Plant phenology and phenotype prediction using remote sensing data is
increasingly gaining the attention of the plant science community to improve
agricultural productivity. In this work, we generate synthetic forestry images
that satisfy certain phenotypic attributes, viz. canopy greenness. The
greenness index of plants describes a particular vegetation type in a mixed
forest. Our objective is to develop a Generative Adversarial Network (GAN) to
synthesize forestry images conditioned on this continuous attribute, i.e.,
greenness of vegetation, over a specific region of interest. 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.
The synthetic images generated by our method are also used to predict another
phenotypic attribute, viz., redness of plants. The Structural SIMilarity (SSIM)
index is utilized to assess the quality of the synthetic images. The greenness
and redness indices of the generated synthetic images are compared against that
of the original images using Root Mean Squared Error (RMSE) in order to
evaluate their accuracy and integrity. Moreover, the generalizability and
scalability of our proposed GAN model is determined by effectively transforming
it to generate synthetic images for other forest sites and vegetation types.

使用遥感数据预测植物现象和表型的主要研究领域，通过生成满足特定表型属性（如冠层绿度）的合成森林图像，进而发展一种基于生成对抗网络（GAN）的方法来合成与植被绿度相关的森林图像，同时预测红度属性，并评估其准确性和完整性。