Applications of remote sensing for crop residue cover mapping

• Crop residue is critical for soil and crop health, and it is valuable to monitor • Many previous studies used remote sensing to quantify crop residue cover (CRC) • This literature review emphasizes remote sensing platforms, sensors, and analytical methods estimation • Recent advancements in these techniques can further support residue management Crop residue is critical for the health of soils and crops as it can maintain soil moisture, reduce soil erosion, support soil nutrient cycling, and increase soil carbon sequestration. Monitoring crop residue cover (CRC) is thus essential for understanding the distribution and amount of crop residues in the field and for developing corresponding management strategies. Remote sensing is a powerful geospatial technique that enables the collection of images covering large areas repeatedly, which can contribute greatly to CRC mapping. This paper reviews the use of remote sensing in estimating CRC, focusing on different remote sensing platforms (e.g., satellites and drones), sensors (e.g., multispectral, hyperspectral, non-optical) and analytical methods (e.g., spectral unmixing, image classification). A total of 101 studies were selected based on their relevance to the scope of this review. The review found that while remote sensing technologies have shown great potential in accurately monitoring CRC, challenges remain in data integration, sensor selection, and computational demands, pointing to the need for ongoing research to optimize crop residue monitoring. This review is expected to bring more insights to agricultural researchers and practitioners and promote developing effective techniques for CRC mapping and management.

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Publication Details

Journal
Smart Agricultural Technology
Published
2025-03-05
DOI
https://doi.org/10.1016/j.atech.2025.100880
Citations
7
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
5.59
Controls
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article

Applications of remote sensing for crop residue cover mapping

David J. McCaffrey, Margaret Schmidt, Bing Lu, Yang Liu et al.
7 citations
Smart Agricultural Technology
Remote Sensing and LiDAR Applications
5.59
article

Applications of remote sensing for crop residue cover mapping

David J. McCaffrey, Margaret Schmidt, Bing Lu, Yang Liu, Sowmya Natesan
article en
7 citations

Abstract

• Crop residue is critical for soil and crop health, and it is valuable to monitor • Many previous studies used remote sensing to quantify crop residue cover (CRC) • This literature review emphasizes remote sensing platforms, sensors, and analytical methods estimation • Recent advancements in these techniques can further support residue management Crop residue is critical for the health of soils and crops as it can maintain soil moisture, reduce soil erosion, support soil nutrient cycling, and increase soil carbon sequestration. Monitoring crop residue cover (CRC) is thus essential for understanding the distribution and amount of crop residues in the field and for developing corresponding management strategies. Remote sensing is a powerful geospatial technique that enables the collection of images covering large areas repeatedly, which can contribute greatly to CRC mapping. This paper reviews the use of remote sensing in estimating CRC, focusing on different remote sensing platforms (e.g., satellites and drones), sensors (e.g., multispectral, hyperspectral, non-optical) and analytical methods (e.g., spectral unmixing, image classification). A total of 101 studies were selected based on their relevance to the scope of this review. The review found that while remote sensing technologies have shown great potential in accurately monitoring CRC, challenges remain in data integration, sensor selection, and computational demands, pointing to the need for ongoing research to optimize crop residue monitoring. This review is expected to bring more insights to agricultural researchers and practitioners and promote developing effective techniques for CRC mapping and management.

Smart Agricultural TechnologyVol. 11
Simon Fraser University (CA), Mira Geoscience (Canada) (CA)
Zero hunger
Openalex Percentile: Top 4%
Remote Sensing and LiDAR Applications
5.59
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