An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data

Miura, Tomoaki; Huete, Alfredo; Yoshioka, Hiroki

doi:10.1016/j.rse.2005.10.010

Cited by 82 publications

(62 citation statements)

References 30 publications

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“…Similar overlapping satellite spectral reflectance data from the SPOT4-VGT (vegetation instrument of the System Pour l'Observation de la Terre 4) sensor were used to bridge the 1999 AVHRR and MODIS records. The AVHRR NDVI was adjusted to the corresponding MODIS NDVI using spectral transformation equations for constructing longterm consistent NDVI records (Miura, Huete, and Yoshioka 2006). For this investigation we selected the VIP record because it represented the longest continuous global NDVI record available (1982-2010 period) with documented data quality information.…”

Section: Satellite Ndvimentioning

confidence: 99%

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

Kim

Kimball

Zhang

et al. 2014

International Journal of Remote Sensing

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Section: Satellite Ndvimentioning

confidence: 99%

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

Kim

Kimball

Zhang

et al. 2014

International Journal of Remote Sensing

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“…A partir dos dados de radiometria de campo, foi realizada a simulação das bandas individuais do V e IVP, para os diferentes sensores, conforme a função de resposta espectral normalizada de cada banda dos sensores (Miura et al, 2006). Para as bandas tratadas no presente trabalho, as funções de resposta normalizada dos sensores HRVIR, ETM+ e Modis/ Terra foram obtidas em National Aeronautics and Space Administration (2012), Astrium an Eads Company (2011) Os modelos de regressão, utilizados para estudar a relação entre os índices de vegetação e as variáveis biofísicas, foram o linear (y = a + bx), o polinomial do segundo grau (y = a + bx + cx 2 ) e o geométrico (y = ax b ), em que y é a variável dependente (IAF ou produtividade), e x é a variável independente, que pode ser as bandas individuais do V ou do IVP e os índices de vegetação SR, NDVI, Savi e EVI para os sensores HRVIR, ETM+ e Modis.…”

Section: Methodsunclassified

Índices de vegetação simulados de diferentes sensores na estimativa das variáveis biofísicas do feijoeiro

Monteiro

Filho

Xavier

et al. 2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar os índices de vegetação e bandas do vermelho e do infravermelho próximo, gerados a partir dos sensores HRVIR, ETM+ e Modis, nas estimativas de índice de área foliar e produtividade da cultura do feijoeiro. O experimento foi realizado em blocos ao acaso, com parcelas subdivididas, com quatro lâminas de irrigação (179,5, 256,5, 357,5 e 406,2 mm), três doses de N (0,0, 80,0 e 160,0 kg ha -1 ) e quatro repetições. As medidas de reflectância foram obtidas com o Spetron SE-590, no estádio R6 da cultura, nas 48 parcelas. Foram testados: a razão simples, o índice de vegetação por diferença normalizada, índice de vegetação ajustado ao solo e índice de vegetação realçado. Os índices de vegetação foram eficientes na estimativa do índice de área foliar (IAF) e da produtividade da cultura do feijoeiro. Os índices de vegetação e a banda do infravermelho apresentam o mesmo potencial na estimativa do IAF, quando se considera a resolução espectral dos sensores Modis, ETM+ e HRVIR.Termos de indexação: Phaseolus vulgaris, agricultura, reflectância espectral, sensoriamento remoto. Vegetation indices simulated from different sensors for the estimation of biophysical variables of common beanAbstract -The objective of this work was to evaluate the vegetation indices and red and infrared bands of the HRVIR, ETM+ and Modis sensors, in the estimations of leaf area index and grain yield of common bean. The experiment was carried out in a randomized block design, with split plots and four irrigation levels (179.5, 256.5, 357.5, and 406.2 mm), three N rates (0.0, 80.0 and 160.0 kg ha -1 ), and four replicates. The reflectance measurements were obtained through the Spectron SE-590, at R6 stage, on 48 plots. Simple ratio, normalized difference vegetation index, soil-adjusted vegetation index, and enhanced vegetation index were tested. The vegetation indices were efficient to estimate the leaf area index (LAI) and grain yield. The vegetation indices and infrared band show the same potential to estimate LAI, when considering the spectral resolution of Modis, ETM+ and HRVIR.

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“…For instance in the case of IBLI, the performance of the first index was found to perform poorly in estimating drought-related mortality (Jensen et al 2014b). The low quality of livestock mortality data led to study a new algorithm for 6 The accuracy of these information, used to estimate the parameters of the probability distribution for the underlying weather risk, clearly determinates the pure risk of the insurance contract (Makaudze 2012 Regarding the NDVI, for instance, retrieving long-term consistent time series from various sources based on different sensor characteristics and algorithms could be time and resource consuming (Miura et al 2006). In addition to this and other technical constraints (i.e.…”

Section: Pilot Projects In Africamentioning

confidence: 99%

Review of Pilot Projects on Index-Based Insurance in Africa: Insights and Lessons Learned

Marcantonio

Francois

2017

Renewing Local Planning to Face Climate Change in the Tropics

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Agricultural risk management involves a portfolio of strategies that can, to different extents, prevent, reduce, and/or properly transfer the impact of a shock. Adaptation, mitigation and coping strategies provide a range of complementary approaches for managing risks resulting from adverse weather events. This paper discusses how the issue of adverse weather events is a challenge for agriculture, and offers an overview of climate risk management strategies and an in-depth examination of one of the most tested risk transfer tools-index-based insurance. It describes the development of the insurance market in Africa, and analyses the major challenges and contributions made by weather index-based insurance.

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An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data

Cited by 82 publications

References 30 publications

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

Índices de vegetação simulados de diferentes sensores na estimativa das variáveis biofísicas do feijoeiro

Review of Pilot Projects on Index-Based Insurance in Africa: Insights and Lessons Learned

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