Python-Based Land Suitability Analysis for Wheat Cultivation Using MCE and Google Earth Engine in Punjab-Pakistan

Nadeem Ahmed; Syed Amer Mahmood; Muhammad Shareef Shazil; Saira Batool

Authors

Nadeem Ahmed Department of Space Science, University of the Punjab, Lahore
Syed Amer Mahmood Department of Space Science, University of the Punjab, Lahore
Muhammad Shareef Shazil Department of Space Science, University of the Punjab, Lahore
Saira Batool Center of Integrated Mountain Research, University of the Punjab

Keywords:

Wheat, Land suitability, Google Earth Engine, Sustainable Planning, Remote Sensing

Abstract

The present study aims to examine the suitability of wheat crops in the four districts of Sheikhupura, Gujranwala, Hafizabad, and Nankana Sahib by conducting a thorough examination of various environmental parameters. The study utilizes the Google Earth Engine and advanced mapping techniques to employ a comprehensive Land Use and Land Cover (LULC) categorization, effectively capturing the prevailing terrain characteristics. The integration of temperature-based and soil-based suitability maps provides a comprehensive understanding of the intricate geographical patterns governing the growth circumstances of wheat. The study highlights a significant finding regarding the identification of very appropriate zones, which encompass around 28% of the total land area (4243 square kilometers) out of complete study site. These zones are particularly noteworthy as they emphasize places that are best for the growing of wheat. Approximately 45% (6819 square kilometers) of the overall land area is classified as moderately suitable, while 15% (2273 square kilometers) of the land area is categorized as less suitable. Furthermore, 16% of the total land area, encompassing 2444 square kilometers, is deemed unsuitable. The rigorous examination of soil parameters, such as pH, drainage, electrical conductivity, and soil type, contributes to a comprehensive comprehension of the soil-related elements that influence the adaptability of wheat crops. The study utilizes a Classification and Regression Tree (CART) methodology to classify crops, resulting in accurate outcomes with a ground truthing accuracy rate of 82%. This study employs a comprehensive approach by integrating temperature and soil-based data to provide a suitability map that enhances the identification of places suitable for wheat growing. Notwithstanding the accuracy of the findings, the research acknowledges certain constraints, including the necessity for heightened farmer consciousness and the incorporation of climate change ramifications. This study offers a comprehensive framework for sustainable agricultural planning, focusing on identifying certain regions that are most suitable for wheat growth. The findings of this research will serve as a valuable resource for guiding future initiatives and decision-making processes related to agricultural development in the studied area.

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