Recently, the paper "Upscaling in situ site-based albedo using machine learning models: main controlling factors on results" by Wang Jingping, a master student of Wu Xiaodan's research group, has been published in IEEE Transactions on Geoscience and Remote, a well-known journal in the field of remote sensing. This paper is another important research result of Wu Xiaodan's team in remote sensing scale conversion. In the context of the application of machine learning methods to remote sensing data scale conversion, the problem of upscaling based on machine learning is deeply studied, the uncertainty factors in the upscaling process based on machine learning are analyzed in detail, and the accuracy of the upscaling results is pointed out. It depends on the choice of machine learning model, the inclusion of key variables, the source and accuracy of variable data sets, the number and representativeness of training samples, and the sensitivity of the model to these factors. At the same time, the research shows that the upscaling method based on machine learning has a certain cross-scale and cross-regional applicability, which improves the current situation that the authenticity test is mainly based on ground test field observations, and has sufficient time length, consistency, and space for generation. The potential of a continuous reference data set has the potential to be used to meet extensive validation requirements. This research was funded by the China High-resolution Earth Observation System Major Project, the National Natural Science Foundation of China's general project, and the central university's basic scientific research business funding project.
https://ieeexplore.ieee.org/document/9497519
In addition, the paper "Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019" by Ma Dujuan, a master student in Wu Xiaodan's research group, was published in Remote Sensing, a well-known journal in the field of remote sensing. This article analyzes the spatiotemporal characteristics of GPP in the Arctic region and its changes with latitude, elevation, and vegetation types. In the context of continuous global climate changes, this article provides a certain reference for understanding the Arctic region and even the global carbon cycle. The research is jointly funded by the National Key Research and Development Program, the National Natural Science Foundation of China, and the special funds for basic scientific research operations of central universities.
https://www.mdpi.com/2072-4292/13/15/2875
