Understanding Seasonal Variations

Oct 13, 2025 by wbrum.org 145 views

Introduction

In this article, we will explore how to calculate seasonal variations of Land Surface Temperature (LST) over a specific area using Google Earth Engine (GEE). We will build upon the existing code that calculates monthly averages of LST and modify it to extract seasonal variations.

Understanding Seasonal Variations

Seasonal variations refer to the changes in a time series that occur over a specific period of time, typically a year. In the context of LST, seasonal variations can be attributed to changes in temperature due to the movement of the Earth around the Sun. Understanding these variations is crucial for various applications such as climate modeling, agriculture, and urban planning.

Monthly Averages vs. Seasonal Variations

While monthly averages provide a general idea of the temperature trends over a year, they do not capture the nuances of seasonal variations. For instance, a month with high temperatures may not necessarily indicate a hot season, as the temperatures may be influenced by other factors such as weather patterns or human activities.

Modifying the Existing Code

The existing code calculates monthly averages of LST using the following snippet:

var LandsatLST = require('users/...');
// Define the study area
var studyArea = ee.Geometry.Polygon(
[[-122.0, 37.0], [-122.0, 38.0], [-121.0, 38.0], [-121.0, 37.0]]
);
// Load the Landsat 8 LST dataset
var landsatLST = ee.ImageCollection('LANDSAT/LC08/C01/T1')
.filterDate('2013-01-01', '2019-12-31')
.filterBounds(studyArea)
.map(function(image) {
return image.normalizedDifference(['B10', 'B11']).rename('LST');
});
// Calculate monthly averages of LST
var monthlyAverages = landsatLST.map(function(image) {
return image.reduceRegion(ee.Reducer.mean(), studyArea, 30);
});

To extract seasonal variations, we need to modify this code to calculate the average LST for each season (e.g., winter, spring, summer, and autumn) instead of monthly averages.

Calculating Seasonal Averages

To calculate seasonal averages, we need to define the seasons and then calculate the average LST for each season. We can use the following code snippet to achieve this:

// Define the seasons
var seasons = {
  'winter': ['12', '01', '02'],
  'spring': ['03', '04', '05'],
  'summer': ['06', '07', '08'],
  'autumn': ['09', '10', '11']
};
// Calculate seasonal averages of LST
var seasonalAverages = landsatLST.map(function(image) {
var season = ee.String(image.date().format('MM')).getInfo();
var seasonName = Object.keys(seasons).find(function(key) {
return seasons[key].includes(season);
});
return image.reduceRegion(ee.Reducer.mean(), studyArea, 30).set('season', seasonName);
});

In this code snippet, we define the seasons as an object with the season names as keys and the corresponding month numbers as values. We then calculate the seasonal averages by reducing the image collection to the study area and setting the season name as a property of the resulting image.

Visualizing Seasonal Averages

To visualize the seasonal averages, we can use the following code snippet:

// Visualize seasonal averages
Map.setCenter(-122.0, 37.5, 10);
Map.addLayer(seasonalAverages, {min: 0, max: 50, palette: ['blue', 'green', 'yellow', 'red']}, 'Seasonal Averages');

In this code snippet, we set the map center to the study area and add a layer to visualize the seasonal averages. We use a color palette to represent the different seasons.

Conclusion

In this article, we demonstrated how to calculate seasonal variations of LST over a specific area using Google Earth Engine. We modified the existing code to extract seasonal averages instead of monthly averages and visualized the results using a color palette. This approach can be applied to various applications such as climate modeling, agriculture, and urban planning.

Future Work

Future work can include:

Improving the accuracy of seasonal averages: We can use more advanced techniques such as machine learning algorithms to improve the accuracy of seasonal averages.
Exploring other climate variables: We can explore other climate variables such as precipitation, temperature, and humidity to gain a better understanding of the climate dynamics.
Applying the approach to other regions: We can apply the approach to other regions to gain a better understanding of the climate dynamics in different parts of the world.

References

Google Earth Engine Documentation: https://developers.google.com/earth-engine
Landsat 8 LST Dataset: https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1
Seasonal Averages Time Series of an Area: Q&A =====================================================

Introduction

In our previous article, we explored how to calculate seasonal variations of Land Surface Temperature (LST) over a specific area using Google Earth Engine (GEE). We demonstrated how to modify the existing code to extract seasonal averages instead of monthly averages and visualized the results using a color palette. In this article, we will answer some frequently asked questions (FAQs) related to calculating seasonal averages time series of an area.

Q: What is the difference between monthly averages and seasonal averages?

A: Monthly averages provide a general idea of the temperature trends over a year, while seasonal averages capture the nuances of seasonal variations. Seasonal averages are more informative as they take into account the changes in temperature due to the movement of the Earth around the Sun.

Q: How do I define the seasons in my code?

A: You can define the seasons as an object with the season names as keys and the corresponding month numbers as values. For example:

var seasons = {
  'winter': ['12', '01', '02'],
  'spring': ['03', '04', '05'],
  'summer': ['06', '07', '08'],
  'autumn': ['09', '10', '11']
};

Q: How do I calculate the seasonal averages?

A: You can calculate the seasonal averages by reducing the image collection to the study area and setting the season name as a property of the resulting image. For example:

var seasonalAverages = landsatLST.map(function(image) {
  var season = ee.String(image.date().format('MM')).getInfo();
  var seasonName = Object.keys(seasons).find(function(key) {
    return seasons[key].includes(season);
  });
  return image.reduceRegion(ee.Reducer.mean(), studyArea, 30).set('season', seasonName);
});

Q: How do I visualize the seasonal averages?

A: You can visualize the seasonal averages by adding a layer to the map and using a color palette to represent the different seasons. For example:

Map.setCenter(-122.0, 37.5, 10);
Map.addLayer(seasonalAverages, {min: 0, max: 50, palette: ['blue', 'green', 'yellow', 'red']}, 'Seasonal Averages');

Q: Can I use this approach for other climate variables?

A: Yes, you can use this approach for other climate variables such as precipitation, temperature, and humidity. You will need to modify the code to extract the relevant data and calculate the seasonal averages.

Q: Can I apply this approach to other regions?

A: Yes, you can apply this approach to other regions to gain a better understanding of the climate dynamics in different parts of the world. You will need to modify the code to extract the relevant data and calculate the seasonal averages.

Q: What are some potential applications of this approach?

A: Some potential applications of this approach include:

Climate modeling: This approach can be used to improve climate models by incorporating seasonal variations of LST.
Agriculture: This approach can be used to identify areas with suitable climate conditions for specific crops.
Urban planning: This approach can be used to identify areas with high temperatures and humidity, which can inform urban planning decisions.

Conclusion

In this article, we answered some frequently asked questions related to calculating seasonal averages time series of an area. We demonstrated how to define the seasons, calculate the seasonal averages, and visualize the results using a color palette. This approach can be applied to various applications such as climate modeling, agriculture, and urban planning.

Future Work

Future work can include:

Improving the accuracy of seasonal averages: We can use more advanced techniques such as machine learning algorithms to improve the accuracy of seasonal averages.
Exploring other climate variables: We can explore other climate variables such as precipitation, temperature, and humidity to gain a better understanding of the climate dynamics.
Applying the approach to other regions: We can apply the approach to other regions to gain a better understanding of the climate dynamics in different parts of the world.

References

Google Earth Engine Documentation: https://developers.google.com/earth-engine
Landsat 8 LST Dataset: https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1