An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Feng, Wei; Wu, Yapeng; He, Li; Ren, Xingxu; Wang, Yangyang; Hou, Gege; Wang, Yonghua; Wan-dai, Liu; Guo, Tao

doi:10.1007/s11119-019-09648-8

Cited by 53 publications

(32 citation statements)

References 49 publications

(75 reference statements)

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“…Some studies have documented the relationship between the RE and the chlorophyll content in maize [3,6,8,21,22] and have proposed a number of indices that enhanced this ratio-the GNDVI (Green Normalized Difference Vegetation Index), CI_green (Chlorophyll Index Green), NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), NDRE (Normalized Difference Red Edge), CCCI (Canopy Chlorophyll Content Index), CI_rededge (Red edge Chlorophyll Index), MCARI/OSAVI (Modified Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index), MCARI/OSAVI RE (Red Edge-based Modified Chlorophyll Absorption in Reflectance Index/Optimized Soil Adjusted Vegetation Index), TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index), TCARI/OSAVI RE (Red Edge-based Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil Adjusted Vegetation Index), and MTCI (MERIS Terrestrial Chlorophyll Index) among others. One common result reported by researchers is that these indices tend to exhibit saturation values in moderate to high plant cover [6,23,24], but discussing this situation is out of the reach of this manuscript.…”

Section: Introductionmentioning

confidence: 89%

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

et al. 2020

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Knowing the total Nitrogen content (Nt) of forage maize (Zea mays) is important so that decisions can be made quickly and efficiently to adjust the timing and amount of both irrigation and fertilizer. In 2017 and 2018 during three growing cycles in two study plots, leaf samples were collected and the Dumas method was used to estimate Nt. During the same growing seasons and on the same sampling plots, a Parrot Sequoia camera mounted on an unmanned aerial vehicle (UAV) was used to collect high resolution images of forage maize study plots. Thirteen multispectral indices were generated and, from these, a Random Forest (RF) algorithm was used to estimate Nt. RF is a machine-learning technique and is designed to work with extremely large datasets. Overall analysis showed five of the 13 indices as the most important. One of these five, the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index, was found to be the most important for estimation of Nt in forage maize (R2 = 0.76). RF handled the complex dataset in a time-efficient manner and Nt did not differ significantly when compared between traditional methods of evaluating Nt at the canopy level and using UAVs and RF to estimate Nt in forage maize. This result is an opportunity to explore many new research options in precision farming and digital agriculture.

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Section: Introductionmentioning

confidence: 89%

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

et al. 2020

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“…Ten winter wheat plants were randomly sampled from each plot, immediately sealed into a paper bag, and brought back to the laboratory for separation of stems and leaves. LAIs were measured according to the method proposed by Feng et al [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

et al. 2021

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Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

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“…2 − Red) * (1 + 0.5)/(NIR 2 + Red + 0.5)[40,41] 18 SAVI Soil-Adjusted Vegetation Index 1.5 * (NIR − Red))/(NIR + Red + 0.5)[42] 19 GSAVI Green Soil-Adjusted Vegetation Index 1.5 * (NIR − Green)/(NIR + Green + 0.5)[43] 20 OSAVI Optimized Soil-Adjusted Vegetation Index (NIR − Red)/(NIR + Red + 0.16)[42] …”

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confidence: 99%

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

et al. 2020

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Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately.

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An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Cited by 53 publications

References 49 publications

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest

Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

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