Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

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 an...

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Bibliographic Details
Published in:Agronomy (Basel) Vol. 14; no. 3; p. 562
Main Authors: Colovic, Milica, Stellacci, Anna Maria, Mzid, Nada, Di Venosa, Martina, Todorovic, Mladen, Cantore, Vito, Albrizio, Rossella
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-03-2024
Subjects:
Agricultural production
Agriculture
Chlorophyll
Climatic conditions
Color imagery
Corn
Crop diseases
Crops
Data analysis
Gas exchange
hyperspectral sensors
Irrigation
Leaf area
Leaf area index
Leaves
Moisture content
Multivariate analysis
Nitrogen
Parameters
Physiology
Plant diseases
Principal components analysis
Radiation
red-edge region
red-green-blue indices
Remote sensors
Stomata
Stomatal conductance
Transpiration
UAV
Unmanned aerial vehicles
Vegetation
Vegetation index
Water
water and nitrogen stresses
Water content
Wheat
Zea mays L
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Summary: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.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy14030562