Abstract
Soil composed of different percentages of solid, liquid and gasses materials. In reality, the percentages of these components vary tremendously, and the determination the precise amount for each fraction on world wide scale is expensive and time-consuming. The success of the precision agriculture depends strongly upon an efficient and accurate method for in-field soil property determination. For that reason, some helpful techniques were developed in order to predict and mapping soil components including remote sensingĀ (RS) and Geographical Information Systems (GIS). Different methodologies have been used for the estimation of soil parameters, based on different remote sensing sensors and techniques. Many studies in soil science have used both RS bare-soil images and spectroscopic reflectance of soil samples for soil survey, mapping, and quantitative soil-property characterization. This chapter aims to demonstrate the development of some statistical models to predict some components of Iraqi soils using Remote sensing techniques including spectral indices and electromagnet induction.
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References
Abbas AH (2010) Characterization and classification of soil units of North Kut project and prediction of some soil physical properties by using GIS and Remote Sensing. PhD thesis, College of Agriculture, Baghdad University, Iraq
Abbas AH, Muhaimeed AS (2012a) Prediction of infiltration and saturated hydraulic conductivity of the soils in the Iraqi central alluvial plain using remote sensing technique. J Iraqi Sci 53(4):971ā976
Abbas AH, Muhaimeed AS (2012b) Prediction of some soil water content parameters using remote sensing & GIS techniques. Iraqi J Agric Sci 43(4):13ā22
Agbu PA, Fehrenbacher DJ, Jansen IJ (1990) Soil property relationships with SPOT satellite digital data in east central Illinois. Soil Sci Soc Am J 54(3):807ā812
Aldakheel YY (2011) Assessing NDVI spatial pattern as related to irrigation and salinity management in Al-Hassa Oasis, Saudi Arabia. J Indian Soc Remote Sens 39(2):171ā180
Allbed A, Kumar L (2013) Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. ARS 2:373ā385
Al-Shiakly FA (2001) Testing remote sensing techniques in determining soil units of different gypsum content and forecasting some of their features. Ms. thesis, Agriculture College, Baghdad University, Iraq
Barnes EM et al (2003) Remote- and ground-based sensor techniques to map soil properties. Photogramm Eng Remote Sens 69(6):3619ā3630
Ben-Dor E, Banin A (1994) Visible and near-infrared (0.4ā1.1Ā Ī¼m) analysis of arid and semiarid soils. Remote Sens Environ 48(3):261ā274
Ben-Dor E, Banin A (1995) Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci Soc Am J 59(2):364ā372
Browning DM, Duniway MC (2011) Digital soil mapping in the absence of field training data: a case study using terrain attributes and semi automated soil signature derivation to distinguish ecological potential. Appl Environ Soil Sci 42:1904ā1910
Buringh P (1960) Soils and soil conditions in Iraq. Ministry of Agriculture of Iraq, 337 p
Chang CW, Laird DA (2002) Near-infrared reflectance spectroscopic analysis of soil C and N. Soil Sci 167(2):110ā116
Chenu C (2004) Soil phases: the organic solid phase. In: Galcomo C, Scalenghe R (eds) Soils: basic concepts and future challenges. Cambridge University Press, Cambridge
Coleman TL, Agbu PA, Montgomery OL (1993) Spectral differentiation of surface soils and soil properties is it possible from space platform? Soil Sci 55:283ā293
Dalal RC, Henry RJ (1986) Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectroscopy. Soil Sci Soc Am J 50(1):120ā123
Dematte JAM, Gama MAP, Cooper M, Araui JC, Nanni MR, Fiorio PR (2004) Effect of fermentation residue on the spectral reflectance properties of soils. Geoderma 1203ā4:187ā200
FAO (2011) Country pasture/forage resource profiles: Iraq. FAO, Rome, Italy, p 34
FAOāUNDP (1987) Soil quality considerations in the selection of sites for aquaculture. Nigerian institute for oceanography and marine research. Project, RAT/82/009
Farifteh J, Farshad A, George RJ (2006) Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma 130(3ā4):191ā206
Ghabour T, Daels L (1993) Mapping and monitoring soil salinity of ISSN. Egypt J Soil Sci 33(4):355ā370
Huete AR (1988) A soil adjusted vegetation index (SAVI). Remote Sens Environ 25:295ā309
Huete AR, Liu HQ, Batchily K, van Leeuwen W (1997) A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens Environ 59:440ā451
Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens 30:261ā270
Lamb JA, Fernandez FG, Kaiser DE (2014) Understanding nitrogen in soil. University of Minnesota Extension
Lhissou R, El Harti A, Chohmani K (2014) Mapping soil salinity in irrigated land using optical remote sensing data. Aust J Soil Sci 3:82ā88, 913ā917
Matinfar HR et al (2013) Dedection of soil salinity changes and mapping land cover types based upon remotely sensed data. Arab J Geosci 6(3):6ā12
Matthew MW, Adler-Golden SM, Berk A, Richtsmeier SC, Levine RY, Bernstein LS, Acharya PK, Anderson GP, Felde GW, Hoke MP, Ratkowski A, Burke H-H, Kaiser RD, Miller DP (2000) Status of atmospheric correction using a MODTRAN4-based algorithm. In: SPIE Proceedings of algorithms for multispectral, hyperspectral, and ultraspectral imagery VI, 4049, pp 199ā207
McBratney AB, MendonƧa Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117(1ā2):3ā52
McCarty GW, Reeves JB, Reeves VB, Follett RF, Kimble JM (2002) Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Am J 66(2):640ā646
McNeil JD (1980) Electromagnetic terrain conductivity measurement at low induction numbers, Tech Note TN-6, Geonics Limited, Mississauga, Ont, Canada
Mermut AR, Eswaran H (2001) Some major developments in soil science since the mid-1960s. Geoderma 100(3ā4):403ā426
Mougenot B, Pouget M, Epema GF (1993) Remote sensing of salt affected soils. Remote Sens Rev 7(3ā4):241ā259
Muhaimeed AS, Al-Shiakly FF (2002) Forecasting some soil components using satellite images for TM sensor. In: 8th Scientific conference for foundation of technical education, pp 111ā112
Muhaimeed AS, Taha AM (2014) Effect of land use and Irrigation water on amount and type of chemical compounds in some soils of Babylonās Governorate using remote sensing and GIS. Int J Environ Glob Clim Change 2(4):137ā147
Muhaimeed AS, Wu W, AL-Shafi WM, Ziadat F, Katai HH, Saleim KA (2013) Use remote sensing to map soil salinity in Musaib area central Iraq.Ā āIntĀ J Geosci Geomatics 2:34ā41
Muhaimeed AS, Taha AM, Almashhadani HA (2017) Using remote sensing and GIS techniques for predicting soil organic carbon in Southern Iraq. In: Global symposium on soil organic carbon GSOC2017, 21ā23 Mar. FAO, Rome, Italy
Mulder VL, Bruin S, de Schaepman ME, Mayr T (2011) The use of remote sensing in soil and terrain mapping: review. Geoderma 162:1ā19
Mustafa BM, Al-Quraishi AMF, Gholizadeh A, Saberioon M (2019) Proximal soil sensing for soil monitoring. In: Al-Quraishi AMF, Negm AM (eds) Environmental remote sensing and GIS in Iraq. Springer Water
Omran EE (2012) On-the-go digital soil mapping for precision agriculture. Int J Remote Sens Appl 2(3):20ā38
Perkins T, Adler-Golden S, Matthew M, Berk A, Anderson G, Gardner J, Felde G (2005) Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm. In: SchƤfer K, ComerĆ³n AT, Slusser JR, Picard RH, Carleer MR, Sifakis N (eds) Remote sensing of clouds and the atmosphere X. Proceedings of SPIE, vol 5979
Pirieet A, Singh B, Islam K (2005) Ultra-violet, visible, near-infra-red, and mid-infra-red diffuse reflectance spectroscopic techniques to predict several soil properties. Aust J Soil Res 43:713ā721
Rhoades JD (1992) Instrumental field methods of salinity appraisal. In: Topp GC, Reynolds WD, Green RE (eds) Advances in measurement of soil physical properties: bringing theory into practice. SSSA Spec. Publ., 30, pp 231ā248
Rhoades JD, Miyamoto S (1990) Testing soils for salinity and sodicity. In: Westerman RL (ed) Soil testing and plant analysis. SSSA Book Ser, vol 3, 3rd edn, Soil Science Society of America, Madison, Wis
Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great plains with ERTS. In: Proceedings of the third ERTS-1 symposium, NASA, SP-351, vol 1, pp 309ā317
Salehi CW, Baumgardner MF, Biehl LL (1999) Delineation of soil variability using geostatistics and fuzzy clustering analyses of hyperspectral data. Soil Sci Soc Am J 63(1):142ā150
Salehi MH, Eghbal MK, Khademi H (2003) Comparison of soil variability in a detailed and a reconnaissance soil map in central Iran. Geoderma 111(1ā2):45ā56
Sethi M, Bundela DS, Lal K, Kamra SK (2010) Remote sensing and GIS for appraisal of salt affected soils in India. J Environ Qual 39(1):5ā15
Simonson RW (1968) The concept of soil. Adv Agron 20:1ā47
Soil Survey Division Staff (2017) Soil survey manual. USDAāSCS. In: Agriculture hand book, 18.3rd edn. U.S. Government printing Office, Washington, DC
Soil Survey Staff (2014) Key to soil taxonomy, 12th edn. USDAāNCRA. U.S. Government printing Office, Washington DC
Terra FS, DemattĆŖ JAM, Viscarra-Rossel RA (2015) Spectral libraries for quantitative analysis of tropical Brazilian soils: comparing VIS-NIR and MIR reflectance data. Geoderma 142:255ā256, 81ā93
Viscarra-Rossel RAV, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO (2006) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131:59ā75
Wu W (2012) The generalized difference vegetation index (GDVI) for dryland characterization. Remote Sens 6:1211ā1233
Wu W, Al-Shafie WM, Muhaimeed AS, Ziadat F, Nangia V, Payne WB (2014a) Soil salinity mapping by multiscale remote sensing in mesopotamia, Iraq. IEEE J Sel Topic Appl Earth Obs Remote Sens 7(11):4442ā4449
Wu W, Muhaimeed AS, Al-Shafie WM, Ziadat F, Dhehibi B, Nangia V, De Pauw E (2014b) Mapping soil salinity changes using remote sensing in Central Iraq. Geoderma Reg 2ā3:21ā31
Wu W, Zucca C, Muhaimeed AS, Al-Shafie WM, Al-Quraishi AMF, Nangia V, Zhu M, Liu G (2018) Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq. Land Degrad Dev 29(11):4005ā4014
Wu W, Muhaimeed AS, Al-Shafie AM, Al-Quraishi AMF (2019) Using radar and optical data for soil salinity modeling and mapping in Central Iraq. In: Al-Quraishi AMF, Negm AM (eds) Environmental remote sensing and GIS in Iraq. Springer Water
Yousif BF (2004) The use of remote sensing techniques in the classification of Al-Najaf Soil. M.Sc. thesis, Building and Construction Engineering Department, University of Technology
Zhang TT et al (2011) Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecol Ind 11(6):1552ā1562
Zribi M, Baghdadi N, Nolin M (2011) Remote sensing of soil. Appl Environ Soil Sci. Hindawi Publishing Corporation
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Muhaimeed, A.S. (2020). Using Remote Sensing to Predict Soil Properties in Iraq. In: Al-Quraishi, A., Negm, A. (eds) Environmental Remote Sensing and GIS in Iraq. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-030-21344-2_3
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