Remote Sensing of Environment serves the remote sensing community with the publication of scientific and technical results on theory, experiments, and applications of remote sensing of Earth resources and environment. Thoroughly interdisciplinary, this journal contains new sections which focus on terrestrial, atmospheric, and oceanic sensing. The journal seeks papers from science and application areas utilizing remote sensing as a tool, as well as papers dealing with remote sensing technology developments in such areas as: - agricultural and soil science - biophysical-spectral models - ecology and environmental science - forestry and range - geography and land information systems - geology and mineral resources - hydrology and water resources - image processing and analysis - meteorology and atmospheric science - oceanography and marine science - sensor systems and spectral measurements In addition to original research papers, comprehensive state-of-the-art review articles and summaries of previously piecewise published works are welcome. Tutorial papers of interest to educators and those engaged in remote sensing training are acceptable if sufficiently broad in their coverage. Brief papers containing little or no analysis but presenting new data are published as Short Communications.

Remote Sensing of Environment

Discriminative feature constraints via supervised contrastive learning for few-shot forest tree species classification using airborne hyperspectral images

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Elsevier BV

In other words, there is a need to extract richer textural features to enhance crop canopy structure information extraction. Many studies highlight the significant potential of deep learning in extracting deep features from images [34]. Therefore, extracting deep features (DF) from texture images using deep learning models has emerged as a viable approach to enhance the estimation of crop parameters. 

Yifan Xie's scientific contributions

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