LATEST NEWS

SpectralX: Parameter-efficient domain generalization for spectral Remote Sensing Foundation Models Congratulations to Yuxiang Zhang, Prof. Shunlin Liang, and the research team on publishing a new article in the ISPRS Journal of Photogrammetry and Remote Sensing (Vol. 239, pp. 774-792). The article is titled “SpectralX: Parameter-efficient domain generalization for spectral Remote Sensing Foundation Models” and is available at https://doi.org/10.1016/j.isprsjprs.2026.06.008.

Fine-grained hierarchical crop type classification from integrated hyperspectral EnMAP data and multispectral Sentinel-2 time series: A large-scale dataset and dual-stream transformer method Congratulations to Dr. Wenyuan Li, Professor Shunlin Liang, and the research team on publishing a new article in Remote Sensing of Environment (Vol. 344, 115525). The paper is titled “Fine-grained hierarchical crop type classification from integrated hyperspectral EnMAP data and multispec

The JC STEM Lab of Quantitative Remote Sensing is proud to announce the recent publication of the Handbook of Satellite Land Products, co-edited by Lab Director Prof. Shunlin Liang and Prof. Han Ma. This monumental academic reference work provides a systematic, global guide to the satellite-derived information products essential for observing, understanding, and managing the Earth system. The book, published by Elsevier, is available at https://shop.elsevier.com/books/handboo

Mitigating Topographic Effects in Optical Remote Sensing: From surface reflectance to high-level products Congratulations to Dr. Yichuan Ma, Professor Shunlin Liang, and the research team for their recent publication in the IEEE Geoscience and Remote Sensing Magazine (Early Access, 2026). The paper is available via DOI: 10.1109/MGRS.2026.3690171. Mountainous terrains cover approximately 24% of the Earth's land surface, serving as vital hotspots for biodiversity and providing

An integrated atmospheric-topographic correction framework for land surface reflectance estimation using a spatial-spectral Attention U-Net model Congratulations to Dr. Yichuan Ma, Professor Shunlin Liang, and the research team for their recent publication in Remote Sensing of Environment (Vol. 334, 115188). The paper, titled "An integrated atmospheric-topographic correction framework for land surface reflectance estimation using a spatial-spectral Attention U-Net model," is

AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping Congratulations to Dr. Wenyuan Li, Professor Shunlin Liang, and the research team for their recent publication in Remote Sensing of Environment (Vol. 334, 115234). The paper, titled "AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping," is available via https://doi.org/10.1016/j.rse.2026.115234 . The codes and models are freely accessible at https://gla

Generating an annual 30 m rice cover product for monsoon Asia (2018–2023) using harmonized Landsat and Sentinel-2 data and the NASA-IBM geospatial foundation model Congratulations to Husheng Fang, Professor Shunlin Liang, and the research team for their recent publication in Remote Sensing of Environment (Vol. 335, 115256). The paper, titled "Generating an annual 30 m rice cover product for monsoon Asia (2018–2023) using harmonized Landsat and Sentinel-2 data and the NASA-IB