Khan et al. (2025) Remote sensing-based cropping pattern identification and its impact on groundwater use in canal command areas of an irrigated agriculture region in Pakistan

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-11-29
• Authors: Aftab Haider Khan, Nuaman Ejaz, Songhao Shang, Khalil Ur Rahman, Aqil Tariq
• DOI: 10.1016/j.agwat.2025.110017

Research Groups

• Department of Hydraulic Engineering, Tsinghua University, Beijing, China.
• Department of Hydraulic Engineering, School of Civil Engineering, Shandong University, Jinan, China.
• College of Forest Resources, Mississippi State University, Mississippi State, MS, USA.

Short Summary

This study integrates Sentinel-2 satellite imagery and Random Forest algorithms to map seasonal cropping patterns across eight Canal Command Areas in Pakistan's Bari Doab from 2018 to 2023. The findings quantify a rising dependency on groundwater for irrigation, driven by water-intensive crops and urbanization, leading to significant regional aquifer depletion.

Objective

• To accurately classify Rabi and Kharif crops and quantify cropping pattern changes at the Canal Command Area (CCA) scale to determine their specific impact on groundwater abstraction and storage anomalies.

Study Configuration

• Spatial Scale: Regional scale covering the Bari Doab region in Punjab, Pakistan (approximately 31,000 km²), subdivided into eight distinct Canal Command Areas (CCAs).
• Temporal Scale: Seasonal analysis (Rabi and Kharif) from 2018 to 2023 for crop mapping; long-term groundwater storage trends analyzed from 2002 to 2023.

Methodology and Data

• Models used: Random Forest (RF) machine learning algorithm for crop classification; Penman-Monteith method for estimating crop evapotranspiration ($ET_c$); USDA Soil Conservation Service (SCS) method for effective rainfall calculation.
• Data sources: Sentinel-2 L1C Top-of-Atmosphere (TOA) reflectance; GRACE/GRACE-FO Mascon solutions (CSR RL06) for Terrestrial Water Storage Anomalies (TWSA); Global Land Data Assimilation System (GLDAS v2.2) for non-groundwater components; In-situ piezometric observations from the Soil Salinity Control and Reclamation Projects (SCARP).

Main Results

• Classification Accuracy: The Random Forest model achieved an overall accuracy of 89.9% for Rabi season crops and 90.1% for Kharif season crops.
• Land Use Change: Significant cropland decline was observed in CCA3 (127 km²) and CCA7 (96 km²) between 2018 and 2023, primarily due to rapid urbanization.
• Water Requirements: $ET_c$ was significantly higher for Kharif crops (e.g., sugarcane: 1060.9–1443.7 mm) than Rabi crops (e.g., wheat: 267.9–538.3 mm).
• Groundwater Abstraction: Abstraction rates increased across all CCAs; for example, in CCA2, Rabi abstraction rose from 504 million m³ in 2018 to 588 million m³ in 2023.
• Storage Depletion: Groundwater Storage Anomalies (GWSA) showed long-term depletion in all areas, with the steepest declines in CCA7 (-0.80 cm/month) and CCA8 (-0.71 cm/month) during the Kharif season, exacerbated by high population density.

Contributions

• Establishes a novel framework linking high-resolution (10 m) satellite-based crop identification directly to groundwater sustainability metrics at the irrigation-unit (CCA) scale.
• Provides a multi-year quantitative assessment of how shifting from traditional crops (like cotton) to water-intensive crops (like sugarcane) accelerates aquifer stress in semi-arid regions.
• Integrates bottom-up water balance modeling with top-down GRACE satellite observations to validate regional groundwater depletion trends.

Funding

• National Key R&D Program of China (Grant No. 2021YFD1900600).

Citation

@article{Khan2025Remote,
  author = {Khan, Aftab Haider and Ejaz, Nuaman and Shang, Songhao and Rahman, Khalil Ur and Tariq, Aqil},
  title = {Remote sensing-based cropping pattern identification and its impact on groundwater use in canal command areas of an irrigated agriculture region in Pakistan},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.110017},
  url = {https://doi.org/10.1016/j.agwat.2025.110017}
}

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