Tanveer et al. (2026) Physics-Informed Earth Observation for High-Resolution Crop Evapotranspiration Mapping and Sustainable Agricultural Water Management

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Land
• Year: 2026
• Date: 2026-09-28
• Authors: Umer Tanveer, Kiran Falak Sher, Ahmed Khan, Abdu Salam, Jamal Ahmed, Farhan Amin, Gyu Sang Choi, Isabel de la Torre, Lázaro Javier Hernández Rodríguez, Pablo Herrero García
• DOI: 10.3390/land15101822

Research Groups

• Department of Land, Air and Water Resources, University of California, Davis
• NASA Jet Propulsion Laboratory

Short Summary

This study presents AquaVolt-AI, a physics-informed machine learning framework for estimating crop evapotranspiration (ETc) using satellite data and meteorological information. The framework achieves high accuracy in ETc estimation without requiring dedicated on-site sensing infrastructure.

Objective

• Develop a scalable and low-infrastructure computational framework for spatially resolved ETc monitoring

Study Configuration

• Spatial Scale: 16 × 16 virtual sensing grid comprising 256 spatial sectors at 10 m resolution
• Temporal Scale: 36 days (28 June–3 August 2026)

Methodology and Data

• Models used: AquaVolt-AI physics-informed machine learning framework, FAO-56 dual crop-coefficient formulation
• Data sources: Sentinel-2 optical imagery, NASA ECOSTRESS thermal observations, meteorological data

Main Results

• Root mean square error of 0.3000 mm day−1 and a mean absolute error of 0.2688 mm day−1 in ETc estimation
• Continuous ETc predictions maintained during a consecutive 9-day satellite data gap using physics-informed state estimator

Contributions

• Development of a low-infrastructure computational framework for spatially resolved ETc monitoring, eliminating the need for dedicated on-site sensing hardware
• Integration of Earth observation, meteorological information, and physics-informed machine learning within a serverless architecture

Funding

• NASA ECOSTRESS project (reference code: NNX17AQ93G)
• University of California, Davis Research Grants Program

Citation

@article{Tanveer2026PhysicsInformed,
  author = {Tanveer, Umer and Sher, Kiran Falak and Khan, Ahmed and Salam, Abdu and Ahmed, Jamal and Amin, Farhan and Choi, Gyu Sang and Torre, Isabel de la and Rodríguez, Lázaro Javier Hernández and García, Pablo Herrero},
  title = {Physics-Informed Earth Observation for High-Resolution Crop Evapotranspiration Mapping and Sustainable Agricultural Water Management},
  journal = {Land},
  year = {2026},
  doi = {10.3390/land15101822},
  url = {https://doi.org/10.3390/land15101822}
}