Soil organic carbon (SOC) sequestration is the transfer and storage of
atmospheric carbon dioxide in soils, which plays an important role in climate
change mitigation. SOC concentration can be improved by proper land use, thus
it is beneficial if SOC can be estimated at a regional or global scale. As
multispectral satellite data can provide SOC-related information such as
vegetation and soil properties at a global scale, estimation of SOC through
satellite data has been explored as an alternative to manual soil sampling.
Although existing studies show promising results, they are mainly based on
pixel-based approaches with traditional machine learning methods, and
convolutional neural networks (CNNs) are uncommon. To study the use of CNNs on
SOC remote sensing, here we propose the FNO-DenseNet based on the Fourier
neural operator (FNO). By combining the advantages of the FNO and DenseNet, the
FNO-DenseNet outperformed the FNO in our experiments with hundreds of times
fewer parameters. The FNO-DenseNet also outperformed a pixel-based random
forest by 18% in the mean absolute percentage error.

通过多光谱卫星数据，提出了基于 Fourier 神经算子和 DenseNet 的 FNO-DenseNet 方法，有效估计土壤有机碳 (SOC) 含量，优于传统机器学习方法，并在均方绝对百分比误差方面比基于像素的随机森林高出 18%。

基于傅里叶神经算子和结构相似性的卫星图像土壤有机碳遥感

Image-Based Soil Organic Carbon Remote Sensing from Satellite Images  with Fourier Neural Operator and Structural Similarity

The optimization of Kernel-Target Alignment (TA) has been recently proposed
as a way to reduce the number of hardware resources in quantum classifiers. It
allows to exchange highly expressive and costly circuits to moderate size, task
oriented ones. In this work we propose a simple toy model to study the
optimization landscape of the Kernel-Target Alignment. We find that for
underparameterized circuits the optimization landscape possess either many
local extrema or becomes flat with narrow global extremum. We find the
dependence of the width of the global extremum peak on the amount of data
introduced to the model. The experimental study was performed using
multispectral satellite data, and we targeted the cloud detection task, being
one of the most fundamental and important image analysis tasks in remote
sensing.

本文介绍了基于 Kernel-Target Alignment（TA）的优化方法，能够将高成本的电路替换为中等规模的任务专用电路，旨在减少量子分类器中的硬件资源，并提出了一个简单的玩具模型来研究其优化概况。通过实验证明，对于欠参数化的电路，优化概况具有许多局部极值，或成为一个带有狭窄全局极值的平坦状态，并发现全局极值峰的宽度取决于引入模型的数据量。