Cropland and Yield Mapping with Multi-source Remote Sensing

Dear Colleagues,

Accurate and timely information on cropland distribution and crop yield estimation or in-season forecasting can be used to support government agricultural decision making, assist in agricultural management practices, and optimize resource use. With the rapid development of the radiometric, spatial, temporal, and spectral resolutions of remote sensing technology, the integration of multi-source remote sensing is a good way to enhance the spatial resolution, improve data accuracy, capture a broader range of environmental variables, and enable the comprehensive monitoring and analysis of landscapes at various scales. Therefore, to better understand the challenges and opportunities presented by integrating multi-source remotely sensed observations for agricultural applications (especially for cropland or crop yield mapping), this Special Issue aims to invite original and innovative research on applications of multi-source remote sensing for croplands, the crop yield, and crop-type mapping, or crop parameter retrieval using data assimilation algorithms, machine learning, and deep learning methods, or other state-of-the-art approaches. The research areas may include (but are not limited to) the following:

• Crop yield estimation or forecasting;
• Farmland or crop-type mapping;
• Multi-sensor imagery fusion;
• Spatially explicit crop model development, implementation, and validation;
• Model data assimilation algorithms, systems, and uncertainty;
• Multi-source data for retrieving crop parameters;
• Machine learning or deep learning for agricultural studies.

Dr. Wen Zhuo
Prof. Dr. Shibo Fang
Prof. Dr. Yi Xie
Dr. Xiaochuang Yao
Guest Editors

Manuscript Submission Information

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