Estimation of soil moisture conditions with Landsat TM in Guangzhou

Q. Sun; J. Tan; S. Chen

doi:10.1117/12.812999

3 November 2008 Estimation of soil moisture conditions with Landsat TM in Guangzhou

Q. Sun, J. Tan, S. Chen

Proceedings Volume 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments; 71450M (2008) https://doi.org/10.1117/12.812999
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

As useful indicators for land surface characteristics, Land Surface Temperature (LST) and Normal Different Vegetation Index (NDVI) can provide information on vegetation and moisture conditions at the surface. In this study, Qin's monowindow algorithm and Temperature-Vegetation Dryness Index (TVDI) were employed to study LST and soil moisture conditions in Guangzhou. Landsat TM image dated on November 23, 2005 was used to retrieve the LST and TVDI. A geospatial model was designed and processed for getting LST and soil moisture status. The result images reveal that, the areas with high land surface temperatures mainly appeared in the centers of urban. On 23 November, 2005, areas with high land surface temperatures took up 26.15%, while urban heat island areas with higher land surface temperatures took up 11.6% in Guangzhou. Except water and urban or built-up land, humid and normal areas took up 22.05%, slight drought areas took up 60.75%, drought areas took up 17.01%, and heavy drought areas took up 0.16%.Compare to the real status of soil moisture, the result indicate that the TVDI index can provide a powerful tool to assess the soil moisture conditions for large scale areas in Guangzhou.

Citation Download Citation

Q. Sun, J. Tan, and S. Chen "Estimation of soil moisture conditions with Landsat TM in Guangzhou", Proc. SPIE 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments, 71450M (3 November 2008); https://doi.org/10.1117/12.812999

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KEYWORDS

Soil science

Earth observing sensors

Landsat

Vegetation

Tantalum

Climatology

Satellites

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