Transfer learning-based soybean LAI estimations by integrating PROSAIL, UAV, and PlanetScope imagery

Journal Article · Sun Nov 02 00:00:00 EDT 2025 · Artificial Intelligence in Agriculture

DOI:https://doi.org/10.1016/j.aiia.2025.10.018· OSTI ID:3005877

Li, Qing ^[1]; Wei, Yanan ^[1]; Hao, Dalei ^[2]; Yu, Weijian ^[1]; Zeng, Yelu ^[1]

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1. China Agricultural University, Beijing (China); Ministry of Agriculture and Rural Affairs, Beijing (China)
2. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Accurate Leaf Area Index (LAI) estimations at the soybean plot scale is achievable using high-resolution Unmanned Aerial Vehicle (UAV) imagery and field measurement samples. However, the limited coverage of UAV flights restricts large-scale remote sensing monitoring in expansive soybean fields. This study leverages the broad coverage and 3-m resolution of PlanetScope satellite imagery to extend LAI prediction from UAV to satellite scales through transfer learning, using UAV-scale LAI estimates as a benchmark to validate cross-scale consistency. To address this challenge, this study proposed the LAI-TransNet, a two-stage transfer learning framework designed for precise and scalable soybean LAI prediction across large areas, demonstrating its effectiveness in cross-scale monitoring. In Stage 1, a UAV-scale benchmark is established using PROSAIL-simulated UAV reflectance data (UAV-Sim) and field-measured soybean LAI. Traditional machine learning, deep learning, and transfer learning models are trained on a hybrid UAV-Sim and field-measured dataset (UAV-Sim_Measured), with the transfer learning model CNN-TL, fine-tuned using pre-trained weights derived from UAV-Sim, achieving the highest accuracy (R² = 0.81, RMSE = 0.64 m²/m², rRMSE = 11.5 %). In Stage 2, LAI-TransNet is developed by fine-tuning the CNN-TL model on PlanetScope simulated data (PS-Sim), preprocessed via cross-domain mapping to align UAV and satellite spectral features. Real PlanetScope imagery is corrected for reflectance consistency with reference to UAV imagery spectral profiles. LAI-TransNet outperforms other deep learning models trained directly on PS-Sim (R² = 0.69 vs. 0.60–0.63), ensuring robust cross-scale consistency. In conclusion, by bridging UAV and satellite scales, LAI-TransNet enables large-scale soybean LAI monitoring, enhancing precision agriculture management through improved monitoring with the PlanetScope imagery.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: Chinese Universities Scientific Fund; National Natural Science Foundation of China; State Key Laboratory of Efficient Utilization of Agricultural Water Resources; USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 3005877
• Report Number(s): PNNL-SA--211811
• Journal Information: Artificial Intelligence in Agriculture, Journal Name: Artificial Intelligence in Agriculture Journal Issue: 1 Vol. 16; ISSN 2589-7217
• Publisher: Elsevier BV
• Country of Publication: United States
• Language: English

Related Subjects

Leaf area index (LAI)
PROSAIL model
Remote sensing
Transfer learning