Yanqun et al. (2026) Harnessing satellite-driven insights for dynamic soil moisture tracking in smart farming systems

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-02-10
• Authors: Liu Yanqun, Imran Ahmad
• DOI: 10.1016/j.ejrh.2026.103230

Research Groups

• Shaoguan Meteorological Bureau, Shaoguan, China
• Department of Water Resources and Irrigation Engineering, Woldia University, Weldia Town, Ethiopia

Short Summary

This study developed and validated an integrated framework using Landsat 8 satellite data and IoT-enabled ground sensors to dynamically track soil moisture variability in the Angreb Watershed, Ethiopia, demonstrating its high accuracy and applicability for precision irrigation and smart farming.

Objective

• To extract and analyze satellite-derived spectral, thermal, and vegetation indices for soil moisture assessment.
• To estimate Land Surface Temperature (LST) and Land Surface Emissivity (LSE) from Landsat 8 data.
• To validate the correlation between the Soil Moisture Index (SMI) and LST, and to ground-truth satellite-derived SMI using IoT-enabled soil moisture sensors.

Study Configuration

• Spatial Scale: Angreb Watershed in northwestern Ethiopia, a semi-arid agroecosystem characterized by diverse topography, bimodal rainfall, and mixed land use (rainfed agriculture, irrigated plots, grazing lands, natural vegetation). Satellite data resolution is 30 meters.
• Temporal Scale: Seasonal analysis across three phases (pre-irrigation, mid-season, post-harvest) to capture dynamic shifts in moisture availability, aligning with the bimodal rainfall pattern (main rainy season June-September).

Methodology and Data

• Models used:
  • Normalized Difference Vegetation Index (NDVI)
  • Proportion of Vegetation (Pv)
  • Brightness Temperature (BT) derived using the inverse Planck function
  • Land Surface Emissivity (LSE) model (ε = 0.004Pv + 0.986)
  • Land Surface Temperature (LST) corrected for emissivity
  • Soil Moisture Index (SMI) combining LST and NDVI
  • Pearson correlation analysis for SMI-LST relationship and ground validation
• Data sources:
  • Satellite: Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (Bands 4, 5, 10) for cloud-free scenes.
  • Observation: 20 IoT-enabled soil moisture sensors deployed at 5 cm, 15 cm, and 30 cm depths, transmitting volumetric water content data via LoRaWAN. Field infiltration tests.

Main Results

• High-resolution SMI maps revealed strong spatial heterogeneity, with vegetated areas showing cooler temperatures and higher moisture, while degraded zones exhibited thermal intensification and dryness.
• A robust inverse correlation was found between Land Surface Temperature (LST) and the Soil Moisture Index (SMI) (Pearson r = –0.84), confirming the reliability of the vegetation–temperature framework.
• Ground validation with 20 IoT sensors demonstrated high accuracy for satellite-derived SMI: Pearson r = 0.86, Root Mean Square Error (RMSE) = ±3.1 %, and Mean Absolute Error (MAE) = ±2.4 %.
• Temporal analysis showed dynamic shifts in SMI values: uniformly low (<0.3) during pre-irrigation, a sharp increase (>0.7) in irrigated zones mid-season, and moisture depletion post-harvest.
• Infiltration rates correlated with SMI: high SMI zones exhibited rates greater than 4 mm/h, while low SMI zones showed rates less than 1.5 mm/h.

Contributions

• Developed and validated an integrated satellite-IoT framework for dynamic soil moisture tracking, offering a reproducible and scalable alternative to traditional methods.
• Provided high-resolution insights into moisture variability, advancing operational drought monitoring and supporting precision irrigation scheduling.
• Enhanced resilience in semi-arid farming landscapes by enabling site-specific water application and early warning of moisture stress.
• Demonstrated the transformative potential of satellite-IoT synergy for sustainable water resource management and agricultural planning, particularly in resource-constrained settings.

Funding

• Science and Technology Research Project of Guangdong Meteorological Service (GRMC2023M45)
• Shaoguan Science and Technology Bureau Project (230616148033947)
• Guangdong Basic and Applied Basic Research Foundation - Meteorology Joint Fund (2024A1515510034)

Citation

@article{Yanqun2026Harnessing,
  author = {Yanqun, Liu and Ahmad, Imran},
  title = {Harnessing satellite-driven insights for dynamic soil moisture tracking in smart farming systems},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103230},
  url = {https://doi.org/10.1016/j.ejrh.2026.103230}
}