Relation between the normalized difference vegetation index and ecological variables

Cihlar, J.; Laurent, L.; Dyer, James A.

doi:10.1016/0034-4257(91)90018-2

Cited by 213 publications

(109 citation statements)

References 41 publications

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“…The CART and RF algorithm were used to establish the downscaling model at a grid pixel resolution of 25 km by 25 km. Multiple studies have acknowledged that precipitation data are strongly correlated to NDVI, LST, and DEM [31][32][33][34][35]. As precipitation is a spatially heterogeneous variable, geolocations (longitude and latitude) are also considered variables that reflect spatial variations in precipitation.…”

Section: Downscaling Algorithmmentioning

confidence: 99%

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

2017

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Precipitation data are important for the fields of hydrology and meteorology, and are fundamental for ecosystem monitoring and climate change research. Satellite-based precipitation products are already able to provide high temporal resolution precipitation information at a global level. However, the coarse spatial resolution has restricted their use in regional level studies. In this study, monthly fine spatial resolution land precipitation data in China was obtained by downscaling the TRMM 3B43 V7 monthly precipitation products. The downscaling model was constructed based on the ensemble learning method called random forest (RF). In addition to the RF model, the classification and regression tree (CART) model was also used to downscale the precipitation data for the purpose of comparison. The results were validated with in situ measurements. Results showed that the RF model outperformed the CART model. The downscaled precipitation data were strongly correlated with the in situ measurements. The downscaling method was applied to mapping fine spatial resolution precipitation over all of China, and is valuable for developing high spatial resolution precipitation products for studies on hydrology, meteorology, and climate science.

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Section: Downscaling Algorithmmentioning

confidence: 99%

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

2017

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“…Entre los trabajos que han aplicado esta metodología pueden citarse los de Wiegand y Richardson (1990), Cihlar et al (1991), Yang et al (1997) o Mora e Iverson (1998). En todos ellos se obtuvieron óptimas estimaciones de ET, tanto para ETR como para ETP, a partir del índice NDVI, que parecerían proporcionar resultados más aproximados a la realidad.…”

Section: ) Métodos Que Utilizan Datos Obtenidos Mediante La Percepciunclassified

Estimación de evapotranspiración real (ETR) y de evapotranspiración potencial (ETP) en el sudoeste bonaerense (Argentina) a partir de imágenes MODIS

et al. 2017

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<p>Using regression analysis between actual evapotranspiration (ETR) and potential evapotranspiration (ETP) values obtained in seven meteorological observatories and remote sensing derived data from MODIS images (Surface temperature and Normalized Difference Vegetation Index - NDVI) models for estimating ETR and ETP in the southwest of the Buenos Aires Province (Argentina) were developed for the 2000–2014 period. Both models were satisfactorily evaluated in the meteorological observatories used. A regression model was adjusted for ETR with a determination coefficient of 0,6959. Regression model was nonlinear in the case of the ETP variable with a determination coefficient of 0,8409. The individual regression analysis for each meteorological observatories explicate the behavior of the regression for the total data set of ETR and ETP. According to these results, the utility of remote sensing in determination of ETR and ETP in areas without meteorological data was confirmed.</p>

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“…National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer, MODIS Terra, SPOT VGT are a few satellite sensors providing vegetation indices to study the vegetation at fine resolution levels. NOAA AVHRR vegetation index, known as Normalized Difference Vegetation Index (NDVI) has been widely used to relate the synoptic meteorology/ cli-matology to understand the vegetation dynamics, vegetation response to climate and climate vegetation feedback mechanism (Cihlar et al, 1991, Davenport and Nicholson, 1993, Barbosa and Kumar, 2011, Kumar et al, 2013. The studies of Kogan (1997), Unganai and Kogan (1998), and Ramesh et al (2003) concluded that AVHRR NDVI is one of the best tools to monitor/assess the large area agricultural droughts.…”

Section: Introductionmentioning

confidence: 99%

Variability in MODIS NDVI in Relation to Southwest Monsoon over Western Ghats, India

Kumar¹,

Uma²,

Rao³

et al. 2016

J ENVIRON INFORM

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ABSTRACT. Eleven years (2000 to 2010) of Normalized Difference Vegetation Index (NDVI) data, derived from Moderate Imaging Spectroradiometer (MODIS) Terra with 250 m resolution is used in the present study to discuss the changes in the vegetal cover over Western Ghats, India. The NDVI over Western Ghats (number of test sites are 16) showed increasing tendency and the pronounced changes in NDVI have been studied in the context of southwest monsoon's distribution and activity. The NDVI progression is observed from June with a minimum value of 0.179 and yielded to a maximum at 0.565 during September/October, on an average. The study then relates NDVI with different rainy events to understand the connection between the ground vegetation and the south west monsoon. The results of the study inferred are as follows: i) NDVI, Antecedent Precipitation Index (API) are in good agreement throughout the monsoon which is evidenced by correlation; ii) NDVI maintained good correlation with a number of Light and Moderate Rainy days alternatively but not with the number of Heavy Rainy days; iii) Relation of NDVI with Isolated, Scattered distributions and active monsoons is substantial; and iv) Rate of Green Up is captured by the satellite during the crop growing season over the Western Ghats.

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Relation between the normalized difference vegetation index and ecological variables

Cited by 213 publications

References 41 publications

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

Estimación de evapotranspiración real (ETR) y de evapotranspiración potencial (ETP) en el sudoeste bonaerense (Argentina) a partir de imágenes MODIS

Variability in MODIS NDVI in Relation to Southwest Monsoon over Western Ghats, India

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