Dataset Details

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Dataset Name: Moderate Resolution Imaging Spectroradiometer (MODIS)

Abbreviation: MODIS

Cost: Need to purchase

Provider: NASA

Covered Area(s): Global

Available Year(s)/ Update Frequency: 1999-Present

Resolution Used: 250m

URL: https://modis.gsfc.nasa.gov/

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Related Article(s)

• Authors: Shen et al., (2010)
  Citation: Shen, L., Xu, H., & Xiao, K. (2010, October). Application of Ocean Remote Sensing in the Detection of Harmful Algal Blooms in the East China Sea. In 2010 International Conference on Multimedia Technology (pp. 1-4). IEEE.
  Health Conditions/Application Areas: Ocean Harmful Algal Blooms

• Authors: Shen et al., (2011)
  Citation: Shen, L., Xu, H., Guo, X., & Li, M. (2011). Characteristics of large-scale harmful algal blooms (HABs) in the Yangtze River Estuary and the adjacent East China Sea (ECS) from 2000 to 2010. Journal of Environmental Protection, 2(10), 1285.
  Health Conditions/Application Areas: Ocean Harmful Algal Blooms

• Authors: Shen et al., (2012a)
  Citation: Shen, L., Xu, H., Guo, X., & Wu, P. (2012). Oceanography of Skeletonema costatum harmful algal blooms in the East China Sea using MODIS and QuickSCAT satellite data. Journal of Applied Remote Sensing, 6(1), 063529-063529.
  Health Conditions/Application Areas: Ocean Harmful Algal Blooms

• Authors: Shen et al., (2012b)
  Citation: Shen, L., Xu, H., & Guo, X. (2012). Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework. Sensors, 12(6), 7778-7803.
  Health Conditions/Application Areas: Ocean Harmful Algal Blooms

• Authors: Shen et al., (2014a)
  Citation: Shen, L., Li, Z., & Guo, X. (2014). Remote sensing of leaf area index (LAI) and a spatiotemporally parameterized model for mixed grasslands. International Journal of Applied Science and Technology, 4(1), 46-61.
  Health Conditions/Application Areas: Mixed Grasslands

• Authors: Shen et al., (2014c)
  Citation: Shen, Li & Guo, Xulin & Xiao, Kang. (2014). Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: A case study in the city of Saskatoon (SK, Canada). Open Geosciences. 7. 10.1515/geo-2015-0005.
  Health Conditions/Application Areas: Urban Temperatures

• Authors: Xiao et al., (2018a)
  Citation: Xiao, H., Tong, X., Gao, L., Hu, S., Tan, H., Huang, Z. Y., ... & Tian, H. (2018). Spatial heterogeneity of Haemorrhagic fever with renal syndrome is driven by environmental factors and rodent community composition. PLoS neglected tropical diseases, 12(10), e0006881.
  Health Conditions/Application Areas: Haemorrhagic Fever

• Authors: Guo et al., (2020)
  Citation: Guo, H., Li, W., Yao, F., Wu, J., Zhou, X., Yue, Y., & Yeh, A. G. (2020). Who are more exposed to PM2. 5 pollution: A mobile phone data approach. Environment International, 143, 105821.
  Health Conditions/Application Areas: Air Pollution

• Authors: Yu et al., (2020)
  Citation: Yu, J., Xiao, H., Yang, W., Dellicour, S., Kraemer, M. U., Liu, Y., ... & Tian, H. (2020). The impact of anthropogenic and environmental factors on human rabies cases in China. Transboundary and emerging diseases, 67(6), 2544-2553.
  Health Conditions/Application Areas: Human Rabies

• Authors: Zhao et al., (2020)
  Citation: Zhao, N., Cao, G., Zhang, W., Samson, E. L., & Chen, Y. (2020). Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution. International Journal of Applied Earth Observation and Geoinformation, 87, 102058.
  Health Conditions/Application Areas: Nighttime Lights