Agriculture Research Articles

Document Type

Article

Publication Date

11-29-2025

Journal Title

Remote Sensing

Keywords

remote sensing; broadacre agriculture; crop productivity; phenology; environmental stress

Disciplines

Agriculture

Abstract

Highlights

What are the main findings?
  • Remote sensing methodologies for crop performance monitoring were systematically reviewed across the themes of productivity, phenology, and environmental stress.
  • Advances and challenges were identified within individual themes and in their integration toward holistic monitoring.
What are the implications of the main findings?
  • Emerging integration approaches offer pathways beyond monitoring toward decision-support systems for broadacre agriculture.
  • Future directions of advancing resilience-focused applications of remote sensing are proposed.

Abstract

Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support the early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches, such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration, provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics, cross-theme predictive integration, and accessible tools to guide climate adaptation.

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Digital Object Identifier (DOI)

https://doi.org/https://doi.org/10.3390/rs17233886