Abstract
Simulating soil reflectance spectra is invaluable for soil-plant radiative modeling and training machine learning models, yet it is difficult as the intricate relationships between soil structure and its constituents. To address this, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra based on soil property inputs was developed. The model is trained on an extensive dataset comprising nearly 180,000 soil spectra-property pairs from 17 datasets. It generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format. The generative model can simulate output spectra based on an incomplete set of input properties. SOGM is based on the denoising diffusion probabilistic model (DDPM). Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the full visible-near-infrared range (VIS-NIR; 400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. The SOGM was up-scaled by coupling with the Helios 3D plant modeling software, which allowed for generation of synthetic aerial images of simulated soil and plant scenes. It can also be easily integrated with soil-plant radiation model used for remote sensin research like PROSAIL. The testing results of the SOGM on new datasets that not included in model training proved that the model can generate reasonable soil reflectance spectra based on available property inputs. The presented models are openly accessible on: this https URL.
Abstract (translated)
模拟土壤反射光谱对于土壤-植物辐射建模和训练机器学习模型非常有价值，然而，它具有挑战性，因为土壤结构和其组成之间的复杂关系。为了解决这个问题，基于土壤属性输入的完全数据驱动土壤光学生成模型（SOGM）用于模拟土壤反射光谱的开发。该模型在包括17个数据集的近180,000个土壤光谱-属性对的数据集上进行训练。它从基于文本的输入描述土壤属性和其值生成土壤反射光谱，而不是仅以二进制向量格式表示数值和标签。生成模型可以根据一组输入属性模拟输出光谱。SOGM基于去噪扩散概率模型（DDPM）。还开发了两个补充模型来补充SOGM：一个填充光谱长度的模型，可以填补光谱较短于完整可见-近红外范围（VIS-NIR；400至2499纳米）的缺口，和一个干土光谱模型，可以根据SOGM预测的干土光谱估计水分含量对土壤反射光谱的影响。通过与Helios 3D植物建模软件耦合，SOGM进行了放大，从而能够生成模拟土壤和植物场景的合成高空图像。它还可以轻松地与用于远地感研究的光谱遥感模型如PROSAIL集成。对SOGM在新数据集上的测试结果表明，基于现有属性输入，该模型可以生成合理的土壤反射光谱。所提出的模型在：https://这个URL公开可用。
URL
https://arxiv.org/abs/2405.01060
