Modelling corn production in China using AVHRR‐based vegetation health indices

Kogan, Felix; Yang, Bojun; Guo, Wei; Pei, Zhiyuan; Jiao, Xianfeng

doi:10.1080/01431160500034235

Cited by 65 publications

(52 citation statements)

References 7 publications

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“…This observation is expected for wheat; healthy vegetation reflects more radiation in the near infrared section of the solar spectrum as compared to the visible section because this portion absorbs chlorophyll. Additionally, healthy vegetation emits less thermal radiation in the infrared section because of cooler transpiration from the canopy (Kogan et al, 2005). The moisture indices are positively related to LST and indicate that wheat farms, which are flourishing have a high moisture content.…”

Section: Spatial Variation Of the Lst-vegetation Index Relationships mentioning

confidence: 82%

“…Weng et al (2004) observed a negative relationship between cropland and land surface temperature. These relationships arise due to the cooling effects of canopy transpiration (Kogan et al, 2005). The use of the LST-vegetation relationships for crop health status monitoring described in this research can be used to replace the Crop Water Stress Index (CWSI), which is more computationally intensive as it requires the computation of the vegetation index-temperature trapezoid (Moran et al, 1994;Clarke, 1997).…”

Section: Statistical Analysis Of the Lst-vegetation Index Relationshipsmentioning

confidence: 99%

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Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Mashaba¹,

Chirima²,

Botai³

et al. 2016

SA J of Geomatics

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Section: Spatial Variation Of the Lst-vegetation Index Relationships mentioning

confidence: 82%

Section: Statistical Analysis Of the Lst-vegetation Index Relationshipsmentioning

confidence: 99%

Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Mashaba¹,

Chirima²,

Botai³

et al. 2016

SA J of Geomatics

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“…NDVI and BT data were composited over a 7-day period, processed to remove shortand long-term noise, special climatology was calculated, and the data were converted to three vegetation health indices (VHI). 9,10,12,13 3 Methodology…”

Section: Datamentioning

confidence: 99%

Space-based vegetation health for wheat yield modeling and prediction in Australia

Kogan

Guo²,

Yang³

et al. 2018

J. Appl. Rem. Sens.

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, "Space-based vegetation health for wheat yield modeling and prediction in Australia," J. Appl. Remote Sens. 12(2), 026002 (2018), doi: 10.1117/1.JRS.12.026002. Abstract. An early warning of crop losses in response to weather fluctuations helps farmers, governments, traders, and policy makers better monitor global food supply and demand and identifies nations in need of aid. This paper discusses the utility of vegetation health (VH) indices, derived from the advance very high-resolution radiometer (AVHRR) and visible infrared imaging radiometer suite (VIIRS), as a proxy for modeling Australian wheat from the National Oceanic and Atmospheric Administration (NOAA) operational afternoon polar-orbiting satellites. These models are used to assess wheat production and to provide an early warning of drought-related losses. The NOAA AVHRR-and VIIRS-based VH indices were used to model wheat yield in Australia. A strong correlation (≥0.7) between wheat yield and VH indices was found during the critical reproductive stage of development (enhanced crops sensitivity to weather), which starts 2 to 3 weeks before and ends 2 to 3 weeks after wheat heading. The results of modeling and independent testing proved that the VH indices (especially those estimating thermal and health conditions) are a good proxy providing 1 to 2 months before harvest yield prediction (with 3% to 6% error). With the new generation of NOAA-20 operational polar-orbiting satellites, launched in November 2017, the VH method will be improved considerably both in an advanced crop/pasture prediction, spatial resolution, and accuracy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…19 All three indices are scaled to range from 0 (severe vegetation stress -low NDVI, hot BT) to 100 (exceptionally favorable conditions -high NDVI, cool BT). 1,20 The GVI data set processed at 16 km 2 spatial resolution and weekly time resolution was averaged over land pixels in Bandarban, Bangladesh. …”

Section: 16mentioning

confidence: 99%

Application of Advanced Very High Resolution Radiometer (AVHRR)-based Vegetation Health Indices for Estimation of Malaria Cases

Krakauer¹,

Roytman²,

Goldberg³

et al. 2010

The American Society of Tropical Medicine and Hygiene

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Satellite data may be used to map climatic conditions conducive to malaria outbreaks, assisting in the targeting of public health interventions to mitigate the worldwide increase in incidence of the mosquito-transmitted disease. This work analyzes correlation between malaria cases and vegetation health (VH) indices derived from satellite remote sensing for each week over a period of 14 years for Bandarban, Bangladesh. Correlation analysis showed that years with a high summer temperature condition index (TCI) tended to be those with high malaria incidence. Principal components regression was performed on patterns of weekly TCI during each of the two annual malaria seasons to construct a model as a function of the TCI. These models reduced the malaria estimation error variance by 57% if first-peak (June–July) TCI was used as the estimator and 74% if second-peak (August–September) was used, compared with an estimation of average number of malaria cases for each year.

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Modelling corn production in China using AVHRR‐based vegetation health indices

Cited by 65 publications

References 7 publications

Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Space-based vegetation health for wheat yield modeling and prediction in Australia

Application of Advanced Very High Resolution Radiometer (AVHRR)-based Vegetation Health Indices for Estimation of Malaria Cases

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