New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice

Wang, Fumin; Huang, Jingfeng; Tang, Yan-Lin; Wang, Xiuzhen

doi:10.1016/s1672-6308(07)60027-4

Cited by 230 publications

(86 citation statements)

References 19 publications

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“…A large number of VIs have been developed, such as the normalized difference vegetation index (NDVI, [16]), the soil adjusted vegetation index (SAVI, [17]), the modified soil-adjusted vegetation index (MSAVI, [18]), the simple ratio vegetation index (SR, [19]) and the green atmospherically resistant vegetation index (GARI, [20]). Several new indices have been derived from the classic NDVI, e.g., the blue normalized difference vegetation index (BNDVI, [21]) and the green normalized difference vegetation index (GNDVI, [22]). Further VIs have been derived from SR, e.g., the modified simple ratio index (MSR, [23]).…”

Section: Introductionmentioning

confidence: 99%

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Zou

Haikarainen

et al. 2018

Applied Sciences

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Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR 705 ), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60 • . Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR 705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

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Section: Introductionmentioning

confidence: 99%

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Zou

Haikarainen

et al. 2018

Applied Sciences

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“…Currently, many methods estimate LAI from remote sensing data by utilizing the statistical relationship between LAI and spectral vegetation indices (Wang et al, 2007), physical model inversion (Xiao et al, 2009), or other nonparametric methods (Kalacska et al, 2005). Each of these methods features both advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatio-Temporal Patterns of Leaf Area Index in Different Forest Types of India Using High Temporal Remote Sensing Data

Chhabra¹,

Panigrahy²

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Knowledge of temporal variations of Leaf Area Index (LAI) aids in understanding the climate-vegetation interaction of different vegetative systems. This information is amenable from high temporal remote sensing data. India has around 78.37 million hectare, accounting for 23.84% of the geographic area of the country under forest/tree cover. India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. We present a detailed spatio-temporal and inter-seasonal analysis of LAI patterns in different forest types of India using MODIS 8-day composites global LAI/fPAR product for the year 2005 at 1-km spatial resolution. A forest cover mask was generated using SPOT 1-km landuse/landcover classification over the Indian region. The range of estimated LAI varied from 0.1-6.9 among the different forest types. Maximum LAI was observed in tropical evergreen forests in North-Eastern region and Western Ghats. Low LAI was observed in Central Indian region due to predominance of dry deciduous forests. The spatial patterns of seasonal variations detected that for most of the forest types, the peak LAI values were observed during September and October months of the autumn season in contrast to minimum LAI during summer season. The mean LAI and standard deviation for each 8-day LAI composite were also computed and mean monthly LAI profiles were derived for each forest type classified on the basis of their geographical locations. These results are useful indicators for detailed understanding of phenological sequence and may also serve as important inputs for deriving bioclimatic indices for different forest types of India.

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“…Respecto al empleo de distintas bandas para elaborar el índice de vegetación, Wang et al (2007) compararon valores de NDVI calculados a partir de un radiómetro en un lote con arroz, con valores de NDVI calculados reemplazando la banda roja por la azul (BNDVI) y por la verde (GNDVI), y encontraron valores de correlación de 0,981412 entre BNDVI y GNDVI y 0,986607 entre BNDVI y NDVI.…”

Section: Método Del Ndvi (Mnd)unclassified

Empleo del NDVI de una cámara digital modificada para estimar la cobertura del cultivo de papa bajo distintas condiciones de fertilización nitrogenada

Casa

Ovando

Bressanini

et al. 2016

AgriS

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Empleo del ndvi de una camara digital modificada para estimar la cobertura del cultivo de papa bajo distintas condiciones de fertilización nitrogenada de la Casa, A.; G. Ovando, L. Bressanini y J. Martínez resumen La fracción del suelo cubierta por el cultivo (CC) determina tanto la intercepción de radiación solar como el tamaño del aparato transpiratorio. Asimismo, la disponibilidad de nitrógeno (N) en papa (Solanum tuberosum L.) afecta el rendimiento y la calidad de los tubérculos. Este trabajo desarrolla procedimientos digitales para estimar la CC en papa usando imágenes fotográficas de cáma-ras convencionales y modificadas para obtener datos del índice de vegetación de la diferencia normalizada (NDVI), evaluados bajo distintas condiciones de fertilización nitrogenada en cultivos de ciclo tardío en 2012 y 2013. En 2012 se comparó la CC y rendimiento de un sector que no recibió fertilización a la plantación respecto del tratamiento comercial, en tanto que en 2013 se consideraron cuatro tratamientos con distintas dosis de N. Ambas cámaras, como así también los procedimientos para estimar la CC, resultaron apropiados para acusar diferencias significativas en la CC asociadas a la fertilización en 2012. Mientras la menor fertilización en 2012 produjo una disminución anticipada de la CC al final del ciclo, lo que redujo la productividad y la calidad comercial, en 2013 las diferencias de la CC y rendimiento entre tratamientos fueron menos pronunciadas.Palabras clave: Solanum tuberosum L., cámara digital, fertilización, teledetección.

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New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice

Cited by 230 publications

References 19 publications

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Analysis of Spatio-Temporal Patterns of Leaf Area Index in Different Forest Types of India Using High Temporal Remote Sensing Data

Empleo del NDVI de una cámara digital modificada para estimar la cobertura del cultivo de papa bajo distintas condiciones de fertilización nitrogenada

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