Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression

Hansen, P. M.; Schjøerring, Jan K.

doi:10.1016/s0034-4257(03)00131-7

Cited by 970 publications

(718 citation statements)

References 40 publications

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“…Several of these indices have also been positively correlated (using logarithmic or linear models) with crop dry matter (DM), leaf area index (LAI), green area index (GAI), and potential photosynthetic capacity [5,17,20]. However, these indices only use a limited portion of the spectral information by reducing, simplifying, or not considering the abundant information it contains [22,23]. Also, optimal wavelengths for estimating traits, such as GY, can vary in accordance with the phenological stage and environmental conditions [18,24].…”

Section: Introductionmentioning

confidence: 99%

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Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg·ha −1 , respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY OPEN ACCESSRemote Sens. 2015, 7 2110 variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R 2 ) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

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Section: Introductionmentioning

confidence: 99%

“…[23][24][25]. One of the approaches is a linear regression model based on least squares; however, this technique performs poorly when input variables are collinear or exceed the number of training cases [26,27].…”

Section: Introductionmentioning

confidence: 99%

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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“…NDVI is calculated utilising the great difference in canopy reflectance between the red and the near-infrared bands. Because of this chain of related properties, NDVI has been used as a valuable tool in estimating crop variables like LAI, crop biomass, grain yield and photosynthetic activity (Penuelas et al 1995;Broge and Mortensen 2002;Hansen and Schjoerring 2003;Groeneveld et al 2007;Xue et al 2007). Based on the above-mentioned data, this study explored the possibilities of estimating the RUE and HI from canopy reflectance by correlation analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Further, in view of the fact that canopy spectral reflectance has been increasingly used to estimate the crop variables related to biophysical, physiological or biochemical characteristics (Penuelas et al 1995;Hansen and Schjoerring 2003;Scotford and Miller 2004;Xue et al 2007;Li et al 2008b), exploring the possibilities of estimating the RUE and HI from canopy reflectance is another aim of this study. Winter wheat, a crop grown widely in the world, was used in the research.…”

Section: Introductionmentioning

confidence: 99%

Radiation-use efficiency and the harvest index of winter wheat at different nitrogen levels and their relationships to canopy spectral reflectance

Luo

2011

Crop Pasture Sci.

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Abstract. Radiation-use efficiency (RUE, g/MJ) and the harvest index (HI, unitless) are two helpful characteristics in interpreting crop response to environmental and climatic changes. They are also increasingly important for accurate crop yield simulation, but they are affected by various environmental factors. In this study, the RUE and HI of winter wheat and their relationships to canopy spectral reflectance were investigated based on the massive field measurements of five nitrogen (N) treatments. Crop production can be separated into light interception and RUE. The results indicated that during a long period of slow growth from emergence to regreening, the effect of N on crop production mainly showed up in an increased light interception by the canopy. During the period of rapid growth from regreening to maturity, it was present in both light interception and RUE. The temporal variations of RUE APAR (aboveground biomass produced per unit of photosynthetically active radiation absorbed by the canopy) during the period from regreening to maturity had different patterns corresponding to the N deficiency, N adequacy and N-excess conditions. Moreover, significant relationships were found between the RUE APAR and the accumulative normalised difference vegetation index (NDVI) in the integrated season (R 2 = 0.68), between the HI and the accumulative NDVI after anthesis (R 2 = 0.89), and between the RUE grain (ratio of grain yield to the total amount of photosynthetically active radiation absorbed by the canopy) and the accumulative NDVI of the whole season (R 2 = 0.89) and that after anthesis (R 2 = 0.94). It suggested that canopy spectral reflectance has the potential to reveal the spatial information of the RUE APAR , HI and RUE grain . It is hoped that this information will be useful in improving the accuracy of crop yield simulation in large areas.

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“…Since plant chlorophyll concentration is highly correlated with nitrogen status (Hansen & Schjoerring, 2003;Haboudane et al, 2004), REP has also been used for evaluating plant nitrogen status (Lamb et al, 2002;Jongschaap & Booij, 2004). However, as most often encountered, REP is not adequate to track the variations in chlorophyll concentration due to its doublepeak feature (Clevers et al, 2004;Cho and Skidmore, 2006).…”

Section: Discussionmentioning

confidence: 99%

Extracting Red Edge Position Parameters from Ground- and Space-Based Hyperspectral Data for Estimation of Canopy Leaf Nitrogen Concentration in Rice

Tian

Yao

Yang

et al. 2011

Plant Production Science

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Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression

Cited by 970 publications

References 40 publications

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Radiation-use efficiency and the harvest index of winter wheat at different nitrogen levels and their relationships to canopy spectral reflectance

Extracting Red Edge Position Parameters from Ground- and Space-Based Hyperspectral Data for Estimation of Canopy Leaf Nitrogen Concentration in Rice

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