Relationship of Organic Matter Content with Spectral Indices in Soil Dedicates to Rice Cultivation

Main Article Content

Roberto Alejandro García Reyes
María Elena Ruíz-Pérez

Abstract

The determination of soil organic matter is a technique that is affected by the cost of reagents, specialized personnel and the time required. As a feasible tool for the determination of this chemical property, the use of remote sensing from digital image processing and the calculation of spectral indices is proposed. The objective of the research was to find the relationships of the organic matter (OM) content with the spectral index obtained by remote sensing and, given the relationship of OM with the fertility of a soil, to know its spatial variability in an area dedicated to rice cultivation. A systematic sampling was carried out in an area of 100 ha where 100 georeferenced points were selected at a distance between points of 100 m. The samples for the determination of the organic matter content were extracted at a depth between 0-0.20 m in a Chromic Vertisol. The spectral index NDVI, SAVI and the ClayIndex CI were calculated from a Landsat 9 image. Later, linear regression analyzes were performed between these indices and the organic matter content. The average values of organic matter, NDVI and SAVI were 3.81; 0.26 and 0.52%, respectively. The mean value for CI was 1.32. It was obtained that there is a high coefficient of determination with values close to 100 % and significant correlation between the spectral index and the organic matter content. The analysis of the spatial variability of the organic matter values was carried out with the Surfer 8 software and the model that best adjusted the experimental semivariogram was the exponential one. The results obtained are promising for the future estimation of the organic matter content from the spectral index in an agroecosystem dedicated to rice under the same edaphoclimatic conditions of the area.

Article Details

How to Cite
García Reyes, R. A., & Ruíz-Pérez, M. E. (2023). Relationship of Organic Matter Content with Spectral Indices in Soil Dedicates to Rice Cultivation. Revista Ciencias Técnicas Agropecuarias, 32(3), https://cu-id.com/2177/v32n3e01. Retrieved from https://revistas.unah.edu.cu/index.php/rcta/article/view/1737
Section
Original Articles

References

ALEXAKIS, D.D.; TAPOGLOU, E.; VOZINAKI, A.E.K.; TSANIS, I.K.: “Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion”, Remote Sensing, 11(9): 1106, 2019, ISSN: 2072-4292, Publisher: MDPI, DOI: https://doi.org/10.3390/rs11091106.

ANGELOPOULOU, T.; BALAFOUTIS, A.; ZALIDIS, G.; BOCHTIS, D.: “From laboratory to proximal sensing spectroscopy for soil organic carbon estimation-A review”, Sustainability, 12(2): 443, 2020, ISSN: 2071-1050, Publisher: MDPI, DOI: https://doi.org/10.3390/su12020443.

AYOUBI, S.; SHAHRI, A.; KARCHEGANI, P.A.; SAHRAWAT, K.L.: Application of artificial neural network (ANN) to predict soil organic matter using remote sensing data in two ecosystems, Ed. InTech Rijeka, Croatia, vol. Biomass and Remote Sensing of Biomass, 181-196 p., 2011, ISBN: 978-953-307-490-0.

BOETTINGER, J.; RAMSEY, R.; BODILY, J.; COLE, N.; KIENAST-BROWN, S.; NIELD, S.; SAUNDERS, A.; STUM, A.: Landsat spectral data for digital soil mapping, Ed. Digital soil mapping with limited data Australia: Springer Scienc, A.E. Hartemink, A.B. McBratney, M.L. Mendonca-Santo ed., Australia, publisher: Springer, 2008, ISBN: 978-1-4020-8591-8.

BOGUNOVIC, I.; KISIC, I.; MESIC, M.; PERCIN, A.; CAÑIZARES, Z.J.; BILANDŽIJA, D.; JONJIC, A.; PEREIRA, P.: “Reducing sampling intensity in order to investigate spatial variability of soil pH, organic matter and available phosphorus using co-kriging techniques. A case study of acid soils in Eastern Croatia”, Archives of Agronomy and Soil Science, 63(13): 1852-1863, 2017, ISSN: 0365-0340, ublisher: Taylor & Francis, DOI: https://doi.org/10.1080/03650340.2017.1311013.

CAMBARDELLA, C.A.; MOORMAN, T.; NOVAK, J.; PARKIN, T.; KARLEN, D.; TURCO, R.; KONOPKA, A.: “Field‐scale variability of soil properties in central Iowa soils”, Soil science society of America journal, 58(5): 1501-1511, 1994, ISSN: 0361-5995, Publisher: Wiley Online Library, DOI: https://doi.org/10.2136/sssaj1994.03615995005800050033x.

CRESSIE, N.: “The origins of kriging”, Mathematical geology, 22: 239-252, 1990, ISSN: 0882-8121, Publisher: Springer, DOI: https://doi.org/10.1007/978-3-319-78999-6.

DENIS, A.; STEVENS, A.; VAN WESEMAEL, B.; UDELHOVEN, T.; TYCHON, B.: “Soil organic carbon assessment by field and airborne spectrometry in bare croplands: Accounting for soil surface roughness”, Geoderma, 226: 94-102, 2014, ISSN: 0016-7061, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.geoderma.2014.02.015.

DORAN, J.W.; ZEISS, M.R.: “Soil health and sustainability: managing the biotic component of soil quality”, Applied soil ecology, 15(1): 3-11, 2000, ISSN: 0929-1393, Publisher: Elsevier, DOI: https://doi.org/10.1016/S0929-1393(00)00067-6.

DURDEVIC, B.; JUG, I.; JUG, D.; BOGUNOVIC, I.; VUKADINOVIC, V.; STIPESEVIC, B.; BROZOVIC, B.: “Spatial variability of soil organic matter content in Eastern Croatia assessed using different interpolation methods”, International Agrophysics, 33(1), 2019, ISSN: 0236-8722, Publisher: Polska Akademia Nauk. Instytut Agrofizyki PAN, DOI: https://doi.org/10.31545/intagr/104372.

GOMEZ, C.; ROSSEL, R.A.V.; MCBRATNEY, A.B.: “Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study”, Geoderma, 146(3-4): 403-411, 2008, ISSN: 0016-7061, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.geoderma.2008.06.011.

HERNÁNDEZ, J.A.; PÉREZ, J.J.M.; MESA, N.A.; BOSCH, I.D.; RIVERO, L.; CAMACHO, E.: Nueva versión de la clasificación genética de los suelos de Cuba., Ed. AGRINFOR, La Habana, Cuba, ISBN: 959-246-022-1, Barcaz L L ed., vol. I, La Habana, Cuba, 64 p., 2015, ISBN: 959-246-022-1.

HERNÁNDEZ-JIMÉNEZ, A.; PÉREZ-JIMÉNEZ, J.M.; BOSCH-INFANTE, D.; SPECK-CASTRO, N.: “La clasificación de suelos de Cuba: énfasis en la versión de 2015”, Cultivos Tropicales, 40(1), 2019, ISSN: 0258-5936, Publisher: Ediciones INCA.

HUETE, A.R.: “A soil-adjusted vegetation index (SAVI)”, Remote sensing of environment, 25(3): 295-309, 1988, ISSN: 0034-4257, Publisher: Elsevier, DOI: https://doi.org/10.1016/0034-4257(88)90106-X.

JIAN-BING, W.; DU-NING, X.; XING-YI, Z.; XIU-ZHEN, L.; XIAO-YU, L.: “Spatial variability of soil organic carbon in relation to environmental factors of a typical small watershed in the black soil region, northeast China”, Environmental monitoring and assessment, 121: 597-613, 2006, ISSN: 0167-6369, Publisher: Springer, DOI: https://doi.org/10.1007/s10661-005-9158-5.

JOKO-PRASETYO, S.Y.; DWI, K.; CHRISMAWATI-PASELENG, M.; WIDIYANTO, D.C.; WINARKO, E.: “Satellite imagery and machine learning for aridity disaster classification using vegetation indices”, Bulletin of Electrical Engineering and Informatics, 9(3): 1149-1158, 2020, ISSN: 2302-9285, DOI: https://doi.org/10.11591/eei.v9i3.1916.

JOURNEL, A.G.; HUIJBREGTS, C.J.: Mining geostatistics, Ed. Academic Press, London, England, 600 p., 1978.

KERRY, R.; OLIVER, M.: “Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood”, Geoderma, 140(4): 383-396, 2007, ISSN: 0016-7061, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.geoderma.2007.04.019.

LAL, R.: “Soil health and carbon management”, Food and Energy Security, 5(4): 212-222, 2016, ISSN: 2048-3694, Publisher: Wiley Online Library, DOI: https://doi.org/10.1002/fes3.96.

LAL, R.: “Soil organic matter content and crop yield”, Journal of Soil and Water Conservation, 75(2): 27A-32A, 2020, ISSN: 0022-4561, Publisher: Soil and Water Conservation Society, DOI: https://doi.org/10.2489/jswc.75.2.27A.

LAMICHHANE, S.; KUMAR, L.; WILSON, B.: “Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review”, Geoderma, 352: 395-413, 2019, ISSN: 0016-7061, V, DOI: https://doi.org/10.1016/j.geoderma.2019.05.031.

LEGRÁ-LOBAINA, A.A.; ATANES-BEATÓN, D.M.: “Variogramas adaptativos: un método práctico para aumentar la utilidad del error de estimación por kriging”, Minería y Geología, 26(4): 53-78, 2010, ISSN: 1993-8012, Publisher: Instituto Superior Minero Metalúrgico de Moa’Dr Antonio Nuñez Jiménez’.

LIU, Z.; ZHOU, W.; SHEN, J.; HE, P.; LEI, Q.; LIANG, G.: “A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China”, Geoderma, 235: 39-47, 2014, ISSN: 0016-7061, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.geoderma.2014.06.027.

LÓPEZ-GRANADOS, F.; JURADO-EXPÓSITO, M.; PEÑA-BARRAGÁN, J.M.; GARCÍA-TORRES, L.: “Using geostatistical and remote sensing approaches for mapping soil properties”, European Journal of Agronomy, 23(3): 279-289, 2005, ISSN: 1161-0301, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.eja.2004.12.003.

MIRZAEE, S.; GHORBANI-DASHTAKI, S.; MOHAMMADI, J.; ASADI, H.; ASADZADEH, F.: “Spatial variability of soil organic matter using remote sensing data”, Catena, 145: 118-127, 2016, ISSN: 0341-8162, Publisher: Elsevier, DOI: http://dx.doi.org/10.1016/j.catena.2016.05.023.

NORMA CUBANA (NC): Calidad del suelo-determinación de loscomponentes orgánicos. No. 1043. ICS: 13.080.10, 13.080.30, Inst. Oficina Nacional de Normalización, Norma cubana, La Habana, Cuba, 2014.

PRUDNIKOVA, E.; SAVIN, I.: “Some peculiarities of arable soil organic matter detection using optical remote sensing data”, Remote Sensing, 13(12): 2313, 2021, ISSN: 2072-4292, Publisher: MDPI, DOI: https://doi.org/10.3390/rs13122313.

RASEL, S.; GROEN, T.A.; HUSSIN, Y.A.; DITI, I.J.: “Proxies for soil organic carbon derived from remote sensing”, International journal of applied earth observation and geoinformation, 59: 157-166, 2017, ISSN: 1569-8432, Publisher: Elsevier, DOI: http://dx.doi.org/10.1016/j.jag.2017.03.004.

RAWASHDEH, A.S.B.: “Assessment of change detection method based on normalized vegetation index in environmental studies”, International Journal of Applied Science and Engineering, 10(2): 89-97, 2012, ISSN: 1727-7841, Publisher: Chaoyang University of Technology.

REEVES, D.: “The role of soil organic matter in maintaining soil quality in continuous cropping systems”, Soil and Tillage Research, 43(1-2): 131-167, 1997, ISSN: 0167-1987, Publisher: Elsevier, DOI: https://doi.org/10.1016/S0167-1987(97)00038-X.

REZA, S.; NAYAK, D.; CHATTOPADHYAY, T.; MUKHOPADHYAY, S.; SINGH, S.; SRINIVASAN, R.: “Spatial distribution of soil physical properties of alluvial soils: a geostatistical approach”, Archives of agronomy and soil science, 62(7): 972-981, 2016, ISSN: 0365-0340, Publisher: Taylor & Francis, DOI: https://doi.org/10.1080/03650340.2015.1107678.

ROMANYÀ, J.; ROVIRA, P.: “An appraisal of soil organic C content in Mediterranean agricultural soils”, Soil Use and Management, 27(3): 321-332, 2011, ISSN: 0266-0032, Publisher: Wiley Online Library, DOI: https://doi.org/10.1111/j.1475-2743.2011.00346.x.

ROUSE, J.W.; HAAS, R.H.; SCHELL, J.A.; DEERING, D.W.: Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351 ed., vol. NASA SP-351, 309-371 p., 1974, ISBN: 30103017.

SANKAR-GOURI., G.; KUMAR-SHIT, P.; REZA-POURGHASEMI, H.: “Soil organic carbon mapping using remote sensing techniques and multivariate regression model”, Geocarto International, 34(2): 215-226, 2019, ISSN: 1010-6049, Publisher: Taylor & Francis, DOI: https://doi.org/10.1080/10106049.2017.1381179.

SEIDEL, E.J.; OLIVEIRA, M.S.: “Novo índice geoestatístico para a mensuração da dependência espacial”, Revista Brasileira de Ciência do Solo, 38: 699-705, 2014, ISSN: 1806-9657, Publisher: SciELO Brasil, DOI: https://doi.org/10.1590/S0100-06832014000300002

SEIDEL, E.J.; OLIVEIRA, M.S.: “A classification for a geostatistical index of spatial dependence”, Revista Brasileira de Ciência do Solo, 40, 2016, ISSN: 1806-9657, Publisher: SciELO Brasil.

SHIBU, M.; LEFFELAAR, P.; VAN KEULEN, H.; AGGARWAL, P.: “Quantitative description of soil organic matter dynamics-A review of approaches with reference to rice-based cropping systems”, Geoderma, 137(1-2): 1-18, 2006, ISSN: 0016-7061, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.geoderma.2006.08.00.

SODANGO, T.H.; SHA, J.; LI, X.; NOSZCZYK, T.; SHANG, J.; ANESEYEE, A.B.; CHAFIK, Z.: “Modeling the spatial dynamics of soil organic carbon using remotely-sensed predictors in Fuzhou city, China”, Remote Sensing, 13(9): 1682, 2021, ISSN: 2072-4292, Publisher: MDPI, DOI: https://doi.org/10.3390/rs1309168.

WALKLEY, A.; BLACK, I.A.: “An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method”, Soil science, 37(1): 29-38, 1934, ISSN: 0038-075X, Publisher: LWW, DOI: https://doi:10.1097/00010694-193401000-00003.

WANG, X.; ZHANG, F.; JOHNSON, V.C.: “New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China”, Remote Sensing of Environment, 218: 104-118, 2018, ISSN: 0034-4257, Publisher: Elsevier, DOI: https://doi.org/10.1016/j.rse.2018.09.020.

WILDING, L.: “Spatial variability: its documentation, accomodation and implication to soil surveys”, En: Soil spatial variability, Las Vegas NV, 30 November-1 December 1984, Netherlands, pp. 166-194, 1985, ISBN: 90-220-0891-6.

XU, X.; DU, C.; MA, F.; QIU, Z.; ZHOU, J.: “A Framework for High-Resolution Mapping of Soil Organic Matter (SOM) by the Integration of Fourier Mid-Infrared Attenuation Total Reflectance Spectroscopy (FTIR-ATR), Sentinel-2 Images, and DEM Derivatives”, Remote Sensing, 15(4): 1072, 2023, ISSN: 2072-4292, Publisher: MDPI, DOI: https://doi.org/10.3390/rs15041072.

Most read articles by the same author(s)