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Desarrollo de un modelo de predicción del contenido de agua para tres tipos de suelos en Colombia
Development of a water content prediction model for three soil types in Colombia
DOI:
https://doi.org/10.15446/acag.v74n1.118362Palabras clave:
agricultura de precisión, análisis de componentes principales, espectroscopía de reflectancia difusa, humedad del suelo, regresión por mínimos cuadrados parciales (es)Diffuse reflectance spectroscopy, partial least squares regression, precision farming, principal component analysis, Soil moisture regime (en)
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Múltiples propiedades del suelo tienen correlación con su contenido de agua (CA), por lo que determinar la humedad del suelo es una medida que permite implementar actividades para una adecuada gestión de los cultivos. Por esta razón, son necesarias técnicas rápidas y de bajo costo que permitan un monitoreo eficiente del suelo como herramienta para tomar decisiones pertinentes sobre su uso y manejo. El objetivo de este estudio fue generar un modelo de predicción espectral para tres suelos de diferente orden (un Oxisol del departamento del Meta (Colombia), un Inceptisol del departamento de Cundinamarca y un Alfisol del departamento del Tolima), a niveles de CA de 0 %, 15 % y 30 %. Todas las muestras fueron tomadas, ajustadas al CA deseado y luego se obtuvieron las curvas espectrales mediante espectroscopia de reflectancia difusa. Se obtuvo un modelo para cada orden de suelo; en todos los casos, los modelos mostraron buena capacidad predictiva (R2 > 0.85 y RMSE < 0.04). El análisis de componentes principales mostró diferencias notables entre los CA de cada uno de los suelos. En conclusión, la técnica de espectroscopía NIR puede ser utilizada para estimar el contenido de agua en los suelos.
Multiple soil properties correlate with water content (WC); therefore, determining soil moisture enables the implementation of activities for proper crop management. Consequently, fast, low-cost techniques are needed to allow efficient soil monitoring as a tool for making relevant decisions about soil use and management. The objective of this study was to develop a spectral prediction model for three soils from different orders (an Oxisol from the department of Meta (Colombia), an Inceptisol from the department of Cundinamarca, and an Alfisol from the department of Tolima), at WC levels of 0 %, 15 %, and 30 %. All samples were collected, adjusted to the desired WC, and then their spectral curves were obtained using diffuse reflectance spectroscopy. One model was developed for each soil order; in all cases, the models showed good predictive performance (R2 > 0.85 and RMSE < 0.04). Principal components analysis indicated marked differences among WC levels within each soil. In conclusion, the NIR spectroscopy technique can be used to estimate soil water content.
Referencias
Baumann, P.; Lee, J.; Behrens, T.; Biswas, A.; Six, J.; McLachlan, G. and Viscarra Rossel, R. A. (2022). Modelling soil water retention and water-holding capacity with visible–near-infrared spectra and machine learning. European Journal of Soil Science, 73(2), e13220. https://doi.org/10.1111/ejss.13220
Byun, Y.; Seo, C.; Yun, T.; Joo, Y. and Jo, H. Y. (2023). Prediction of Na- and Ca-montmorillonite contents and swelling properties of clay mixtures using Vis-NIR spectroscopy. Geoderma, 430, 116294, https://doi.org/10.1016/j.geoderma.2022.116294
Camacho-Tamayo, J. H.; Rubiano S., Y. and Hurtado S, M. del P. (2014). Near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an Oxisol. Agronomía Colombiana, 32(1), 86-94. https://doi.org/10.15446/agron.colomb.v32n1.38967
Chang, C. W.; Laird, D. A.; Mausbach, M. J. and Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal, 65(2), 480-490. https://doi.org/10.2136/sssaj2001.652480x
Cortés-Delgadillo, D. L.; Camacho-Tamayo, J. H. and Giraldo, R. (2024). Analysis and spatial prediction of water retention curves in two types of soil. Revista Brasileira de Engenharia Agrícola e Ambiental, 28(3), e277718. https://doi.org/10.1590/1807-1929/agriambi.v28n3e277718
Fernández-Martínez, F.; Camacho-Tamayo, J. H. and Rubiano-Sanabria, Y. (2023). Near-infrared spectroscopy: assessment of soil organic carbon stock in a Colombian Oxisol. Ingeniería e Investigación, 43(3), e99102. https://doi.org/10.15446/ing.investig.99102
Forero-Cabrera, N. M.; Camacho-Tamayo, J. H.; Ramírez-López, L. and Rubiano Sanabria, Y. (2017). Near-infrared spectroscopic assessment of soil texture in an oxisol of the eastern plains of Colombia. Colombia Forestal, 20(1), 5-18. https://doi.org/10.14483/udistrital.jour.colomb.for.2017.1.a01
Ge, Y.; Morgan, C. L. S. and Ackerson, J. P. (2014). VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact. Geoderma, 221-222, 61-69. https://doi.org/10.1016/j.geoderma.2014.01.011
Gou, Y.; Wei, J.; Li, J.; Han, C.; Tu, Q. and Liu, C. (2020). Estimating purple-soil moisture content using Vis-NIR spectroscopy. Journal of Mountain Science, 17, 2214-2223. https://doi.org/10.1007/s11629-019-5848-2
Huang, Y. C.; Ng, W.; Minasny, B. and McBratney, A. B. (2025). Characterising and quantifying soil clay-sized minerals using mid-infrared spectroscopy. Soil and Tillage Research, 252, 106590. https://doi.org/10.1016/j.still.2025.106590
IGAC. (2004). Estudio general de suelos y zonificación de tierras. Departamento del Tolima. Escala 1: 100000. Ali 2004. Instituto Geográfico Agustín Codazzi (IGAC). https://metadatos.icde.gov.co/geonetwork/srv/api/records/14145360
INVÍAS. (2013). Determinación en el laboratorio del contenido de agua (humedad) de muestras de suelo, roca y mezclas de suelo-agregado. Instituto Nacional de Vías (INVÍAS). https://www.labicivil.com/Media/pdfs/INV%20E-122-13%20DETERMINACI%C3%93N%20EN%20EL%20LABORATORIO%20DEL%20CONTENIDO%20DE%20AGUA%20(HUMEDAD)%20DE%20MUESTRAS%20DE%20SUELO,%20ROCA%20Y%20MEZCLAS%20DE%20SUELO%20-AGREGADO.pdf
Lobell, D. B. and Asner, G. P. (2002). Moisture effects on soil reflectance. Soil Science Society of American Journal, 66(3), 722-727. https://doi.org/10.2136/sssaj2002.7220
Lu, B.; Wang, X.; Hu, C.; Xing, J.; Zhu, S. and Li, X. (2025). On-site rapid quantitative assessment of wet soil nutrients based on laser-induced breakdown spectroscopy fingerprint spectral lines corrected by near-infrared spectroscopy. Computers and Electronics in Agriculture, 237(Part C), 110741, https://doi.org/10.1016/j.compag.2025.110741
Machado, S.; Barreiros, L.; Graça, A. R.; Madeira, M.; Páscoa, R. N. M. J.; Segundo, M. A. and Lopes, J. A. (2023). Assessing the differences of two vineyards soils’ by NIR spectroscopy and chemometrics. Heliyon, 9,(12), e23000. https://doi.org/10.1016/j.heliyon.2023.e23000
Ma, Y.; Roudier, P.; Kumar, K.; Palmada, T.; Grealish, G.; Carrick, S.; Lilburne, L. and Triantafilis, J. (2023). A soil spectral library of New Zealand. Geoderma Regional, 35, e00726. https://doi.org/10.1016/j.geodrs.2023.e00726
Marakkala Manage, L. P.; Greve, M. H.; Knadel, M.; Moldrup, P.; Jonge, L. W. and Katuwal, S. (2018). Visible-near-infrared spectroscopy prediction of soil characteristics as affected by soil-water content. Soil Science Society of American Journal, 82(6), 1333-1346. https://doi.org/10.2136/sssaj2018.01.0052
Meneses-Suárez, D. A.; Lozano-Osorno, F. and Camacho-Tamayo, J.H. (2026). Application of diffuse reflectance spectroscopy (NIR) related to soil electrical conductivity. Revista Brasileira de Engenharia Agrícola e Ambiental, 30(3), e295260. https://www.agriambi.com.br/revista/v30n03/v30n03a09.pdf
Mochen, L.; Kuankuan, Y.; Yinfa, Y.; Zhanhua, S.; Fuyang, T.; Fade, L.; Zhenwei, Y.; Zhang, R.; Qinglu, Y. and Yao, L. (2025). Multi-spectral evaluation of total nitrogen, phosphorus and potassium content in soil using Vis-NIR spectroscopy based on a modified support vector machine with whale optimization algorithm. Soil and Tillage Research, 252, 106567. https://doi.org/10.1016/j.still.2025.106567
Niu, T.; Hou, Z.; Yu, J.; Lu, J.; Yu, Q.; Yang, L.; Ma, J.; Liu, Y.; Shi, H. and Jin, X. (2024). Construction of prediction model for water retention of forest ecosystem in alpine region based on vegetation spectral features. Ecological Indicators, 169, 112889. https://doi.org/10.1016/j.ecolind.2024.112889
Nocita, M.; Stevens, A.; Noon, C. and van Wesemael, B. (2013). Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 199, 37-42. https://doi.org/10.1016/j.geoderma.2012.07.020
Norouzi, S.; Sadeghi, M.; Tuller, M.; Liaghat, A.; Jones, S. B. and Ebrahimian, H. (2022). A novel physical-empirical model linking shortwave infrared reflectance and soil water retention. Journal of Hydrology, 614(Part B), 128653. https://doi.org/10.1016/j.jhydrol.2022.128653
Ordoñez Delgado, N. and Bolívar Gamboa, A. (2014). Levantamiento agrológico del Centro Agropecuario. Universidad Nacional de Colombia, Instituto Geográfico Agustín Codazzi. Subdirección de Agrología. Imprenta Nacional de Colombia. https://tiendavirtual.igac.gov.co/es/content/levantamiento-agrol%C3%B3gico-del-centro-agropecuario-marengo
Park, S.; Jeon, S.; Kwon, N. H.; Kwon, M.; Shin, J. H.; Kim, W. C. and Lee, J. G. (2024). Application of near-infrared spectroscopy to predict chemical properties in clay rich soil: a review. European Journal of Agronomy, 159, 127228. https://doi.org/10.1016/j.eja.2024.127228
Salazar, O.; Benvenuto, A.; Fajardo, M.; Fuentes, J. P.; Nájera, F.; Celedón, A.; Pfeiffer, M.; Renwick, L. L. R.; Seguel, O.; Tapia, Y. and Casanova, M. (2023). Evaluation of a miniaturized portable NIR spectrometer for the prediction of soil properties in Mediterranean central Chile. Geoderma Regional, 34, e00675. https://doi.org/10.1016/j.geodrs.2023.e00675
Seo, C.; Jo, H. Y.; Byun, Y.; Ryu, J. H. and Joo, Y. (2024). Montmorillonite content prediction in bentonite using Vis–NIR spectroscopy and PLSR analysis: effects of humidity and mineralogical variability. Geoderma, 448, 116980, https://doi.org/10.1016/j.geoderma.2024.116980
Stenberg, B.; Viscarra Rossel, R. A.; Mouazen, A. M. and Wetterlind, J. (2010). Chapter five - Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107, 163-215. https://doi.org/10.1016/S0065-2113(10)07005-7
Tian, J.; Yue, J.; Philpot, W. D.; Dong, X. and Tian, Q. (2021). Soil moisture content estimate with drying process segmentation using shortwave infrared bands. Remote Sensing of Environment, 263, 112552, https://doi.org/10.1016/j.rse.2021.112552
Wang, Y. P.; Lee, C. K.; Dai, Y. H. and Shen, Y. (2020). Effect of wetting on the determination of soil organic matter content using visible and near-infrared spectrometer. Geoderma, 376, 114528. https://doi.org/10.1016/j.geoderma.2020.114528
Whalley, W. R.; Leeds-Harrison, P. B. and Bowman, G. E. (1991). Estimation of soil moisture status using near infrared reflectance. Hydrological Processes, 5(3), 321-327. https://doi.org/10.1002/hyp.3360050312
Whalley, W. R. and Stafford, J. V. (1992). Real-time sensing of soil water content from mobile machinery: options for sensor design. Computers and Electronics in Agriculture, 7(4), 269-284. https://doi.org/10.1016/S0168-1699(05)80010-9
Whiting, M. L.; Li, L. and Ustin, S. L. (2004). Predicting water content using Gaussian model on soil spectra. Remote Sensing of Environment, 89(4), 535-552. https://doi.org/10.1016/J.RSE.2003.11.009
Xu, L.; Tian, Z. and Zhang, K. (2026). An improving spectral PTF for mining area soil water content prediction: combining 2D correlation spectroscopy and soil-crop indicators with ResGRU. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 347, 126889, https://doi.org/10.1016/j.saa.2025.126889
Zhang, X.; Feng, G. and Sun, X. (2024). Advanced technologies of soil moisture monitoring in precision agriculture: a review. Journal of Agriculture and Food Research, 18, 101473. https://doi.org/10.1016/j.jafr.2024.101473
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