Evaluating the performance of the CCCI-CNI index for estimating N status of winter wheat

Palka, M.; Manschadi, Ahmad M.; Koppensteiner, Lukas J.; Neubauer, Thomas; Fitzgerald, Glenn J.

doi:10.1016/j.eja.2021.126346

Cited by 17 publications

(10 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The four-stage model was constructed based on the GBNDVI, TCARI, NRI, and MSAVI2 spectral indices, whose functions were logarithmic, linear, linear, and power for each spectral index, respectively. The form of model function is consistent with other research results [29,34,72,73]. To further analyze these indices, the sensitivity of the model using NE was discussed (Figure 6).…”

Section: Sensitivity Validity and Applicability Of The Modelsupporting

confidence: 83%

“…Compared with the results of the satellite observation experiments, it was found that the prediction accuracy of this method was higher than that of the satellite observation experiments [47][48][49]. Furthermore, it could overcome the influence of satellite remote sensing by heavy field cycle [22] and weather cloud [18,[27][28][29][30]. For example, Li, F.L.…”

Section: Model Inversion Results Based On Uav Imagesmentioning

confidence: 99%

“…However, satellite remote sensing data are greatly affected by weather-it is rainy and cloudy during the growing period of summer maize. It is challenging to obtain remote sensing data in time under cloudy conditions [18,[27][28][29][30]. Furthermore, some authors stress that this technique has limited operational flexibility for real-time field monitoring or management due to the low spatial resolution of the images acquired and the long intervals between satellite revisits.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

et al. 2022

View full text Add to dashboard Cite

Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution.

show abstract

Section: Sensitivity Validity and Applicability Of The Modelsupporting

confidence: 83%

Section: Model Inversion Results Based On Uav Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The GDD is a good indicator of crop growth and development during the growing period. In addition, the crops may display changed physiological status and influence the relationships between crop N status and sensing parameters in different years even at the same growth phase [64,65], partly due to weather conditions [32]. The combination of GDD using multiplication or division method could help to increase the variation of Multiplex indicators at different growth stages just like the PNC and PNU, and help to identify the seasonal variation at the same time.…”

Section: Comparison Of Different Regression Modelsmentioning

confidence: 99%

Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

et al. 2021

View full text Add to dashboard Cite

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.

show abstract

“…Another parameter used to estimate nitrogen and chlorophyll contents is red edge position (REP), which corresponds to the inflection point between 680 and 780 nm (Cho and Skidmore, 2006) or 680 and 750 nm in the spectral reflectance (Palka et al, 2021). There are several methods for calculating REP; one, based on linear interpolation (REP-L), has been used to estimate leaf chlorophyll and nitrogen contents in leaves (Guyot and Baret, 1988) because it is affected by the biochemical and biophysical characteristics of plants.…”

Section: Introductionmentioning

confidence: 99%

Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis

Ramos-García

Martinez

2022

Rev. Colomb. Cienc. Hortic

View full text Add to dashboard Cite

The relationship between reflectance and chlorophyll (Chl) and nitrogen (N) contents in maize leaves was analyzed to identify useful spectral indices for diagnosing the nutritional status of crops in terms of N. An experiment was carried out in random blocks with five treatments of nitrogen fertilizer (0, 50, 100, 150, 200 kg ha-1) and four repetitions and the foliar spectral responses were measured with a FieldSpec 4 spectroradiometer in three phenological stages. Several spectral indices and values of red edge position (REP) were calculated using various methods. Red-edge position linear interpolation (REP-L), Red-edge position linear extrapolation (REP-LE), REP-Inverted Gaussian fitting technique (REP-IG), REP-Polynomial fitting technique (REP-P) and NDVI had the best relationship with chlorophyll and nitrogen contents. The first derivative of reflectance, between 560 and 760 nm, transformed by the normal state variable (SNV) also had highly significant correlation coefficients with the N, Chl, and yield. Additionally, the corn yield showed highly significant correlations with the N and Chl contents. From the point of view of the diagnosis of the nutritional status of corn, the spectral indices and REP values were suitable for establishing the nutritional status of corn in relation to N in the phenological stages V8 and R1.

show abstract

Evaluating the performance of the CCCI-CNI index for estimating N status of winter wheat

Cited by 17 publications

References 67 publications

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis

Contact Info

Product

Resources

About