Resumen

GARCIA REYES, Roberto Alejandro  y  RUIZ-PEREZ, María Elena. Relationship of Organic Matter Content with Spectral Indices in Soil Dedicates to Rice Cultivation. Rev Cie Téc Agr [online]. 2023, vol.32, n.3  Epub 01-Sep-2023. ISSN 2071-0054.

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.

Palabras clave : Kriging; Landsat; Remote Sensing.

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