Plant phenology and phenotype prediction using remote sensing data are increasingly gaining attention within the plant science community as a promising approach to enhance agricultural productivity. This work focuses on generating synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. We harness a Generative Adversarial Network (GAN) to synthesize biologically plausible and phenotypically stable forestry images conditioned on the greenness of vegetation (a continuous attribute) over a specific region of interest, describing a particular vegetation type in a mixed forest. The training data is based on the automated digital camera imagery provided by the National Ecological Observatory Network (NEON) and processed by the PhenoCam Network. Our method helps render the appearance of forest sites specific to a greenness value. The synthetic images are subsequently utilized to predict another phenotypic attribute, viz., redness of plants. The quality of the synthetic images is assessed using the Structural SIMilarity (SSIM) index and Fr\'echet Inception Distance (FID). Further, the greenness and redness indices of the synthetic images are compared against those of the original images using Root Mean Squared Percentage Error (RMSPE) to evaluate their accuracy and integrity. The generalizability and scalability of our proposed GAN model are established by effectively transforming it to generate synthetic images for other forest sites and vegetation types. From a broader perspective, this approach could be leveraged to visualize forestry based on different phenotypic attributes in the context of various environmental parameters.

翻译：利用遥感数据进行植物物候与表型预测，作为一种提升农业生产力的有效途径，正日益受到植物科学界的关注。本研究聚焦于生成满足特定表型属性（即冠层绿度）的合成林业图像。我们利用生成对抗网络（GAN）合成具有生物学合理性且表型稳定的林业图像，这些图像以特定感兴趣区域（描述混合森林中特定植被类型）的植被绿度（一种连续属性）为条件。训练数据基于美国国家生态观测网络（NEON）提供的自动化数码相机影像，并由PhenoCam网络处理。我们的方法有助于渲染对应于特定绿度值的森林站点外观。合成图像随后被用于预测另一表型属性，即植物红度。合成图像的质量通过结构相似性（SSIM）指数和弗雷歇起始距离（FID）进行评估。此外，使用均方根百分比误差（RMSPE）将合成图像的绿度与红度指数与原始图像进行对比，以评估其准确性与完整性。通过有效转换模型以生成其他森林站点和植被类型的合成图像，我们验证了所提GAN模型的泛化能力与可扩展性。从更广阔的视角看，该方法可被用于基于不同表型属性，在各种环境参数背景下实现林业可视化。