Wang et al. (2026) Remote-Sensing Estimation of Evapotranspiration for Multiple Land Cover Types Based on an Improved Canopy Conductance Model

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

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-02-05
• Authors: Jianfeng Wang, Xiaozhou Xin, Zhiqiang Ye, Shihao Zhang, Tianci Li, Yu Sun
• DOI: 10.3390/rs18030513

Research Groups

Not available from the provided text.

Short Summary

This study developed an improved canopy conductance model by integrating the K95 and Jarvis frameworks, significantly enhancing large-scale remote-sensing evapotranspiration (ET) retrieval accuracy across diverse land cover types. The model demonstrates strong temporal and spatial stability, outperforming existing models and products.

Objective

• To develop a remote-sensing evapotranspiration (ET) estimation approach suitable for large scales and diverse land cover types, and to propose an improved canopy conductance model for daily latent heat flux (LE) estimation.

Study Configuration

• Spatial Scale: Global, validated across 79 flux sites and 9 independent sites representing diverse land cover types.
• Temporal Scale: Daily and 8-day scales, using observations from 2015–2023.

Methodology and Data

• Models used: An improved canopy conductance model, developed by integrating the canopy radiation transfer concept from the K95 model into the multiplicative Jarvis framework. This model includes limiting effects from photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T), and soil moisture (θ).
• Data sources: Observations from 79 global flux sites, ERA5 reanalysis data, MODIS satellite products, and SMAP satellite products.

Main Results

• The improved model achieved strong temporal stability at the daily scale with a Kling–Gupta efficiency (KGE) of 0.82, a correlation coefficient (R) of 0.82, and a Root Mean Square Error (RMSE) of 27.83 W/m².
• Spatial validation over independent holdout sites yielded a daily KGE of 0.84, R of 0.84, and RMSE of 22.53 W/m².
• At the 8-day scale over holdout sites, the model achieved a KGE of 0.87, R of 0.88, and RMSE of 18.74 W/m².
• Compared to the K95 and Jarvis models, the KGE increased by approximately 34% and 15%, respectively, while RMSE decreased by approximately 38% and 12%, respectively.
• Relative to the MOD16 and PML-V2 products, KGE increased by approximately 32% and 16%, respectively, while RMSE decreased by approximately 33% and 17%, respectively.

Contributions

• Developed a novel canopy conductance model that explicitly couples canopy structure with multiple environmental constraints within the Jarvis framework.
• Implemented a structure optimization approach across various land cover types, leading to marked improvements in large-scale remote-sensing ET retrieval accuracy.
• Demonstrated enhanced physical consistency and physiological rationality compared to existing models and products.
• Provided an effective pathway and parameterization scheme for producing ET products applicable across diverse ecosystems.

Funding

Not available from the provided text.

Citation

@article{Wang2026RemoteSensing,
  author = {Wang, Jianfeng and Xin, Xiaozhou and Ye, Zhiqiang and Zhang, Shihao and Li, Tianci and Sun, Yu},
  title = {Remote-Sensing Estimation of Evapotranspiration for Multiple Land Cover Types Based on an Improved Canopy Conductance Model},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18030513},
  url = {https://doi.org/10.3390/rs18030513}
}