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

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Qing Li , Yanan Wei , Dalei Hao , Weijian Yu , Yelu Zeng · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1016/j.aiia.2025.10.018

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. 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.

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article

Language

en

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https://doi.org/10.1016/j.aiia.2025.10.018

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gold

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59

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https://openalex.org/W4415901172

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https://openalex.org/W4415901172

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DOI

https://doi.org/10.1016/j.aiia.2025.10.018

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Title

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

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article

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en

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2025

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2025-11-05

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Qing Li, Yanan Wei, Dalei Hao, Weijian Yu, Yelu Zeng

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https://doi.org/10.1016/j.aiia.2025.10.018

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Yes

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gold

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https://doi.org/10.1016/j.aiia.2025.10.018

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0

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59

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