Earth Observations and Crop Models for Sustainable Agricultural Management: Part II

Dear Colleagues,

Because the 1^st edition of this Special Issue (available at https://www.mdpi.com/journal/remotesensing/special_issues/crop_rs) was so successful, we are pleased to continue the series in a 2^nd edition. We introduce this volume here.

Modern agricultural management strongly requires intensive and extensive information from earth observation and spatially explicit models (SEMs). Thanks to the rapid development of earth observation systems and data processing technologies, the quantity and quality of the available information for agriculture have improved substantially in the past decade. On the other hand, crop models have contributed greatly to agricultural management and research. Both process-based and statistical crop models often require wide-spectrum data input, and inadequate data input will limit the performance and thus the applications of crop models. Many innovative research works have been committed to incorporating earth observations into crop models to facilitate agricultural management, but there are still gaps to be met for sustainable and profitable agricultural management.

To better understand the challenges and opportunities to integrate earth observation with crop modelling technologies, this Special Issue invites contributions on: (i) innovative EO methods to derive crop parameters; (ii) novel spatially-explicit crop models towards a better understanding of agricultural production system and ecosystems; and (iii) remote sensing data assimilation with crop models. Submissions are encouraged to cover a broad range of topics that may include, but are not limited to, the following:

• EO quantitative inversion of crop and relevant environmental parameters
• Calibration and verification of various of EO datasets including Sentinel and Gaofen imagery
• Multi-sensor and multi-system EO data fusion
• EO for monitoring crop growth, health and yield
• EO for pests and diseases
• Spatially-explicit crop model development, implementation, and validation
• Data assimilation algorithms, system and uncertainty

Dr. Zhongxin Chen
Dr. Jianxi Huang
Prof. Guijun Yang
Prof. Shibo Fang
Prof. Zhenhong Li
Guest Editors

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