Impact of Standing Water Level and Observation Time on Remote-Sensed Canopy Indices for Rice Nitrogen Status Monitoring

The observation time and water background can affect the remote sensing estimates of the nitrogen (N) content in rice crops. This makes the use of vegetation indices (VIs) for N status monitoring and topdressing recommendations challenging, as the timing of panicle initiation and the water level in bays usually differ between farms even when managed using the same irrigation technique. This study aimed to investigate the influence of standing water levels (from 0 to 20 cm) and the time of image acquisition on a set of N-sensitive VIs to identify those less affected by these factors. The experiment was conducted using a split-plot experimental design with two side-by-side bays (main plots) where rice was grown ponded for most of the growing season and aerobically (not permanently ponded), each with four fertilization N rates. The SCCCI and SCCCI2 were the only indices that did not vary depending on the time of the day when the multispectral images were collected. These indices showed the lowest variation among water layer treatments (5%), while the Clg index showed the highest (20%). All VIs were significantly correlated with N uptake (average R2 = 0.73). However, the SCCCI2 was the index that showed the lowest variation in N-uptake estimates resulting in equal N-fertilizer recommendations across water level treatments. The consistent performance of SCCCI2 across different water levels makes this index of interest for different irrigation strategies, including aerobic management, which is gaining increasing attention to improve the sustainability of the rice industry.

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Publication Details

Journal
Remote Sensing
Published
2025-03-16
DOI
https://doi.org/10.3390/rs17061045
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72
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article

Impact of Standing Water Level and Observation Time on Remote-Sensed Canopy Indices for Rice Nitrogen Status Monitoring

Carlos Ballester, Gonzalo Carracelas, John Hornbuckle
1 citations
Remote Sensing
Remote Sensing in Agriculture
1.72
article

Impact of Standing Water Level and Observation Time on Remote-Sensed Canopy Indices for Rice Nitrogen Status Monitoring

Carlos Ballester, Gonzalo Carracelas, John Hornbuckle
article en
1 citations

Abstract

The observation time and water background can affect the remote sensing estimates of the nitrogen (N) content in rice crops. This makes the use of vegetation indices (VIs) for N status monitoring and topdressing recommendations challenging, as the timing of panicle initiation and the water level in bays usually differ between farms even when managed using the same irrigation technique. This study aimed to investigate the influence of standing water levels (from 0 to 20 cm) and the time of image acquisition on a set of N-sensitive VIs to identify those less affected by these factors. The experiment was conducted using a split-plot experimental design with two side-by-side bays (main plots) where rice was grown ponded for most of the growing season and aerobically (not permanently ponded), each with four fertilization N rates. The SCCCI and SCCCI2 were the only indices that did not vary depending on the time of the day when the multispectral images were collected. These indices showed the lowest variation among water layer treatments (5%), while the Clg index showed the highest (20%). All VIs were significantly correlated with N uptake (average R2 = 0.73). However, the SCCCI2 was the index that showed the lowest variation in N-uptake estimates resulting in equal N-fertilizer recommendations across water level treatments. The consistent performance of SCCCI2 across different water levels makes this index of interest for different irrigation strategies, including aerobic management, which is gaining increasing attention to improve the sustainability of the rice industry.

Remote SensingVol. 17(6)
Deakin University (AU), Instituto Nacional de Investigación Agropecuaria (UY)
Openalex Percentile: Top 19%
Remote Sensing in Agriculture
1.72
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