Welcome to Song’s personal academic website!
About me
Yang Song’s interests broadly cover remote sensing, agriculture, and climate change. His current research focuses on applying satellite observations to study terrestrial ecosystems, climate feedbacks, and crop production. He is leading a project on China’s maize responses to recent climate change funded by the National Natural Science Foundation of China (2024.01–2026.12, Grant Number: 32301395).
Seeking for collaboration! It’ s my pleasure to work on your manuscript/project!
Keywords: Remote Sensing, Precision Agriculture, Climate Change, Plant Phenotyping, Global Carbon Cycle, Satellite Solar-Induced Chlorophyll Fluorescence
Featured Research
Song Y, Guo Y, Li S, Li W, Jin X. Elevated CO2 concentrations contribute to a closer relationship between vegetation growth and water availability in the Northern Hemisphere mid-latitudes. Environmental Research Letters. 2024, 19, 084013. DOI: 10.1088/1748-9326/ad5f43 →PDF
Song Y, Penuelas J, Ciais P, Wang S, Zhang Y, Gentine P, McCabe M, Wang L, Li X, Li F, Wang X, Jin Z, Wu C, Jin X. Recent water constraints mediate the dominance of climate and atmospheric CO2 on vegetation growth across China. Earth’s Future. 2024, 10, e2021EF002634. DOI: 10.1029/2023EF004395 →PDF
Song Y, Jiao W, Wang J Wang L. Increased global vegetation productivity despite rising atmospheric dryness over the last two decades. Earth’s Future. 2022, 10, e2021EF002634. DOI: 10.1029/2021EF002634 →PDF
Song Y, Wang L, Wang J. Improved understanding of the spatially-heterogeneous relationship between satellite solar-induced chlorophyll fluorescence and ecosystem productivity. Ecological Indicators. 2021, 129, 107949. DOI: 10.1016/j.ecolind.2021.107949 →PDF
Song Y, Wang J, Wang L. Satellite solar-induced chlorophyll fluorescence reveals heat stress impacts on wheat yield in India. Remote Sensing. 2020, 12(20), 3277. DOI: 10.3390/rs12203277 →PDF
Song Y, Wang J, Yu Q, Huang J. Using MODIS LAI data to monitor spatio-temporal changes of winter wheat phenology in response to climate warming. Remote Sensing, 2020, 12(5), 786. DOI: 10.3390/rs12050786 →PDF
Song Y, Wang J. Mapping winter wheat planting area and monitoring its phenology using Sentinel-1 backscatter time series. Remote Sensing, 2019, 11(4), 449. DOI: 10.3390/rs11040449 →PDF
Song Y, Fang S, Yang Z, Shen S. Drought indices based on MODIS data compared over a maize-growing season in Songliao Plain, China. Journal of Applied Remote Sensing, 2018, 12(4), 046003. DOI: 10.1117/1.JRS.12.046003 →PDF
Nan F†, Song Y†, Yu X, Nie C, Liu Y, Bai Y, Zou D, Wang C, Yin D, Yang W, Jin X. A novel method for maize leaf disease classification using the RGB-D post-segmentation image data. Frontiers in Plant Science. 2023, 14, 1268015. DOI: 10.3389/fpls.2023.1268015 →PDF
Liu Y, Fan K, Meng L, Nie C, Liu Y, Cheng M, Song Y, Jin X. Synergistic use of stay-green traits and UAV multispectral information in improving maize yield estimation with the random forest regression algorithm. Computers and Electronics in Agriculture. 2024, 229, 109724. DOI: 10.1016/j.compag.2024.109724