Abstract
This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment.
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Claims 5
Claim · model-written summary
Red-edge indices CIred-edge, NDRE and DD were the best predictors of sweet maize physiological traits.
- Taxon
- Sweet Maizet0
- Treatment
- red-edge indices including CIred-edge, NDRE and DDe1
- Outcome
- maize physiological traitse1
Not reportedDirection
Verbatim
t0“Sweet Maize”
e1“the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits”
Claim · model-written summary
WI and WI:NDVI were important predictors of relative water content and crop temperature in sweet maize.
Not reportedDirection
Verbatim
t0“Sweet Maize”
e2“stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature”
Claim · model-written summary
The green-area index contributed significantly to predicting leaf area index, stomatal conductance and leaf transpiration in sweet maize.
- Taxon
- Sweet Maizet0
- Treatment
- green-area indexe3
- Outcome
- leaf area index, stomatal conductance, leaf transpiratione3
Not reportedDirection
Verbatim
t0“Sweet Maize”
e3“the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content”
Claim · model-written summary
The green-area index contributed significantly to predicting relative water content in sweet maize.
Not reportedDirection
Verbatim
t0“Sweet Maize”
e3“the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content”
Claim · model-written summary
Correlation coefficients of GGA, NDLuv and NDLab with crop variables were higher than those of ground hyperspectral indices.
Verbatim
t0“Sweet Maize”
e4“the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level”