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MODIS Data Product Non-Technical Description - MOD 11

Sometimes the ground is hot enough to burn your bare feet, sometimes it’s freezing cold and slippery, and sometimes it’s damp and cool. Different places, times of day, seasons, and weather combine to determine the temperature of the ground, which in turn have reciprocal effects on climate, water, and ecology cycles, as well as studies of the relationship between animal/plant life and the chemical/geological composition of the Earth’s surface.

The MODIS instruments collect data about land surface temperatures (LST) as part of the puzzle of how the world works and to help scientists create other MODIS data products. LST is the result of the complex interaction of energy between of the Earth's surface and the atmosphere. Knowing where the surface is hotter or colder reveals key information about energy and heat balance on our warming planet. Because land surface temperature is one of the key parameters in the physics of the land-surface processes, it is a good indicator of the Earth's greenhouse effect. Generally, the lower the LST, the more energy and heat the surface is reflecting, while the higher LSTs are, the more energy and heat is absorbed by the Earth’s systems (For a more detailed explanation, please read Changing Global Land Surface and Why Isn’t Earth Hot as an Oven?.). These data recently helped scientists to determine that the winter of 2001-2002 was unusually drier and warmer than previous winters.

On land, soil and canopy temperature are among the main determinants of the rate of growth of vegetation, and they also govern when growing seasons start and end. LST can be used in agricultural applications, such as evaluating water requirements for wheat and determining frost damage in orange groves. This ability can be quite valuable in such a context. For instance, farmers can use the data to track the temperature of corn plants themselves as well as the air surrounding them as a method of managing water requirements and estimating crop productivity.

The ability to measure LSTs is also quite important to the process of classifying land surface types. The world is a very large and constantly changing place, so knowing how much and where certain land types are, such as deserts, forests, prairies, etc., is extremely important for carbon cycle modelers, natural resource planners, and numerous other studies. Deserts tend to have very high LSTs, forests and plant-covered lands have more moderate temperatures, and permafrost lands have much colder temperatures. This knowledge, combined with seasonal change patterns, allows the creation of land cover maps that can be used by ecologists, urban planners, and many others. One way that LST data are used is in tracking the spread of the West Nile virus in North America. The West Nile virus is linked to land types that can support mosquitoes, the carriers of the virus.
LST also has a significant impact on hydrologic processes, such as evapotranspiration (moisture lost to the air from evaporation and plant transpiration) and snow/ice melt. When LSTs rise, snow and ice melt, and can then evaporate, soak into the ground, and join larger bodies of water. Additionally, higher LSTs contribute to plants’ transpiration process, whereby they draw water from their root systems to replace water lost during photosynthesis.

Obviously, land surface temperature data are valuable to a number of disciplines and groups of people. The LST data that make up this product also help to create other MODIS data products, such as such as Thermal Anomalies, Fires, and Biomass Burning (MOD 14), Leaf Area Index and FPAR (MOD 15), Land Cover/Land Cover Change (MOD 12), and Snow Cover (MOD 10), among others. Over time, this and other data sets will become even more valuable as they show long-term patterns that are difficult or impossible to observe any other way. The longer a data set is, the more confident users can be about the decisions and forecasts they make based on that data, since trends and anomalies will become more and more apparent.

 

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