Sunflower mapping using machine learning algorithm in Google Earth Engine platform | JoVE Visualize

Area of Science:

Agricultural Remote Sensing
Machine Learning Applications
Geospatial Analysis

Background:

Sunflower is a globally significant source of vegetable oil, with cultivation widespread, including in Haryana, India.
Accurate crop mapping is hindered by computational demands, data storage needs, small farm sizes, and a lack of knowledge on optimal algorithms and spectral bands.
Existing land use and land cover classification methods often rely on computationally intensive processes and may not be optimized for specific crop mapping.

Purpose of the Study:

To identify the most effective machine learning algorithm (Random Forest vs. Support Vector Machine) for sunflower crop mapping.
To determine the optimal spectral band combinations and data types (Optical, SAR, combined, time series) for accurate sunflower mapping.
To evaluate the efficiency and applicability of the Google Earth Engine (GEE) cloud platform for large-scale crop mapping.

Main Methods:

Comparison of Random Forest (RF) and Support Vector Machine (SVM) algorithms for land use/land cover classification.
Evaluation of six different spectral band combinations, including Sentinel-Optical, Sentinel-SAR, and combined Optical-SAR data, in single and time-series formats.
Utilized the Google Earth Engine (GEE) cloud platform for data processing and analysis of sunflower crop mapping in Haryana, India.

Main Results:

The Random Forest classifier with single-date optical data yielded the highest accuracy among initial combinations (0.0% to 90%).
The Support Vector Machine (SVM) classifier, optimized with specific parameters, achieved superior overall accuracy (98.09%–98.44%) and Kappa coefficients (0.96–0.97).
SVM demonstrated high accuracy for classifying land use, land cover, and sunflower using optical data and combined SAR-optical time series.

Conclusions:

The Google Earth Engine (GEE) platform, coupled with optimized machine learning algorithms like SVM and appropriate satellite data combinations, provides an efficient solution for accurate sunflower crop mapping.
The developed methodology is scalable and applicable to larger regions in India for mapping sunflower and potentially other crops.
This study addresses the limitations of computational power and data handling for precise agricultural monitoring using advanced remote sensing techniques.