Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop

Colovic, Milica; Yu, Kang; Todorović, Mladen; Cantore, V.; Hamze, Mohamad; Albrizio, Rossella

doi:10.3390/agronomy12092181

Cited by 22 publications

(12 citation statements)

References 94 publications

(98 reference statements)

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“…The potential use of the red-edge region for assessing sweet maize nutritional status was confirmed by a study conducted by [45], who reported that the vegetation indices (NDRE, MTCI and CI red-edge ), calculated by red-edge wavelengths, were significantly affected by nitrogen levels. As in our study, they pointed out a positive correlation between the red-edge-based VIs and leaf-gas-exchange parameters.…”

Section: Rgb and Hyperspectral Vegetation Indices For Estimating Maiz...mentioning

confidence: 84%

“…There is evidence from the literature about the high number of studies concerning the use of spectral indices to independently evaluate nitrogen and water status of crops, utilizing either RGB or hyperspectral indices [20,39,45]. However, to the best of our knowledge, none of these studies have specifically addressed the assessment of maize crop response under varied water and nitrogen conditions by integrating both sensing techniques simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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

Colovic,

Stellacci,

Mzid

et al. 2024

Agronomy

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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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Section: Rgb and Hyperspectral Vegetation Indices For Estimating Maiz...mentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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

Colovic,

Stellacci,

Mzid

et al. 2024

Agronomy

Self Cite

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show abstract

“…In ref. [11], vegetation indices were also used to assess the water and nitrogen status of sweet maize. Most of the previous studies were designed using vegetation indices derived from hyperspectral or multispectral data.…”

Section: Discussionmentioning

confidence: 99%

Spectral Library of Maize Leaves under Nitrogen Deficiency Stress

Torres-Madronero

Goez

Guzmán

et al. 2022

Data

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Maize crops occupy an important place in world food security. However, different conditions, such as abiotic stress factors, can affect the productivity of these crops, requiring technologies that facilitate their monitoring. One such technology is spectroscopy, which measures the energy reflected and emitted by a surface along the electromagnetic spectrum. Spectral data can help to identify abiotic factors in plants, since the spectral signature of vegetation has discriminating features associated with the plant’s health condition. This paper introduces a spectral library captured on maize crops under different nitrogen-deficiency stress levels. The datasets will be of potential interest to researchers, ecologists, and agronomists seeking to understand the spectral features of maize under nitrogen-deficiency stress. The library includes three datasets captured at different growth stages of 10 tropical maize genotypes. The spectral signatures collected were in the visible to near-infrared range (450–950 nm). The data were pre-processed to reduce noise and anomalous signatures. This study presents a spectral library of the effects of nitrogen deficiency on ten maize genotypes, highlighting that some genotypes show tolerance to this type of stress at different phenological stages. Most of the evaluated genotypes showed discriminate spectral features 4–6 weeks after sowing. Higher reflectance was obtained at approximately 550 nm for the lowest nitrogen fertilization treatments. Finally, we describe some potential applications of the spectral library of maize leaves under nitrogen-deficiency stress.

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“…In this way, it was ensured that each plot had four grid cell values which amounts to 12 values per treatment, per sensing date (4 grid cell x 3 plots per treatment) (Figure 3). The experiment was set up following the procedure already applied for maize in Colovic and Yuand [56] and Piscitelli and Colovicand [57].…”

Section: Experimental Design and On-field Measurementsmentioning

confidence: 99%

Upgrading Maize Cultivation in Bosnia and Herzegovina from Rainfed to Irrigated Systems: Use of Remote Sensing Data and the Dual Crop Coefficient Approach to Estimate Evapotranspiration

Čadro,

Omerović,

Soares

et al. 2024

Preprint

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A two-year experiment was conducted with a local maize hybrid under full irrigation (F), deficit irrigation (D) and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina. Three approaches (Allen & Pereira (A&P), SIMDualKc (SD), and Vegetation Index (VI)) were used to estimate the actual crop coefficient (Kc act), actual basal crop coefficient (Kcb act), and actual crop evapotranspiration (ETc act) were applied with the dual crop coefficient method and remote sensing (RS) data. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences, in comparison to A&P, of up to 0.24 in D and R conditions, especially in initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71-0.87 for Kc ini act and from 0.80-1.06 for Kc end act, while the mid-season period showed very good agreement with the literature. Maize crop evapotranspiration range is 769-813 mm, 480-752 mm, and 332-618 mm for F, D and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The 67.6% increase in grain yields with irrigation emphasizes the need to transition from rainfed to irrigation-dependent agriculture, even for drought-resistant crops like maize.

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Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop

Cited by 22 publications

References 94 publications

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

Spectral Library of Maize Leaves under Nitrogen Deficiency Stress

Upgrading Maize Cultivation in Bosnia and Herzegovina from Rainfed to Irrigated Systems: Use of Remote Sensing Data and the Dual Crop Coefficient Approach to Estimate Evapotranspiration

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