Cavallito, M., FAO: “Soil protection is the first response to the global food crisis,” Re Soil Foundation. https://resoilfoundation.org/en/agricultural-industry/fao-soil-nutrients-food-crisis/, 2022. (Accessed 11 September 2024)
Rojas, R.V., Achouri, M., Maroulis, J., Caon, L., Healthy soils: a prerequisite for sustainable food security. Environ. Earth Sci., 75(180), 2016, 10.1007/s12665-015-5099-7 s12665-015-5099–7.
Qiao, L., Wang, X., Smith, P., Fan, J., Lu, Y., Emmett, B., Li, R., Dorling, S., Chen, H., Liu, S., Benton, T.G., Wang, Y., Ma, Y., Jiang, R., Zhang, F., Piao, S., Mϋller, C., Yang, H., Hao, Y., Li, W., Fan, M., Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Change 12 (2022), 574–580, 10.1038/s41558-022-01376-8.
Food and Agriculture Organization of the United Nations. The Future of Food and Agriculture: Trends and Challenges, 2017, Food and Agriculture Organization of the United Nations, Rome.
Musa, P., Sugeru, H., Wibowo, E.P., Wireless sensor networks for precision agriculture: a review of NPK sensor implementations. Sensors, 24, 2023, 51, 10.3390/s24010051.
Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., Finke, P., Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma 338 (2019), 445–452, 10.1016/j.geoderma.2018.09.006.
Montanarella, L., The global soil partnership. IOP Conf. Ser. Earth Environ. Sci., 25, 2015, 012001, 10.1088/1755-1315/25/1/012001.
Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., Ben Dor, E., Brown, D.J., Clairotte, M., Csorba, A., Dardenne, P., Demattê, J.A.M., Genot, V., Guerrero, C., Knadel, M., Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., Sakai, H., Soriano-Disla, J.M., Shepherd, K.D., Stenberg, B., Towett, E.K., Vargas, R., Wetterlind, J., Chapter four - soil spectroscopy: an alternative to wet chemistry for soil monitoring. Sparks, D.L., (eds.) Advances in Agronomy, 2015, Academic Press, 139–159, 10.1016/bs.agron.2015.02.002.
Clark, R.N., King, T.V.V., Klejwa, M., Swayze, G.A., Vergo, N., High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. Solid Earth 95 (1990), 12653–12680, 10.1029/JB095iB08p12653.
Gholizadeh, A., Borůvka, L., Saberioon, M.M., Kozák, J., Vašát, R., Němeček, K., Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res. 10 (2015), 218–227, 10.17221/113/2015-SWR.
Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 2010, Elsevier, 163–215, 10.1016/S0065-2113(10)07005-7.
Rossel, R.A.V., Bouma, J., Soil sensing: a new paradigm for agriculture. Agric. Syst. 148 (2016), 71–74, 10.1016/j.agsy.2016.07.001.
Barra, I., Haefele, S.M., Sakrabani, R., Kebede, F., Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: recent advances-A review. Trac. Trends Anal. Chem., 135, 2021, 116166, 10.1016/j.trac.2020.116166.
Sharma, V., Chauhan, R., Kumar, R., Spectral characteristics of organic soil matter: a comprehensive review. Microchem. J., 171, 2021, 106836, 10.1016/j.microc.2021.106836.
Coutinho, M.A.N., Alari, F. de O., Ferreira, M.M.C., do Amaral, L.R., Influence of soil sample preparation on the quantification of NPK content via spectroscopy. Geoderma 338 (2019), 401–409, 10.1016/j.geoderma.2018.12.021.
Gandariasbeitia, M., Besga, G., Albizu, I., Larregla, S., Mendarte, S., Prediction of chemical and biological variables of soil in grazing areas with visible- and near-infrared spectroscopy. Geoderma 305 (2017), 228–235, 10.1016/j.geoderma.2017.05.045.
Soriano-Disla, J.M., Janik, L.J., Rossel, R.A.V., Macdonald, L.M., McLaughlin, M.J., The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl. Spectrosc. Rev. 49 (2014), 139–186, 10.1080/05704928.2013.811081.
Jia, X., O'Connor, D., Shi, Z., Hou, D., VIRS based detection in combination with machine learning for mapping soil pollution. Environ. Pollut., 268, 2021, 115845, 10.1016/j.envpol.2020.115845.
Margenot, A.J., Calderón, F.J., Parikh, S.J., Limitations and potential of spectral subtractions in fourier‐transform infrared spectroscopy of soil samples. Soil Science Soc of Amer J 80 (2016), 10–26, 10.2136/sssaj2015.06.0228.
Roberts, J.J., Cozzolino, D., Wet or dry? The effect of sample characteristics on the determination of soil properties by near infrared spectroscopy. TrAC, Trends Anal. Chem. 83 (2016), 25–30, 10.1016/j.trac.2016.08.002.
Nduwamungu, C., Ziadi, N., Tremblay, G.F., Parent, L.-É., Near-infrared reflectance spectroscopy prediction of soil properties: effects of sample cups and preparation. Soil Sci. Soc. Am. J. 73 (2009), 1896–1903, 10.2136/sssaj2008.0213.
Hong, Y., Munnaf, M.A., Guerrero, A., Chen, S., Liu, Y., Shi, Z., Mouazen, A.M., Fusion of visible-to-near-infrared and mid-infrared spectroscopy to estimate soil organic carbon. Soil Tillage Res., 217, 2022, 105284, 10.1016/j.still.2021.105284.
Mouazen, A.M., De Baerdemaeker, J., Ramon, H., Towards development of on-line soil moisture content sensor using a fibre-type NIR spectrophotometer. Soil Tillage Res. 80 (2005), 171–183, 10.1016/j.still.2004.03.022.
Ben Dor, E., Ong, C., Lau, I.C., Reflectance measurements of soils in the laboratory: standards and protocols. Geoderma 245–246 (2015), 112–124, 10.1016/j.geoderma.2015.01.002.
Dhawale, N.M., Adamchuk, V.I., Prasher, S.O., Viscarra Rossel, R.A., Ismail, A.A., Kaur, J., Proximal soil sensing of soil texture and organic matter with a prototype portable mid-infrared spectrometer. Eur. J. Soil Sci. 66 (2015), 661–669, 10.1111/ejss.12265.
Minasny, B., McBratney, A.B., Bellon-Maurel, V., Roger, J.-M., Gobrecht, A., Ferrand, L., Joalland, S., Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma 167–168 (2011), 118–124, 10.1016/j.geoderma.2011.09.008.
Ji, W., Viscarra Rossel, R.A., Shi, Z., Accounting for the effects of water and the environment on proximally sensed vis–NIR soil spectra and their calibrations. Eur. J. Soil Sci. 66 (2015), 555–565, 10.1111/ejss.12239.
Guan, Q., Zhao, R., Wang, F., Pan, N., Yang, L., Song, N., Xu, C., Lin, J., Prediction of heavy metals in soils of an arid area based on multi-spectral data. J. Environ. Manag. 243 (2019), 137–143, 10.1016/j.jenvman.2019.04.109.
Ben-Dor, E., Chabrillat, S., Demattê, J.A.M., Taylor, G.R., Hill, J., Whiting, M.L., Sommer, S., Using Imaging Spectroscopy to study soil properties. Rem. Sens. Environ. 113 (2009), S38–S55, 10.1016/j.rse.2008.09.019.
Hbirkou, C., Pätzold, S., Mahlein, A.-K., Welp, G., Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale. Geoderma 175–176 (2012), 21–28, 10.1016/j.geoderma.2012.01.017.
Vivone, G., Multispectral and hyperspectral image fusion in remote sensing: a survey. Inf. Fusion 89 (2023), 405–417, 10.1016/j.inffus.2022.08.032.
Campbell, J.L., Maxwell, J.A., Andrushenko, S.M., Taylor, S.M., Jones, B.N., Brown-Bury, W., A GUPIX-based approach to interpreting the PIXE-plus-XRF spectra from the Mars Exploration Rovers: I. Homogeneous standards. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 269 (2011), 57–68, 10.1016/j.nimb.2010.10.004.
Shi, P., Castaldi, F., van Wesemael, B., Van Oost, K., Large-Scale, High-resolution map∗∗ of soil aggregate stability in croplands using APEX hyperspectral imagery. Rem. Sens., 12, 2020, 666, 10.3390/rs12040666.
Anne, N.J.P., Abd-Elrahman, A.H., Lewis, D.B., Hewitt, N.A., Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands. Int. J. Appl. Earth Obs. Geoinf. 33 (2014), 47–56, 10.1016/j.jag.2014.04.007.
Seema, Ghosh, A.K., Das, B.S., Reddy, N., Application of VIS-NIR spectroscopy for estimation of soil organic carbon using different spectral preprocessing techniques and multivariate methods in the middle Indo-Gangetic plains of India. Geoderma Regional, 23, 2020, e00349, 10.1016/j.geodrs.2020.e00349.
Yun, Y.-H., Li, H.-D., Deng, B.-C., Cao, D.-S., An overview of variable selection methods in multivariate analysis of near-infrared spectra. TrAC, Trends Anal. Chem. 113 (2019), 102–115, 10.1016/j.trac.2019.01.018.
Delwiche, S.R., Reeves, J.B., A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression. Appl. Spectrosc. 64 (2010), 73–82, 10.1366/000370210790572007.
Yang, H., Kuang, B., Mouazen, A., Selection of spectral preprocessing methods for soil texture classification. Adv. Mater. Res. 181–182 (2011), 416–421 https://doi.org/10.4028/www.scientific.net/AMR.181-182.416.
Newman, D.R., Cockburn, J.M.H., Drǎguţ, L., Lindsay, J.B., Local scale optimization of geomorphometric land surface parameters using scale-standardized Gaussian scale-space. Comput. Geosci., 165, 2022, 105144, 10.1016/j.cageo.2022.105144.
Tranter, G., Minasny, B., McBratney, A.B., Rossel, R.A.V., Murphy, B.W., Comparing spectral soil inference systems and mid-infrared spectroscopic predictions of soil moisture retention. Soil Sci. Soc. Am. J. 72 (2008), 1394–1400, 10.2136/sssaj2007.0188.
Berg, F., Rinnan, Å., Chapter 5. Calibration transfer methods. Infrared Spectroscopy for Food Quality Analysis and Control, 2009, 105–118, 10.1016/B978-0-12-374136-3.00005-5.
Huazhou, C., University, J., Guangzhou, Guangdong, China, Combination optimization of multiple scatter correction and Savitzky-Golay smoothing modes applied to the near infrared spectroscopy analysis of soil organic matter. Comput. Appl. Chem. 28 (2011), 518–522, 10.1007/s12274-011-0112-2.
Guolin, M., Jianli, D., Zipeng, Z., Soil organic matter content estimation based on soil covariate and VIS-NIR spectroscopy, laser optoelectron. Prog., 57, 2020, 192801, 10.3788/LOP57.192801.
Sun, Z., Zhang, Y., Li, J., Zhou, W., Spectroscopic determination of soil organic carbon and total nitrogen content in pasture soils. Commun. Soil Sci. Plant Anal. 45 (2014), 1037–1048, 10.1080/00103624.2014.883628.
Yang, H., Qian, Y., Yang, F., Li, J., Ju, W., Using wavelet Transform of hyperspectral reflectance data for extracting spectral features of soil organic carbon and nitrogen. Soil Sci. 177 (2012), 674–681, 10.1097/SS.0b013e3182792bcc.
Patel, A.K., Ghosh, J.K., Soil fertility status assessment using hyperspectral remote sensing. Neale, C.M., Maltese, A., (eds.) Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, SPIE, 2019, 14, 10.1117/12.2533115 Strasbourg, France.
Tong Jiajun, T.W., Improvement of NIR model by fractional order Savitzky-Golay derivation (FOSGD) coupled with wavelength selection. Chemometr. Intell. Lab. Syst., 143, 2015 https://www.zhangqiaokeyan.com/journal-foreign-detail/070401270731.html (accessed May 6, 2024).
Smith, G., Step away from stepwise. J. Big Data, 5, 2018, 32, 10.1186/s40537-018-0143-6.
Tang, G., Huang, Y., Tian, K., Song, X., Yan, H., Hu, J., Xiong, Y., Min, S., A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm. Analyst 139 (2014), 4894–4902, 10.1039/C4AN00837E.
Wu, D., Chen, X., Zhu, X., Guan, X., Wu, G., Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver. Anal. Methods 3 (2011), 1790–1796, 10.1039/C1AY05075C.
Reza Keyvanpour, M., Shirzad, M.B., Chapter 14 - machine learning techniques for agricultural image recognition. Khan, M.A., Khan, R., Ansari, M.A., (eds.) Application of Machine Learning in Agriculture, 2022, Academic Press, 283–305, 10.1016/B978-0-323-90550-3.00011-4.
Han, K., Wang, Y., A review of artificial neural network techniques for environmental issues prediction. J. Therm. Anal. Calorim. 145 (2021), 2191–2207, 10.1007/s10973-021-10748-9.
Harrell, F.E., Regression Modeling Strategies: with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2015, Springer International Publishing, Cham, 10.1007/978-3-319-19425-7.
He, Y., Huang, M., García, A., Hernández, A., Song, H., Prediction of soil macronutrients content using near-infrared spectroscopy. Comput. Electron. Agric. 58 (2007), 144–153, 10.1016/j.compag.2007.03.011.
Zhang, Y., Li, M., Zheng, L., Zhao, Y., Pei, X., Soil nitrogen content forecasting based on real-time NIR spectroscopy. Comput. Electron. Agric. 124 (2016), 29–36, 10.1016/j.compag.2016.03.016.
Carra, J.B., Fabris, M., Dieckow, J., Brito, O.R., Vendrame, P.R.S., Macedo Dos Santos Tonial, L., Near-infrared spectroscopy coupled with chemometrics tools: a rapid and non-destructive alternative on soil evaluation. Commun. Soil Sci. Plant Anal. 50 (2019), 421–434, 10.1080/00103624.2019.1566465.
Kodaira, M., Shibusawa, S., Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping. Geoderma 199 (2013), 64–79, 10.1016/j.geoderma.2012.09.007.
Kusumo, B.H., Hedley, C.B., Hedley, M.J., Hueni, A., Tuohy, M.P., Arnold, G.C., The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils. Aust. J. Soil Res. 46 (2008), 623–635, 10.1071/SR08118.
Wetterlind, J., Stenberg, B., Söderström, M., Increased sample point density in farm soil mapping by local calibration of visible and near infrared prediction models. Geoderma 156 (2010), 152–160, 10.1016/j.geoderma.2010.02.012.
Madhavan, D.B., Kitching, M., Mendham, D.S., Weston, C.J., Baker, T.G., Mid-infrared spectroscopy for rapid assessment of soil properties after land use change from pastures to Eucalyptus globulus plantations. J. Environ. Manag. 175 (2016), 67–75, 10.1016/j.jenvman.2016.03.032.
McCarty, G.W., Reeves, J.B., Comparison of near infrared and mid infrared diffuse reflectance spectroscopy for field-scale measurement of soil fertility parameters. Soil Sci. 171 (2006), 94–102, 10.1097/01.ss.0000187377.84391.54.
dos Santos, U.J., de Melo Dematte, J.A., Menezes, R.S.C., Dotto, A.C., Guimarães, C.C.B., Alves, B.J.R., Primo, D.C., Sampaio, E.V., Predicting carbon and nitrogen by visible near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy in soils of Northeast Brazil. Geoderma Regional, 23, 2020, e00333, 10.1016/j.geodrs.2020.e00333.
Alomar, S., Mireei, S.A., Hemmat, A., Masoumi, A.A., Khademi, H., Comparison of Vis/SWNIR and NIR spectrometers combined with different multivariate techniques for estimating soil fertility parameters of calcareous topsoil in an arid climate. Biosyst. Eng. 201 (2021), 50–66, 10.1016/j.biosystemseng.2020.11.007.
Wu, Q., Yang, Y., Xu, Z., Jin, Y., Guo, Y., Lao, C., Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu, 2014 https://www.semanticscholar.org/paper/%5BApplying-local-neural-network-and-visible-to-and-Wu-Yang/e241daacf28f99fea7ae8e32e3bf0567e76a9d70. (Accessed 1 July 2024)
Wijewardane, N.K., Ge, Y., Wills, S., Libohova, Z., Predicting physical and chemical properties of US soils with a mid-infrared reflectance spectral library. Soil Sci. Soc. Am. J. 82 (2018), 722–731, 10.2136/sssaj2017.10.0361.
Wenjun, J., Zhou, S., Jingyi, H., Shuo, L., In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS One, 9, 2014, e105708, 10.1371/journal.pone.0105708.
Reda, R., Saffaj, T., Ilham, B., Saidi, O., Issam, K., Brahim, L., El Hadrami, E.M., A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy. Chemometr. Intell. Lab. Syst., 195, 2019, 103873, 10.1016/j.chemolab.2019.103873.
Kuang, B., Mouazen, A.M., Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. Eur. J. Soil Sci. 62 (2011), 629–636, 10.1111/j.1365-2389.2011.01358.x.
Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Shah, N., Smith, P., In situ measurement of soil chemical composition by near-infrared spectroscopy: a tool toward sustainable vineyard management. Commun. Soil Sci. Plant Anal. 44 (2013), 1610–1619, 10.1080/00103624.2013.768263.
Barthès, B.G., Kouakoua, E., Clairotte, M., Lallemand, J., Chapuis-Lardy, L., Rabenarivo, M., Roussel, S., Performance comparison between a miniaturized and a conventional near infrared reflectance (NIR) spectrometer for characterizing soil carbon and nitrogen. Geoderma 338 (2019), 422–429, 10.1016/j.geoderma.2018.12.031.
Schirrmann, M., Gebbers, R., Kramer, E., Performance of automated near-infrared reflectance spectrometry for continuous in situ mapping of soil fertility at field scale. Vadose Zone J., 12, 2013, 10.2136/vzj2012.0199 vzj2012.0199.
Liu, Y., Jiang, Q., Shi, T., Fei, T., Wang, J., Liu, G., Chen, Y., Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy. Acta Agric. Scand. Sect. B Soil Plant Sci 64 (2014), 267–281, 10.1080/09064710.2014.906644.
Djuuna, I., Abbott, L., Russell, C., Determination and prediction of some soil properties using partial least square (PLS) calibration and mid-infra red (MIR) spectroscopy analysis. J. Tropical Soil. 16 (2013), 93–98, 10.5400/jts.2011.v16i2.93-98.
Sepahvand, H., Mirzaeitalarposhti, R., Beiranvand, K., Feizian, M., Müeller, T., Prediction of soil carbon levels in calcareous soils of Iran by mid-infrared reflectance spectroscopy. Environ. Pollut. Bioavailabil. 31 (2019), 9–17, 10.1080/09542299.2018.1549961.
Xie, H.T., Yang, X.M., Drury, C.F., Yang, J.Y., Zhang, X.D., Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada. Can. J. Soil Sci. 91 (2011), 53–63, 10.4141/cjss10029.
Soriano-Disla, J.M., Janik, L.J., Allen, D.J., McLaughlin, M.J., Evaluation of the performance of portable visible-infrared instruments for the prediction of soil properties. Biosyst. Eng. 161 (2017), 24–36, 10.1016/j.biosystemseng.2017.06.017.
Johnson, J.-M., Vandamme, E., Senthilkumar, K., Sila, A., Shepherd, K.D., Saito, K., Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa. Geoderma, 354, 2019, 113840, 10.1016/j.geoderma.2019.06.043.
Gras, J.-P., Barthès, B.G., Mahaut, B., Trupin, S., Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils. Geoderma, 2014, 214–215, 10.1016/j.geoderma.2013.09.021 126–134.
Towett, E.K., Shepherd, K.D., Sila, A., Aynekulu, E., Cadisch, G., Mid-infrared and total X-ray fluorescence spectroscopy complementarity for assessment of soil properties. Soil Sci. Soc. Am. J. 79 (2015), 1375–1385, 10.2136/sssaj2014.11.0458.
Yu, X., Liu, Q., Wang, Y., Liu, X., Liu, X., Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. Catena 137 (2016), 340–349, 10.1016/j.catena.2015.09.024.
Martínez-España, R., Bueno-Crespo, A., Soto, J., Janik, L.J., Soriano-Disla, J.M., Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument. Biosyst. Eng. 177 (2019), 101–108, 10.1016/j.biosystemseng.2018.09.013.
Xu, S., Zhao, Y., Wang, M., Shi, X., Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma 310 (2018), 29–43, 10.1016/j.geoderma.2017.09.013.
Sanderman, J., Savage, K., Dangal, S.R.S., Mid-infrared spectroscopy for prediction of soil health indicators in the United States. Soil Sci. Soc. Am. J. 84 (2020), 251–261, 10.1002/saj2.20009.
Wu, Q., Yang, Y., Xu, Z., Jin, Y., Guo, Y., Lao, C., Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu 34 (2014), 2102–2105, 10.3964/j.issn.1000-0593(2014)08-2102-04.
Ji, W., Adamchuk, V.I., Biswas, A., Dhawale, N.M., Sudarsan, B., Zhang, Y., Viscarra Rossel, R.A., Shi, Z., Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields. Biosyst. Eng. 152 (2016), 14–27, 10.1016/j.biosystemseng.2016.06.005.
Mohamed, E.S., Baroudy, A.A.E., El-beshbeshy, T., Emam, M., Belal, A.A., Elfadaly, A., Aldosari, A.A., Ali, A.M., Lasaponara, R., Vis-NIR spectroscopy and satellite landsat-8 OLI data to map soil nutrients in arid conditions: a case study of the northwest coast of Egypt. Rem. Sens., 12, 2020, 3716, 10.3390/rs12223716.
Shao, Y., He, Y., Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy. Soil Res. 49 (2011), 166–172, 10.1071/SR10098.
Paz-Kagan, T., Shachak, M., Zaady, E., Karnieli, A., A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use. Geoderma 230–231 (2014), 171–184, 10.1016/j.geoderma.2014.04.003.
Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O., Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131 (2006), 59–75, 10.1016/j.geoderma.2005.03.007.
Yin, J., Shi, Z., Li, B., Sun, F., Miao, T., Shi, Z., Chen, S., Yang, M., Ji, W., Prediction of soil properties in a field in typical black soil areas using in situ MIR spectra and its comparison with vis-NIR spectra. Rem. Sens., 15, 2023, 2053, 10.3390/rs15082053.
Xu, D., Ma, W., Chen, S., Jiang, Q., He, K., Shi, Z., Assessment of important soil properties related to Chinese Soil Taxonomy based on vis–NIR reflectance spectroscopy. Comput. Electron. Agric. 144 (2018), 1–8, 10.1016/j.compag.2017.11.029.
Hu, X.-Y., Application of visible/near-infrared spectra in modeling of soil total phosphorus. Pedosphere 23 (2013), 417–421, 10.1016/S1002-0160(13)60034-X.
Recena, R., Fernández-Cabanás, V.M., Delgado, A., Soil fertility assessment by Vis-NIR spectroscopy: predicting soil functioning rather than availability indices. Geoderma 337 (2019), 368–374, 10.1016/j.geoderma.2018.09.049.
Xu, S., Zhao, Y., Wang, Y., Optimizing machine learning models for predicting soil pH and total P in intact soil profiles with visible and near-infrared reflectance (VNIR) spectroscopy. Comput. Electron. Agric., 218, 2024, 108643, 10.1016/j.compag.2024.108643.
Marmette, M.-C., Adamchuk, V., Nault, J., Tabatabai, S., Cocciardi, R., Comparison of the Performance of Two Vis-NIR Spectrometers in the Prediction of Various Soil Properties. 2018.
Mouazen, A.M., Maleki, M.R., De Baerdemaeker, J., Ramon, H., On-line measurement of some selected soil properties using a VIS–NIR sensor. Soil Tillage Res. 93 (2007), 13–27, 10.1016/j.still.2006.03.009.
Vendrame, P.R.S., Marchão, R.L., Brunet, D., Becquer, T., The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols. Eur. J. Soil Sci. 63 (2012), 743–753, 10.1111/j.1365-2389.2012.01483.x.
Ma, F., Du, C.W., Zhou, J.M., Shen, Y.Z., Investigation of soil properties using different techniques of mid-infrared spectroscopy. Eur. J. Soil Sci. 70 (2019), 96–106, 10.1111/ejss.12741.
Araújo, S.R., Söderström, M., Eriksson, J., Isendahl, C., Stenborg, P., Demattê, JoséA.M., Determining soil properties in Amazonian Dark Earths by reflectance spectroscopy. Geoderma 237–238 (2015), 308–317, 10.1016/j.geoderma.2014.09.014.
Christy, C.D., Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Comput. Electron. Agric. 61 (2008), 10–19, 10.1016/j.compag.2007.02.010.
Daniel, K.W., Tripathi, N.K., Honda, K., Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand). Soil Res. 41 (2003), 47–59, 10.1071/sr02027.
Qi, H., Paz-Kagan, T., Karnieli, A., Jin, X., Li, S., Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data. Soil Tillage Res. 175 (2018), 267–275, 10.1016/j.still.2017.09.006.
Sarathjith, M.C., Das, B.S., Wani, S.P., Sahrawat, K.L., Gupta, A., Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy. Curr. Sci., 110, 2015, 1031, 10.18520/cs/v110/i6/1031-1037.
Terra, F.S., Demattê, J.A.M., Viscarra Rossel, R.A., Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing vis–NIR and mid-IR reflectance data. Geoderma 255–256 (2015), 81–93, 10.1016/j.geoderma.2015.04.017.
Gholizade, A., Soom, M.A.M., Saberioon, M.M., Borůvka, L., Visible and near infrared reflectance spectroscopy to determine chemical properties of paddy soils. J. Food Agric. Environ. 11 (2013), 859–866.
Metzger, K., Liebisch, F., Herrera, J.M., Guillaume, T., Walder, F., Bragazza, L., The use of visible and near-infrared spectroscopy for in-situ characterization of agricultural soil fertility: a proposition of best practice by comparing scanning positions and spectrometers. Soil Use Manag., 40, 2024, e12952, 10.1111/sum.12952.
Li, X.-Y., Fan, P.-P., Liu, Y., Hou, G.-L., Wang, Q., Lv, M.-R., Prediction results of different modeling methods in soil nutrient concentrations based on spectral technology. J. Appl. Spectrosc. 86 (2019), 765–770, 10.1007/s10812-019-00891-5.
Cozzolino, D., Morón, A., The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics. J. Agric. Sci. 140 (2003), 65–71, 10.1017/S0021859602002836.
Chang, C.-W., Laird, D.A., Mausbach, M.J. Jr., Hurburgh, C.R., Near-infrared reflectance spectroscopy - principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 65 (2001), 480–490, 10.2136/sssaj2001.652480x.
Mouazen, A.M., Kuang, B., De Baerdemaeker, J., Ramon, H., Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158 (2010), 23–31, 10.1016/j.geoderma.2010.03.001.
Xuemei, L., Jianshe, L., Using short wave visible–near infrared reflectance spectroscopy to predict soil properties and content. Spectrosc. Lett. 47 (2014), 729–739, 10.1080/00387010.2013.840315.
Albinet, F., Peng, Y., Eguchi, T., Smolders, E., Dercon, G., Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning: from prediction to explainability. Artificial Intellig. Agricult. 6 (2022), 230–241, 10.1016/j.aiia.2022.10.001.
Chabrillat, S., Ben-Dor, E., Cierniewski, J., Gomez, C., Schmid, T., Van Wesemael, B., Imaging spectroscopy for soil mapping and monitoring. Surv. Geophys. 40 (2019), 361–399, 10.1007/s10712-019-09524-0.
Salman, A.K., Aldulaimy, S.E., Mohammed, H.J., Abed, Y.M., Performance of soil moisture sensors in gypsiferous and salt-affected soils. Biosyst. Eng. 209 (2021), 200–209, 10.1016/j.biosystemseng.2021.07.006.
Angelopoulou, T., Balafoutis, A., Zalidis, G., Bochtis, D., From laboratory to proximal sensing spectroscopy for soil organic carbon estimation—a review. Sustainability, 12, 2020, 443, 10.3390/su12020443.
Viscarra Rossel, R.A., Cattle, S.R., Ortega, A., Fouad, Y., In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma 150 (2009), 253–266, 10.1016/j.geoderma.2009.01.025.
Roger, J.-M., Chauchard, F., Bellon-Maurel, V., EPO–PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemometr. Intell. Lab. Syst. 66 (2003), 191–204, 10.1016/S0169-7439(03)00051-0.
Woody, N.A., Feudale, R.N., Myles, A.J., Brown, S.D., Transfer of multivariate calibrations between four near-infrared spectrometers using orthogonal signal correction. Anal. Chem. 76 (2004), 2595–2600, 10.1021/ac035382g.
Mendes, W. de S., Demattê, J.A.M., Bonfatti, B.R., Resende, M.E.B., Campos, L.R., da Costa, A.C.S., A novel framework to estimate soil mineralogy using soil spectroscopy. Appl. Geochem., 127, 2021, 104909, 10.1016/j.apgeochem.2021.104909.
Madejová, J., FTIR techniques in clay mineral studies. Vib. Spectrosc. 31 (2003), 1–10, 10.1016/S0924-2031(02)00065-6.
Fan, P., Li, X., Qiu, H., Hou, G.-L., Spectral analysis of total phosphorus in soils based on its diagnostic reflectance spectra. Result. Chem., 3, 2021, 100145, 10.1016/j.rechem.2021.100145.
Russell, J.D., Van der Marel, H.W., Beutelspacher, H., Atlas of Infrared Spectroscopy of Clay Minerals and Their Admixtures, vol. 12, 1977, Elsevier, Amsterdam, 279–280, 10.1180/claymin.1977.012.3.11 1976. viii + 396 pp. £34.35, Clay Miner.
Wilson, M.J., (eds.) Clay Mineralogy: Spectroscopic and Chemical Determinative Methods, 1994, Springer Netherlands, Dordrecht, 10.1007/978-94-011-0727-3.
Stoner, E.R., Baumgardner, M., Physiochemical, site, and bidirectional reflectance factor characteristics of uniformly moist soils. [Brazil, Spain and the United States of America] https://www.semanticscholar.org/paper/Physiochemical%2C-site%2C-and-bidirectional-reflectance-Stoner-Baumgardner/44888e7ad24df66f9515b4cf9e3b6319592d55d5, 1980. (Accessed 30 August 2024)
Hunt, G., Visible and near-infrared spectra of minerals and rocks : I silicate minerals. https://www.semanticscholar.org/paper/Visible-and-near-infrared-spectra-of-minerals-and-%3A-Hunt/d6d90ca316b10199236f28926df4f29b8e01aa74, 1970. (Accessed 30 August 2024)
Coleman, T., Montgomery, O., Soil moisture, organic matter, and iron content effect on the spectral characteristics of selected vertisols and alfisols in Alabama. Photogramm. Eng. Rem. Sens., 1987 https://www.semanticscholar.org/paper/Soil-moisture%2C-organic-matter%2C-and-iron-content-on-Coleman-Montgomery/a8ba93dcf5af32583c7b8e9d509cfad0cb451137. (Accessed 30 August 2024)
Mirzaeitalarposhti, R., Demyan, M.S., Rasche, F., Cadisch, G., Müller, T., Overcoming carbonate interference on labile soil organic matter peaks for midDRIFTS analysis. Soil Biol. Biochem. 99 (2016), 150–157, 10.1016/j.soilbio.2016.05.010.
Ben-Dor, E., Inbar, Y., Chen, Y., The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Rem. Sens. Environ. 61 (1997), 1–15, 10.1016/S0034-4257(96)00120-4.
Liu, H., Yu, W., Zhang, X., Ma, Q., Zhou, H., Jiang, Z., Study on the main influencing factors of black soil spectral characteristics. Spectrosc. Spectr. Anal. 29 (2009), 3019–3022, 10.3964/j.issn.1000-0593(2009)11-3019-04.
Mihoub, A., Bouhoun, M.D., Naeem, A., Saker, M.L., Low-molecular weight organic acids improve plant availability of phosphorus in different textured calcareous soils. Arch. Agron Soil Sci. 63 (2017), 1023–1034, 10.1080/03650340.2016.1249477.
Jindo, K., Audette, Y., Olivares, F.L., Canellas, L.P., Smith, D.S., Voroney, R.P., Biotic and abiotic effects of soil organic matter on the phytoavailable phosphorus in soils: a review. Chem. Biol. Technol. Agric., 10, 2023, 29, 10.1186/s40538-023-00401-y.
Taghdis, S., Farpoor, M.H., Mahmoodabadi, M., Pedological assessments along an arid and semi-arid transect using soil spectral behavior analysis. Catena, 214, 2022, 106288, 10.1016/j.catena.2022.106288.
Ng, W., Minasny, B., Mendes, W. de S., Demattê, J.A.M., The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. SOIL 6 (2020), 565–578, 10.5194/soil-6-565-2020.
Liu, Y., Liu, Y., Chen, Y., Zhang, Y., Shi, T., Wang, J., Hong, Y., Fei, T., Zhang, Y., The influence of spectral pretreatment on the selection of representative calibration samples for soil organic matter estimation using vis-NIR reflectance spectroscopy. Rem. Sens., 11, 2019, 450, 10.3390/rs11040450.
Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., Chapter five - visible and near infrared spectroscopy in soil science. Sparks, D.L., (eds.) Advances in Agronomy, 2010, Academic Press, 163–215, 10.1016/S0065-2113(10)07005-7.
Ben-Dor, E., Chabrillat, S., Demattê, J.A.M., Taylor, G.R., Hill, J., Whiting, M.L., Sommer, S., Using Imaging Spectroscopy to study soil properties. Rem. Sens. Environ. 113 (2009), S38–S55, 10.1016/j.rse.2008.09.019.
Kamath, U., Liu, J., Introduction to interpretability and explainability. Kamath, U., Liu, J., (eds.) Explainable Artificial Intelligence: an Introduction to Interpretable Machine Learning, 2021, Springer International Publishing, Cham, 1–26, 10.1007/978-3-030-83356-5_1.
Fu, W., Hopkins, W.S., Applying machine learning to vibrational spectroscopy. J. Phys. Chem. A 122 (2018), 167–171, 10.1021/acs.jpca.7b10303.
Friedman, J., Hastie, T., Tibshirani, R., Regularization paths for generalized linear models via coordinate descent. J. Stat. Software 33 (2010), 1–22.
Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning. 2009, Springer, New York, NY, 10.1007/978-0-387-84858-7.
Li, L.-N., Liu, X.-F., Yang, F., Xu, W.-M., Wang, J.-Y., Shu, R., A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis. Spectrochim. Acta B Atom Spectrosc., 180, 2021, 106183, 10.1016/j.sab.2021.106183.
Xiong, Y., McCarthy, C., Humpal, J., Percy, C., Near-infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi-season trials. Agron. J. 116 (2024), 2370–2390, 10.1002/agj2.21648.
Gogé, F., Joffre, R., Jolivet, C., Ross, I., Ranjard, L., Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database. Chemometr. Intell. Lab. Syst. 110 (2012), 168–176, 10.1016/j.chemolab.2011.11.003.
