Prathyusha et al. (2025) Geospatial assessment of cropping intensity: Advances, challenges and future directions

Identification

• Journal: Plant Science Today
• Year: 2025
• Date: 2025-10-08
• Authors: I Prathyusha, K P Ragunath, S Pazhanivelan, R. Kumaraperumal, S Selvakumar, A P Sivamurugan
• DOI: 10.14719/pst.11351

Research Groups

• Department of Remote Sensing & GIS, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
• Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

Short Summary

This review synthesizes the current state, advances, and challenges in geospatial assessment of cropping intensity, highlighting the evolution of methodologies from traditional time-series analysis to modern machine and deep learning algorithms. It identifies critical gaps, such as the need for better integration of socio-economic data and standardized methodologies, and proposes future directions for more accurate and integrated monitoring to address global food security and sustainable agriculture.

Objective

• To review the current state, significant advances, persistent challenges, and future directions of geospatial assessment of cropping intensity, with implications for global food security, sustainable land management, and economic stability.

Study Configuration

• Spatial Scale: Field-scale, micro-scales, farm-level, regional, continental, and global scales. Case studies include India, Southeast Asia, Sub-Saharan Africa, Latin America (Brazil), and China.
• Temporal Scale: Daily, 5-day, 16-day, 6-12 day revisits; multi-season, annual, decadal, and long-term (since 1970s) trend analysis; near real-time monitoring.

Methodology and Data

• Models used:
  • Vegetation Index Time-series Analysis: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Harmonic analysis, Savitzky-Golay filtering, Fourier series analysis, thresholding.
  • Phenology-based Classification: Extraction of key phenological measurements (onset, end, length of growing season) for classifying cropping patterns (single, double, triple cropping).
  • Land Use Trajectory Analysis: Examining long-term changes in land use and cropping patterns.
  • Machine Learning (ML): Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting, XGBoost, Stacking2 ensemble model, Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), Bayes Net.
  • Deep Learning (DL): Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, U-Net, AgriFM architecture.
• Data sources:
  • Satellite Optical Sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), Landsat (Landsat-8/9), Sentinel-2, PlanetScope (CubeSats).
  • Satellite Radar Sensors: Sentinel-1 (C-band Synthetic Aperture Radar), RISAT (X-band and C-band SAR).
  • Airborne Sensors: Unmanned Aerial Vehicles (UAVs/drones), Hyperspectral sensors.
  • Ground-based Data: Ground truth data, IoT devices, national agricultural census, socio-economic survey data, plot-level data from national household surveys.
  • Ancillary Data: Digital elevation models (e.g., SRTM), climate data (rainfall, temperature, growing degree-days), weather forecasts, market prices, socio-economic and policy data.

Main Results

• Geospatial technologies, particularly remote sensing and GIS, are indispensable for monitoring cropping intensity across various scales, offering objective and reproducible observations.
• Methodologies have advanced from traditional vegetation index time-series analysis to sophisticated machine learning and deep learning algorithms, capable of handling complex, non-linear relationships in remote sensing data.
• Multi-source data fusion (optical + SAR, multi-resolution satellite data, satellite + airborne + ground sensors) significantly improves classification accuracy and enables continuous monitoring, especially in cloud-prone regions.
• Cropping intensity is influenced by a complex interplay of biophysical (e.g., irrigation, soil health, climate) and socio-economic drivers (e.g., market demand, labor availability, government policies).
• Cropping intensity data is crucial for yield gap analysis, land degradation monitoring, and understanding its linkages to food security and poverty alleviation.
• Key challenges include spectral confusion between crops and natural vegetation, persistent cloud contamination in optical imagery, temporal mismatch between satellite revisits and short cropping cycles, insufficient ground truth data for validation, and a lack of long-term continuous harmonized records.
• A major research gap is the limited integration of socio-economic and policy data into geospatial models, hindering explanatory and predictive capabilities.
• Future directions emphasize promoting open-access datasets and cloud-based platforms, developing automated and scalable algorithms (especially deep learning), leveraging citizen science for ground truth data, and fostering collaborative, interdisciplinary efforts for data harmonization and model validation.
• The field is moving towards "smart agriculture" through the integration of big data and IoT for dynamic, near real-time, predictive, and prescriptive agricultural intelligence.

Contributions

• Provides a comprehensive and up-to-date review of the state-of-the-art in geospatial assessment of cropping intensity, encompassing technological advancements, methodological innovations, and diverse applications.
• Systematically identifies and categorizes the persistent challenges and research gaps that limit the full potential of geospatial tools for cropping intensity monitoring.
• Highlights the critical importance and effectiveness of multi-source data fusion techniques for robust and continuous monitoring, especially in challenging environmental conditions.
• Emphasizes the necessity of integrating socio-economic and climate data into geospatial models to move beyond descriptive mapping towards explanatory and predictive intelligence.
• Offers a clear roadmap for future research and development, including concrete recommendations for promoting open science, developing advanced algorithms, leveraging citizen science, and fostering interdisciplinary collaboration to achieve global food security and sustainable agriculture goals.

Funding

• Department of Remote Sensing & GIS, Tamil Nadu Agricultural University
• Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University

Citation

@article{Prathyusha2025Geospatial,
  author = {Prathyusha, I and Ragunath, K P and Pazhanivelan, S and Kumaraperumal, R. and Selvakumar, S and Sivamurugan, A P},
  title = {Geospatial assessment of cropping intensity: Advances, challenges and future directions},
  journal = {Plant Science Today},
  year = {2025},
  doi = {10.14719/pst.11351},
  url = {https://doi.org/10.14719/pst.11351}
}