Remote Sensing in Irrigated Crop Water Stress Assessment

Dear Colleagues,

Optimizing water management in agriculture is essential over arid and semi-arid regions in order to preserve water resources, which are already scarce due to human activities and climate change. In these regions, water scarcity is one of the main factors limiting agricultural development and, thus, food security. The impact of such water scarcity is amplified by inefficient irrigation practices, especially since irrigation consumes more than 85% of the available water in these regions. Thus, in order to increase water use efficiency in agriculture, it is necessary to improve on-farm irrigation management by adjusting the irrigation amount to crop water requirements along the crop growing season.

Crop water stress can be detected by measuring the in situ root zone soil moisture in order to trigger the irrigations. However, in situ point measurements are generally expensive, time consuming, and not available over extended areas. Therefore, remote sensing provides an alternative and cost-effective method for mapping and monitoring broad areas and can be used to assess crop water stress through retrieving different biophysical crop variables from a multisensor/multiwavelength.

In this Special Issue, we are seeking original scientific contributions on assessment of Crop Water Stress Based on Different Remote Sensing data. Specific topics include but are not limited to the following:

• Detecting crop water stress from remote sensing;
• Multispectral and hyperspectral remote sensing for mapping plant water status;
• Developing crop water stress indices based on remote sensing data;
• Retrieval of biophysical crop variables from a multisensor remote sensing;
• Time-series analysis of remote sensing data;
• Management and irrigation triggering using crop modeling combined with satellite data;
• Evapotranspiration and remote sensing data;
• Improving irrigation water use efficiency based on remote sensing data;
• Precision farming with high resolution from multispectral and hyperspectral satellite data or drones;
• ICT tools for real-time irrigation management with remote sensing data;
• Decision support tools and irrigation decision making;
• IoT-based smart farming.

Dr. Abdelghani Chehbouni
Prof. Dr. Salah Er-Raki
Guest Editors

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