An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction

Miura, Tomoaki; Huete, Alfredo; Yoshioka, Hiroki; Holben, B. N.

doi:10.1016/s0034-4257(01)00223-1

Cited by 83 publications

(43 citation statements)

References 29 publications

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“…Unfortunately, the uncertainties of MODIS-C6 VI data used in the present study are unknown so far. However, similar uncertainties have been reported in the earlier collections of MODIS VI data [46,47]. Assuming similar uncertainties in the MODIS C5 and C6 VI data, the uncertainty of NDVI (EVI) based on the formula of 0.02 + 0.02 * VI is ±0.034 (±0.03) in JJA and ±0.03 (±0.026) during the growing season in Illinois (Figure 2).…”

Section: Uncertainties In Detection and Attributionsupporting

confidence: 80%

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Xia

Zhou

2017

Remote Sensing

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This study examines the possible impacts of real-world wind farms (WFs) on vegetation growth using two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), at a~250 m resolution from the MODerate resolution Imaging Spectroradimeter (MODIS) for the period 2003-2014. We focus on two well-studied large WF regions, one in western Texas and the other in northern Illinois. These two regions differ distinctively in terms of land cover, topography, and background climate, allowing us to examine whether the WF impacts on vegetation, if any, vary due to the differences in atmospheric and boundary conditions. We use three methods (spatial coupling analysis, time series analysis, and seasonal cycle analysis) and consider two groups of pixels, wind farm pixels (WFPs) and non-wind-farm pixels (NWFPs), to quantify and attribute such impacts during the pre-and post-turbine periods. Our results indicate that the WFs have insignificant or no detectible impacts on local vegetation growth. At the pixel level, the VI changes demonstrate a random nature and have no spatial coupling with the WF layout. At the regional level, there is no systematic shift in vegetation greenness between the pre-and post-turbine periods. At interannual and seasonal time scales, there are no confident vegetation changes over WFPs relative to NWFPs. These results remain robust when the pre-and post-turbine periods and NWFPs are defined differently. Most importantly, the majority of the VI changes are within the MODIS data uncertainty, suggesting that the WF impacts on vegetation, if any, cannot be separated confidently from the data uncertainty and noise. Overall, there are some small decreases in vegetation greenness over WF regions, but no convincing observational evidence is found for the impacts of operating WFs on vegetation growth.

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Section: Uncertainties In Detection and Attributionsupporting

confidence: 80%

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Xia

Zhou

2017

Remote Sensing

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“…The EVI values range from 0 (bare ground) to 1 (complete vegetation cover). As previously stated, this index provides minimized canopy background influence and reduced atmospheric variations [Miura et al, 2001]. As the LANDSAT data is available at a 16-day time-step, cubic splines are applied on an annual basis to interpolate the EVI at any given DOY.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Examining the relationship between the Enhanced Vegetation Index and grapevine phenology

Fraga

Amraoui

Malheiro

et al. 2014

European Journal of Remote Sensing

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Monitoring the main grapevine phenological stages is a key procedure for optimizing vineyard activities and improving yield and quality attributes. Remote sensing may be an effective and practical monitoring tool, as data from on-board satellite sensors can measure vegetative growth. In the current study, a 12-year time series of four main phenophases (budburst, flowering, veraison and harvest) were obtained from an experimental vineyard located in Lisbon (Portugal). LANDSAT surface reflectances were used to calculate the Enhanced Vegetation Index (EVI) and derived metrics. Both time series were then analysed. Results show statistically significant relationships between vegetation metrics and phenological timings and intervals, such as the linkage between the peak greenness and flowering/veraison. The current study highlights the applicability of remote sensing to monitor grapevine phenology in both retrospective and real-time, bringing an added-value to the winemaking sector.

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“…Índices espectrais de vegetação ou simplesmente índices de vegetação, têm sido largamente utilizados para monitorar a cobertura vegetal da Terra em escalas global e/ou local (Miura et al, 2001). Tais índices são combinações de dados espectrais de duas ou mais bandas, selecionadas com o objetivo de sintetizar e melhorar a relação desses dados com os parâmetros biofísicos da vegetação.…”

Section: Introductionunclassified

Correlação espacial do índice de vegetação (NDVI) de imagem Landsat/ETM+ com atributos do solo

Zanzarini

Pissarra

Brandão

et al. 2013

Rev. bras. eng. agríc. ambient.

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RESUMOAs tecnologias de agricultura de precisão, como o uso da variabilidade espacial dos atributos do solo, vêm sendo muito estudadas para a cultura de cana-de-açúcar. Dentre essas tecnologias se destaca o uso de índices de vegetação derivados de produtos de sensoriamento remoto, como poderosas ferramentas indicadoras do desenvolvimento da vegetação. Objetivou-se, neste estudo, analisar a variabilidade espacial dos teores de argila, fósforo e o pH de um Latossolo Vermelho-amarelo em área com produção de cana-de-açúcar e correlacioná-los com o índice espectral de vegetação (NDVI). Foram estudados, a partir de uma malha georreferenciada, os atributos do solo (argila, fósforo e pH) e gerados mapas de variabilidade espacial. Para esses mesmos locais calculou-se o NDVI possibilitando, além do mapeamento deste índice, a avaliação da correlação espacial entre este e as demais propriedades em estudo. A argila e o teor de fósforo apresentaram correlação espacial positiva com o NDVI enquanto nenhuma correlação espacial foi observada com o pH. A imagem do sensor ETM+ do satélite Landsat 5 utilizada neste estudo em relação ao NDVI, apresentou boa aplicação para observar a variabilidade espacial dos atributos estudados. Palavras-chave: sensoriamento remoto, variabilidade espacial, argila, pH, fósforo Spatial correlation of the vegetation index (NDVI)of a Landsat/ETM+ images with soil attributes ABSTRACT The precision agriculture technologies such as the spatial variability of soil attributes have been widely studied mostly with sugarcane. Among these technologies have been recently highlighted the use of the vegetation index derived from remote sensing products, such as powerful tools indicating the development of vegetation. This study aimed to analyze the spatial variability of clay content, pH and phosphorus in an Oxisol in an area with sugarcane production, and correlate with the Normalized Difference Vegetation Index (NDVI). The georeferenced grid was created for the soil properties (clay, phosphorus and pH) and generated the maps of spatial variability. For these same sites were calculated the NDVI, in addition to mapping of this ratio, the evaluation of the spatial correlation between this and other studied properties. The clay and phosphorus content showed positive spatial correlation with the NDVI, while no spatial correlation was observed between NDVI and pH. The satellite images from the sensor ETM + Landsat were used to correlate to NDVI to observe the spatial variability of the studied attributes.

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An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction

Cited by 83 publications

References 29 publications

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

Examining the relationship between the Enhanced Vegetation Index and grapevine phenology

Correlação espacial do índice de vegetação (NDVI) de imagem Landsat/ETM+ com atributos do solo

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