Vegetation indices in crop assessments

Wiegand, C. L.; Richardson, A. J.; Escobar, D. E.; Gerbermann, A. H.

doi:10.1016/0034-4257(91)90004-p

Cited by 328 publications

(179 citation statements)

References 40 publications

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“…Lower NDVI AP values occurred due to atmospheric attenuation caused by aerosols, water vapor and ozone vapor, which alter the actual values that should be recorded by the sensor. From the crop cycle, the period represented the sugarcane maturation phase, resulting in an expected reflectance reduction in the infrared band, with a consequent NDVI value reduction (Wiegand et al 1991).…”

Section: Resultsmentioning

confidence: 99%

“…One of these techniques is the Normalized Difference Vegetation Index (NDVI), which is determined by the ratio between the difference and the sum of reflectance in the visible (VIS) and near-infrared (NIR) bands (Wiegand et al 1991). NDVI associates the electromagnetic radiation portion related to biomass amount (NIR) and the electromagnetic radiation portion related to plant photosynthetic pigments (VIS) activity (Jackson & Huete 1991).…”

Section: Introductionmentioning

confidence: 99%

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Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

Pereira

Casaroli

Vellame

et al. 2016

Pesqui. Agropecu. Trop.

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Large farmland areas and the knowledge on the interaction between solar radiation and vegetation canopies have increased the use of data from orbital remote sensors in sugarcane monitoring. However, the constituents of the atmosphere affect the reflectance values obtained by imaging sensors. This study aimed at improving a sugarcane Leaf Area Index (LAI) estimation model, concerning the Normalized Difference Vegetation Index (NDVI) subjected to atmospheric correction. The model generated by the NDVI with atmospheric correction showed the best results (R2 = 0.84; d = 0.95; MAE = 0.44; RMSE = 0.55), in relation to the other models compared. LAI estimation with this model, during the sugarcane plant cycle, reached a maximum of 4.8 at the vegetative growth phase and 2.3 at the end of the maturation phase. Thus, the use of atmospheric correction to estimate the sugarcane LAI is recommended, since this procedure increases the correlations between the LAI estimated by image and by plant parameters.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

Pereira

Casaroli

Vellame

et al. 2016

Pesqui. Agropecu. Trop.

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“…A number of ecological variables have been shown to be correlated with vegetation indices, particularly NDVI (e.g., Asrar et al 1984, Running et al 1989, Wiegand et al 1991, Myneni et al 1992b, Sellers et al 1992). We briefly introduce some of these quantities below.…”

Section: Empiricalmentioning

confidence: 99%

Global terrestrial biogeochemistry: Perturbations, interactions, and time scales

Braswell¹

1996

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“…However, few studies compared LAI estimation based on data from various satellite sensors. (Baret and Guyot, 1991;Wiegand et al, 1991;Thenkabail et al, 2000;Hong et al, 2004;Doraiswamy et al, 2005 …”

Section: Introductionmentioning

confidence: 99%

Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images

Kim¹,

Hong²,

Sudduth³

et al. 2012

Korean Journal of Remote Sensing

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: Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

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Vegetation indices in crop assessments

Cited by 328 publications

References 40 publications

Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

Global terrestrial biogeochemistry: Perturbations, interactions, and time scales

Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images

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