Behura et al. (2025) Evaluation of Actual Evapotranspiration from Rice Fields of Odisha Using Remote Sensing Based Surface Energy Balance Approach

Identification

• Journal: Journal of the Indian Society of Remote Sensing
• Year: 2025
• Date: 2025-11-05
• Authors: Kiran Bala Behura, Sanjay Kumar Raul, Jagadish Chandra Paul, Sheelabhadra Mohanty, Prachi Pratyasha Jena, Sanat Kumar Dwibedi
• DOI: 10.1007/s12524-025-02345-2

Research Groups

• Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Odisha University of Agriculture and Technology, Bhubaneswar, India
• ICAR-Indian Institute of Water Management, Bhubaneswar, India
• Department of Agronomy, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, India

Short Summary

This research utilized the Surface Energy Balance Algorithm for Land (SEBAL) to evaluate actual evapotranspiration (AET) from rice fields in Odisha, India, integrating remote sensing and meteorological data. The study successfully quantified spatial and temporal AET variations across four crop growth stages, demonstrating SEBAL's strong performance and utility for efficient water resource management in rice-based agricultural systems.

Objective

• To evaluate actual evapotranspiration (AET) from rice fields in Odisha, India, across four critical crop growth stages using the SEBAL model.
• To integrate remote sensing and meteorological data to enhance the accuracy of evapotranspiration estimates for improved water resource management and irrigation planning.

Study Configuration

• Spatial Scale: State of Odisha, India (17.78°N to 22.73°N latitudes and 81.37°E to 87.53°E longitudes). Landsat 8 OLI and TIRS imagery provided 30 m and 100 m spatial resolution, respectively. NASA POWER data had a 0.5° × 0.625° spatial resolution.
• Temporal Scale: Four critical rice crop growth stages: initial (January 10–16, 2023), development (February 9–16, 2023), mid-season (March 18–24, 2023), and late season (April 27- May 8, 2023).

Methodology and Data

• Models used: Surface Energy Balance Algorithm for Land (SEBAL). Reference evapotranspiration (ET) was estimated using REF-ET Software (Allen, 2002).
• Data sources:
  • Satellite: Cloud-free Landsat 8 OLI and TIRS imagery (30 m and 100 m spatial resolution).
  • Meteorological: NASA POWER data (daily estimates of precipitation, solar radiation, maximum and minimum temperature, wind speed, and humidity at 0.5° × 0.625° resolution); ground-based automatic weather station data from the Indian Institute of Water Management (IIWM) Research Farm, Deras, Mendhasal, Odisha, and the Department of Agrometeorology, Odisha University of Agriculture and Technology (OUAT), Bhubaneswar.
  • Ancillary: Digital Elevation Model (DEM), rice masks from the National Remote Sensing Centre (NRSC), Hyderabad.
  • Software: Geographic Information System (GIS) for integration and analysis, ERDAS for SEBAL algorithm implementation.

Main Results

• Actual evapotranspiration (AET) exhibited significant spatial and temporal variations, peaking during the mid-season stage (3.5–6.2 mm/day), aligning with the crop's maximum water demand and vegetative activity.
• AET was lowest during the initial (0.5–2.3 mm/day) and late-season (1.8–4.2 mm/day) stages, corresponding to minimal canopy cover and crop maturity, respectively.
• Net radiation (Rn) and latent heat flux (λET) were identified as primary drivers of AET, with R_n values reaching 667–729 W/m² during mid-season.
• Sensible heat flux (H) showed an inverse relationship with AET, being lowest (87–165 W/m²) during mid-season and increasing in the initial and late stages.
• Spatial analysis revealed higher AET in central and eastern coastal zones, linked to denser canopy cover and favorable microclimatic conditions.
• Model validation demonstrated strong performance with FAO reference crop coefficients (Kc): Coefficient of Determination (R²) = 0.79, Root Mean Square Error (RMSE) = 0.965 (dimensionless), Mean Absolute Error (MAE) = 0.27 (dimensionless), and Kling-Gupta Efficiency (KGE) = 0.733, indicating reliable and consistent estimation of crop coefficients.
• Localized cloud interference (12–18% partial cloud cover) during mid-season led to an underestimation of net radiation (30–60 W/m²) and AET (0.4–0.7 mm/day) in affected pixels, particularly in coastal districts.

Contributions

• This study provides the first comprehensive evaluation of AET from rice fields across four growth stages in Odisha, India, using the SEBAL model, integrating remote sensing with meteorological data.
• It demonstrates the robustness and operational applicability of the SEBAL model for spatially distributed AET estimation in subtropical rice-growing regions, offering a cost-effective and scalable alternative to traditional ground-based methods.
• The research generated high-resolution (30 m) spatial and temporal AET maps, which are crucial for identifying heterogeneity in crop water use and informing site-specific irrigation interventions.
• It provides reliable crop coefficients (Kc) for rice in the region, enhancing the accuracy of crop water-use simulation models and supporting informed water management decisions for policymakers and farmers.
• The findings highlight the potential for developing real-time irrigation recommendation systems and contributing to sustainable water governance in agricultural areas facing water scarcity.

Funding

Not Applicable.

Citation

@article{Behura2025Evaluation,
  author = {Behura, Kiran Bala and Raul, Sanjay Kumar and Paul, Jagadish Chandra and Mohanty, Sheelabhadra and Jena, Prachi Pratyasha and Dwibedi, Sanat Kumar},
  title = {Evaluation of Actual Evapotranspiration from Rice Fields of Odisha Using Remote Sensing Based Surface Energy Balance Approach},
  journal = {Journal of the Indian Society of Remote Sensing},
  year = {2025},
  doi = {10.1007/s12524-025-02345-2},
  url = {https://doi.org/10.1007/s12524-025-02345-2}
}