A Preliminary Study on the Use of Remote Sensing Techniques to Determine the Nutritional Status and Productivity of Oats on Spatially Variable Sandy Soils

Field studies and satellite imagery were conducted on an oat cultivation field located on sandy soil with significant spatial heterogeneity in southwestern Poland. Observations and field measurements were carried out during the BBCH growth stages 12, 31, 49, 77, and 99 at 40 points each. Satellite images were acquired at specific intervals, and selected remote sensing indices (NDVI, GNDVI, SAVI, EVI, NDMI, MCARI) were calculated to investigate possibility of early detection of nitrogen demand at the early stage of oat development. The results of this study confirmed that sandy soils, characterized by limited water and nutrient capacity, require a specialized approach to resource management. The selected remote sensing indices provided an effective method for monitoring oat canopy variability in real time. At BBCH 12 growing stage, the highest correlations with plant density were shown by NDVI, SAVI, GNDVI, and EVI. The correlation coefficients ranged from 0.38 to 0.56, with a significance level of ≤0.01, which indicates their usefulness for monitoring crop emergency and early development. At early growing stage (BBCH 31–34), GNDVI was significantly correlated with the final nitrogen uptake (r = 0.44, p < 0.01) and biomass yield of oat (r = 0.39, p = 0.01). This suggests that the GNDVI index is particularly useful for predicting the final nitrogen uptake and biomass yield of oat. It offers a reliable estimation of the plant’s nitrogen status and its potential for nitrogen absorption, allowing for fertilization management at this critical stage.

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Publication Details

Journal
Agronomy
Published
2025-02-28
DOI
https://doi.org/10.3390/agronomy15030616
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

A Preliminary Study on the Use of Remote Sensing Techniques to Determine the Nutritional Status and Productivity of Oats on Spatially Variable Sandy Soils

W. Fialkiewicz, Arkadiusz Głogowski, Józef Sowiński, A. Franz
Agronomy
Remote Sensing in Agriculture
article

A Preliminary Study on the Use of Remote Sensing Techniques to Determine the Nutritional Status and Productivity of Oats on Spatially Variable Sandy Soils

W. Fialkiewicz, Arkadiusz Głogowski, Józef Sowiński, A. Franz
article en

Abstract

Field studies and satellite imagery were conducted on an oat cultivation field located on sandy soil with significant spatial heterogeneity in southwestern Poland. Observations and field measurements were carried out during the BBCH growth stages 12, 31, 49, 77, and 99 at 40 points each. Satellite images were acquired at specific intervals, and selected remote sensing indices (NDVI, GNDVI, SAVI, EVI, NDMI, MCARI) were calculated to investigate possibility of early detection of nitrogen demand at the early stage of oat development. The results of this study confirmed that sandy soils, characterized by limited water and nutrient capacity, require a specialized approach to resource management. The selected remote sensing indices provided an effective method for monitoring oat canopy variability in real time. At BBCH 12 growing stage, the highest correlations with plant density were shown by NDVI, SAVI, GNDVI, and EVI. The correlation coefficients ranged from 0.38 to 0.56, with a significance level of ≤0.01, which indicates their usefulness for monitoring crop emergency and early development. At early growing stage (BBCH 31–34), GNDVI was significantly correlated with the final nitrogen uptake (r = 0.44, p < 0.01) and biomass yield of oat (r = 0.39, p = 0.01). This suggests that the GNDVI index is particularly useful for predicting the final nitrogen uptake and biomass yield of oat. It offers a reliable estimation of the plant’s nitrogen status and its potential for nitrogen absorption, allowing for fertilization management at this critical stage.

AgronomyVol. 15(3)
University of Basel (CH), Wrocław University of Environmental and Life Sciences (PL), Institute of Environmental Engineering (PL)
Narodowe Centrum Nauki
Openalex Percentile: Top 97%
Remote Sensing in Agriculture
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