Remote sensing and GIS are widely used in monitoring

The gap

Evidence profile

Sourced from the abstract and conclusions and future-work section and stated research gap and stated challenges of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 4 journals. Those papers have been cited 83 times in total.

Supporting evidence — 6 representative gaps

• Integrating Remote Sensing, Landscape Metrics, and Random Forest Algorithm to Analyze Crop Patterns, Factors, Diversity, and Fragmentation in a Kharif Agricultural Landscape (2025) · Land · cited 13× · doi

  Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored.

  generalabstract
  Keywords: remote sensing landscape widely used monitoring croplands integrating machine learning metrics holistic management remains underexplored
• An AI-Driven End-to-End Agricultural Guidance System with Multilingual and Voice Support (2026) · International Research Journal on Advanced Engineering Hub (IRJAEH) · doi

  Future work will focus on expanding the regional language support of incorporating satellite imagery for large- scale crop health monitoring, and conducting large- scale field trials in collaboration with agricultural extension services to validate real- world impact and adoption outcomes.

  generalconclusionsevidence 5/5
  Keywords: large scale future focus expanding regional language support incorporating satellite imagery crop health monitoring conducting
• BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation (2024) · Frontiers in Plant Science · cited 37× · doi

  Explore how the spatial and temporal heterogeneity of remotely sensed data affects yield data, - Enrich feature dimensions to explain variations in yield across different ecosystems, - Investigate the mechanisms underlying SIF to enhance the information available for crop yield estimation

  generalfuture-work sectionevidence 5/5
  Keywords: explore spatial temporal heterogeneity remotely sensed data affects
• Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection (2024) · Remote Sensing · cited 11× · doi

  The impacts of spatial and temporal resolutions on crop emergence detection are not well understood. There is a lack of evaluation of the performance of different remote sensing datasets in detecting crop emergences. The importance of high temporal resolution for accurate emergence detection is not well established.

  generalstated research gapevidence 5/5
  Keywords: impacts spatial temporal resolutions crop emergence detection well
• Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection (2024) · Remote Sensing · cited 11× · doi

  Using radar data, such as those from Sentinel-1, to monitor crop emergence, especially in cloudy regions. Evaluating the WISE algorithm in regions with different weather conditions and field sizes. Investigating the use of higher-spatial-resolution data for areas with mixed crops or small fields.

  generalfuture-work sectionevidence 5/5
  Keywords: using radar data sentinel-1 monitor crop emergence especially
• Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection (2024) · Remote Sensing · cited 11× · doi

  Cloud coverage can affect the accuracy of remote sensing observations. The heterogeneity of fields can affect the performance of the WISE algorithm. The lack of usable observations can limit the detection of crop emergences.

  generalstated challengesevidence 5/5
  Keywords: cloud coverage affect accuracy remote sensing observations heterogeneity

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