Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing

A special issue of Earth (ISSN 2673-4834).

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 594

Special Issue Editors


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Guest Editor
College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
Interests: precision irrigation and deficit irrigation; agricultural water resources management; agrometeorology and evapotranspiration; drip fertigation regulation; modelling of crop growth
Special Issues, Collections and Topics in MDPI journals
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: agriculture; quantitative remote sensing; chlorophyll fluorescence; phenology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Interests: water resources system management; water system nexus simulation and planning; uncertainty optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate and timely monitoring of water, vegetation, and soil variables in farmland ecosystems plays an irreplaceable role in optimizing farmland management for maximizing crop productivity. The recent and rapid development of different sensors (e.g., optical, SAR, and LiDAR) with different temporal and spatial resolutions has greatly promoted the potential of near real-time crop monitoring from space, which is demanded in precision agriculture. Multi-source remote sensing (RS) can provide near real-time and accurate spatial information on farmland surfaces in large areas, such as crop types, crop water deficits, leaf area indexes (LAIs), leaf chlorophyll contents, and soil moistures. The incorporation of dynamic crop traits from multi-source RS data into crop growth models to minimize discrete performance gaps and spatiotemporal gaps has been recently highlighted. However, differences in the association between different RS platforms and the dynamic inconsistencies in the correlation between crop varieties exist. New approaches for effectively fusing multi-source RS data to improve crop growth monitoring are required. Developing advanced approaches for quantifying the soil‒water‒plant relationship based on multi-source RS is urgent as well. This Special Issue will focus on the advancement of multi-source remote sensing in monitoring water, vegetation, and soil conditions in farmland ecosystems. We welcome novel research, reviews, and opinion pieces covering all the related topics, which include but are not limited to:

  • Retrieving soil moisture and nutrients in farmlands;
  • Cultivation structure extraction and area estimation;
  • Crop trait (crop types, LAIs, crop water deficits, and leaf chlorophyll contents) inversion;
  • Assimilation of multi-source RS into crop growth models;
  • Optimization of an irrigation system and the evaluation of water productivity;
  • Construction of a regional intelligent irrigation decision-making system;
  • Further understanding of the soil‒water‒crop relationship.

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Ningbo Cui
Dr. Taifeng Dong
Dr. Cong Wang
Prof. Dr. Yulei Xie
Guest Editors

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Keywords

  • soil variables
  • crop traits
  • multi-source RS
  • crop growth model
  • irrigation system

Published Papers

There is no accepted submissions to this special issue at this moment.
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