A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards

Delalieux, Stephanie; Somers, Ben; Hereijgers, S.; Verstraeten, Willem; Keulemans, Wannes; Coppin, Pol

doi:10.1016/j.rse.2008.05.003

Cited by 77 publications

(40 citation statements)

References 52 publications

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“…Most modified vegetation indices were better than their respective originals, including: MNLI vs. NLI, MSAVI2 vs. SAVI and RDVI vs. DVI, which is consistent with previous studies [18,19,42]. Some vegetation indices based on three discrete bands also produced strong correlation, including the following: EVI, TVI, TGDVI, MTVI2 and sLAIDI*, which take advantage of sensitive spectral regions to reduce external factor and are highly sensitive to LAI [16,56].…”

Section: Discussionsupporting

confidence: 89%

“…Many different optical indices have been reported in the literature and have proven to be well correlated with vegetation parameters. A total of twenty-four vegetation indices were employed in the present study (Table 3 [ [16][17][18][19][20][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]). The atmospherically-resistant vegetation index (ARVI), DVI, EVI, green normalized difference vegetation index (GNDVI), modified nonlinear vegetation index (MNLI), MSAVI2, modified simple ratio (MSR), NDVI, nonlinear vegetation index (NLI), optimization of SAVI (OSAVI), RDVI, ratio vegetation index (RVI), SAVI, three gradient difference vegetation index (TGDVI), TVI and modified triangular vegetation index (MTVI2) were calculated using simulated reflectance bands of the Moderate Resolution Imaging Spectrometer (MODIS) (blue: 459-479 nm; green: 545-565 nm; red: 620-670 nm; NIR: 841-876 nm).…”

Section: Spectral Position Featuresmentioning

confidence: 99%

“…VIP calculates the contribution of independent variables to [56] the contribution of the dependent variable, with the most influential predictors in the model selected according to the magnitude of their values [60]. VIP scores serve as an apt measure for identifying individual waveband importance and for providing valuable insight into the most effective spectral regions [60,61].…”

Section: Partial Least Squares Regressionmentioning

confidence: 99%

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Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

et al. 2014

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Abstract:The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging task for hyperspectral data. In this study, the hyperspectral reflectance of winter wheat was selected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various growth stages during 2008 and 2009. We extracted hyperspectral features using different techniques, including reflectance spectra and first derivative spectra, absorption and reflectance position and vegetation indices. In order to find the best subset of features with the best predictive accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to estimated LAI values. The results indicated that the red edge-NIR spectral region (680 nm-1300 nm) was the most sensitive to LAI. Most features in this region exhibited a high correlation with LAI and had higher VIP values, especially the first derivative waveband at 750 nm (r = 0.900, VIP = 1.144). Adding a large number of features would not

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Section: Discussionsupporting

confidence: 89%

Section: Spectral Position Featuresmentioning

confidence: 99%

Section: Partial Least Squares Regressionmentioning

confidence: 99%

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Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

et al. 2014

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“…A known limitation of NDVI index is the decrease or the saturation in sensitivity when leaf area index values (LAI) increase [45,46], such as coffee trees that can reach values of LAI higher than 8 [47]. Delalieux et al [48] found the NDVI saturation effect when leaf area rates of crops exceed the value 5. On the other hand, EVI has shown to be less prone to saturation effect with higher sensitivity in regions of high biomass [49,50] which could make it more appropriate for studies of coffee canopy.…”

Section: Discussionmentioning

confidence: 99%

Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

Bernardes

Moreira

Adami

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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Abstract:Coffee is the second most valuable traded commodity worldwide. Brazil is the world's largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on OPEN ACCESSRemote Sens. 2012, 4 2493 yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield.

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“…Eitel et al [44] MERIS terrestrial chlorophyll index (MTCI) (R750 − R710)/(R710 − R680) Dash et al [45] Standardized LAI Determining Index (sLAIDI) S(R1050 − R1250)/(R1050 + R1250), S = 5 Delalieux et al [46] Enhanced VI (EVI) 2.5(R800 − R660)/(1 + R800 + 2.4R660) Jiang et al [47] Normalized difference red edge index (NDRE) (R790 − R720)/(R790 + R720) Fitzgerald et al [48] Normalized difference chlorophyll index (NDCI) (R708 − R665)/(R708 + R665) Mishra et al [49] Double-peak canopy nitrogen index (DCNII) (R750 − R700)/(R700 − R670)/(R750 − R670 + 0.09) Jin et al [50] Three band water index (TBWI) (R973 − R1720)/R1447 Jin et al [51] Note: # represents vegetation index.…”

Section: Mcari/mtvi2mentioning

confidence: 99%

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Wang

et al. 2015

Remote Sensing

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Abstract:The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

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A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards

Cited by 77 publications

References 52 publications

Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

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