Precision Agriculture Monitoring Using Remote Sensing

Dear Colleagues,

Precision agriculture aims at gathering, processing, and analyzing spatiotemporal data in soil (e.g., soil texture and soil moisture) and crop variables related to crop health and dynamics (e.g., leaf area index and leaf chlorophyll content, and water and nitrogen stress) to obtain site-specific crop management strategies for improving resource use efficiency, reducing environmental effects, and maximizing crop productivity. Early crop mapping or real-time monitoring of crop growth is demanded in precision agriculture.  

The rise of near real-time (NRT) collected data from multiple remote sensing platforms, such as satellites, unmanned aerial vehicles, and proximal sensors, in the farmland enable us to conduct NRT agricultural monitoring at a higher spatial resolution. In addition, multi-modal remote sensing sources, such as optical, SAR, and LiDAR data, can provide different features (e.g., red-edge, solar-induced chlorophyll fluorescence, and coherence) to retrieve agricultural variables. In combination with model-data fusion approaches, machine learning techniques, and high-performance computers, advanced approaches of using various remote sensing can be developed for obtaining new insights in precision agriculture. 

This Special Issue focuses on novel methods and applications for precision agricultural monitoring using multi-source remote sensing data. Research areas may include, but are not limited to, the following:

(1) Crop variables (LAI, biomass, height, chlorophyll, and nitrogen) estimation;

(2) Crop phenology detection;

(3) Crop stress (water, nutrient, etc.) identification;

(4) Crop yield prediction;

(5) Crop type mapping;

(6) Site-management zone delineation;

(7) Soil property mapping;

(8) Multi-source, multi-modal data fusion.

Dr. Chunhua Liao
Dr. Taifeng Dong
Prof. Dr. Jiali Shang
Prof. Dr. Jinfei Wang
Guest Editors

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