A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States

Gu, Yingxin; Brown, Jesslyn F.; Verdin, J. P.; Wardlow, Brian D.

doi:10.1029/2006gl029127

Cited by 505 publications

(334 citation statements)

References 22 publications

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“…This NDWI is the variation developed by Gao (1996). The study conducted by Gu et al (2007) showed that NDWI has a stronger response to drought conditions than NDVI. The average of NDVI and NDWI were consistently lower (NDVI<0.5 and NDWI<0.3) under drought conditions than under nondrought conditions (NDVI>0.6 and NDWI>0.4) At shallow, turbid waters the water-leaving reflectance at NIR is not negligible, and is not only related to phytoplankton abundance, but also to suspended sediment concentration.…”

Section: Characterization Of Spectral Indicesmentioning

confidence: 99%

“…A new method for drought delineation using MODIS surface reflectance data was presented in the paper by Gu et al (2007) Rouse et al (1973), and it has been in use for decades for monitoring vegetation cover, chlorophyll content and other properties of the plants. Absorption of healthy vegetation is very high in the visible wavelength range.…”

Section: Characterization Of Spectral Indicesmentioning

confidence: 99%

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Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

Gulácsi

Kovács

2015

Journal of Environmental Geography

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In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite's MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.

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Section: Characterization Of Spectral Indicesmentioning

confidence: 99%

Section: Characterization Of Spectral Indicesmentioning

confidence: 99%

Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

Gulácsi

Kovács

2015

Journal of Environmental Geography

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“…The Normalized Difference Water Index (NDWI) is commonly used as an accurate estimate of plant water content [10]. It has been successfully applied to the remote detection of plant water content for grasslands [11]. Enhanced Vegetation Index (EVI) is an "optimized" index designed to enhance the vegetation signal with improved sensitivity in high biomass region, and it is also an improvement of vegetation monitoring through de-coupling of the canopy background signal and a reduction in atmospheric influences [12,13].…”

Section: Introductionmentioning

confidence: 99%

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

Wang

Liu

2013

Remote Sensing

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Abstract:Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the OPEN ACCESSRemote Sens. 2013, 5 3612 water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration.

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“…The time interval of these pictures is eight days and accessible for peninsular region of India. These images are for free of cost, pre calibrated and cleaned for cloud effects using the maximum NDVI method (Holben, 1986;King et al, 2003 (Gu et al, 2007). Using this concept, NDVI can be defined as the ratio of difference of near infrared and red to its sum.…”

Section: Methodsmentioning

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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A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States

Cited by 505 publications

References 22 publications

Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

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

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