Toward Resilience in Broadacre Agriculture: A Methodological Review of Remote Sensing in Crop Productivity, Phenology, and Environmental Stress Detection

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.

Plant phenotyping relevance

作物の生産性、フェノロジー、環境ストレスをリモートセンシングで推定する方法論的レビューであり、植物状態の取得・推定手法が中心である。

Code and data availability

This is a methodological review with a case study. The Data Availability Statement points only to a supplementary list of reviewed articles (not phenotype data, images, or code) and states generically that case-study datasets are 'publicly accessible through their respective official data portals' without naming any. R

No evidence-backed public reproduction asset is currently recorded.