Application of Remote Sensing in Crop Production and Farmland Soil Monitoring

Dear Colleagues,

In recent years, the integration of remote sensing technologies with agricultural practices has ushered in a new era of precision farming and soil management. The capacity to acquire high-resolution data from satellites, UAVs, and other airborne platforms has profoundly transformed the methodologies by which farmers monitor crop growth and assess soil condition. Accurate and timely information on crop growth and soil conditions is essential to precision farming and sustainable agricultural production. Precise and timely insights into crop growth and soil conditions are indispensable for facilitating precision farming and fostering sustainable agricultural production. This Special Issue aims to illuminate the latest advancements in remote sensing techniques and their applications in enhancing crop productivity and monitoring soil conditions directly related to crop growth.

We welcome contributions that delve into various aspects of remote sensing in agriculture, focusing particularly on crop growth monitoring and soil conditions crucial for crop growth. These include, but are not limited to, the following:

• Utilization of satellite and aerial remote sensing techniques for continuous monitoring of crop development and estimation of yield.
• Exploration of hyperspectral and multispectral imaging for early detection of crop diseases, nutrient deficiencies, and stress conditions.
• Utilizing remote sensing enables precise and cost-effective data collection in agricultural fields, facilitating spatial analysis of crop growth patterns and soil variability.
• Implementation of machine learning and artificial intelligence algorithms to analyze remote sensing data for prediction modeling in different farming practices.
• Development of innovative models for real-time monitoring of soil moisture, salinity, nutrient content, and other essential soil parameters directly impacting crop health and productivity.
• Integration of remote sensing data with agricultural models for improved crop management.

Dr. Jianjun Wang
Prof. Dr. Jiahua Zhang
Prof. Dr. Rafia Mumtaz
Dr. Minfeng Xing
Guest Editors

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• precision agriculture
• remote sensing
• crop growth monitoring
• yield estimation and prediction
• farmland soil monitoring
• machine learning
• environment
• image processing
• data fusion