<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Jing M. Chen | Precision Agriculture Lab</title><link>https://paglab.org/author/jing-m.-chen/</link><atom:link href="https://paglab.org/author/jing-m.-chen/index.xml" rel="self" type="application/rss+xml"/><description>Jing M. Chen</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 01 Jan 2025 00:00:00 +0000</lastBuildDate><image><url>https://paglab.org/media/logo_hu_32055a858e223df5.png</url><title>Jing M. Chen</title><link>https://paglab.org/author/jing-m.-chen/</link></image><item><title>A more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks</title><link>https://paglab.org/publication/li-2025-rse-more/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://paglab.org/publication/li-2025-rse-more/</guid><description>&lt;p>Dong Li, Jing M. Chen, Gregory Duveiller, Christian Frankenberg, Philipp Köhler, &amp;amp; Kang Yu (2025). A more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks. &lt;em>Remote Sensing of Environment&lt;/em>, 330: 114987.&lt;/p></description></item></channel></rss>