Google Earth Engine Tutorial: Time Series Analysis of Soil Moisture

Introduction

Google Earth Engine (GEE) is a powerful platform for analyzing geospatial data, including time series analysis of soil moisture. This tutorial will guide you through the process of retrieving, visualizing, and analyzing soil moisture data over time using GEE’s JavaScript API. Soil moisture data is critical for agriculture, climate modeling, and hydrological studies, and GEE allows you to process large-scale datasets efficiently.

Accessing Soil Moisture Data

GEE provides access to several soil moisture datasets. A commonly used one is the NASA SMAP (Soil Moisture Active Passive) Level 3 Soil Moisture (1 km) dataset. Here’s how to load it:


// Load SMAP soil moisture dataset
var smap = ee.ImageCollection("NASA/SMAP/LE1/SMAP10KM");


Filtering and Preprocessing

Filter the dataset by date, location, and other parameters to extract relevant data. For example:


// Define region of interest (e.g., a polygon)
var region = ee.FeatureCollection("FAO/GAUL/2015/level0").filter(ee.Filter.eq("ADM0_NAME", "Nigeria")).geometry();

// Filter data for a specific time range
var filteredData = smap.filterDate('2020-01-01', '2020-12-31')
.filter(ee.Filter.eq('instrument', 'SMAP'));

Time Series Visualization

To visualize soil moisture trends, use the ui.Chart.image.series() function to generate a time series chart:


// Create a time series chart for the selected region
var chart = ui.Chart.image.series(filteredData, 'soil_moisture', region, 1000)
.setOptions({
title: 'Soil Moisture Time Series',
axisTitle: 'Date',
verticalAxis: {label: 'Soil Moisture (%)'}
});

print(chart);


Statistical Analysis

Compute monthly averages or trends using reduction operations. Example for monthly averages:


// Group images by month and compute the average soil moisture
var monthlyStats = filteredData.select('soil_moisture')
.reduce(ee.Reducer.mean())
.getRegion(region, 1000);

// Print the result
print(monthlyStats);


Exporting Results

You can export the processed data as a CSV file for further analysis:


// Export time series data
Export.table.toDrive({
collection: monthlyStats,
description: 'soil_moisture_time_series',
fileNamePrefix: 'soil_moisture',
fileFormat: 'CSV'
});