Lei, Guo-Qing; Zhang, Hong-Ya; Hong, Ming-Hai; Xu, Chi; Wu, Jing-Wei; Huang, Jie-Sheng

doi:10.1515/eces-2017-0033

References

[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 Back to article
[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 DOI Search in Google Scholar Back to article
[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 Back to article
[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 Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article
[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 DOI Search in Google Scholar Back to article

Estimating Root Zone Moisture from Surface Soil Using Limited Data

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