Update intro.md

_pages/includes/intro.md

@@ -8,20 +8,4 @@ I am now a master's student in the <a href='http://english.geo.bnu.edu.cn/'>Facu
 - 🛰️**Rich experience in remote sensing**: Proficient in processing and analyzing satellite data, by programming (Python, Matlab), cloud computing platform (Google Earth Engine), and using professional software (ENVI, ArcGIS, SNAP).
 - 📑**Scientific research training**: Completed one first-author paper, accepted by *Remote Sensing of Environment* ((a flagship journal of remote sensing with a 13.5 impact factor).
 - 🌏**Solid Geography background**: Explored various branches of geography with high GPAs, ranging from physical geography (e.g., ecology, climatology, hydrology) to human geography (e.g., economic geography, urban geography, social and culture geography).
-- 🚀**Active and fast learner**: Constantly stepping out of my comfort zone with rich experience in phenology, machine learning, and deep learning.
-
-<h2 style="margin: 30px 0px 10px;">Publication</h2>
-
-<div class='paper-box'><div class='paper-box-image'><div><div class="badge">RSE</div><img src='images/flowchart.tif' alt="sym" width="100%"></div></div>
-<div class='paper-box-text' markdown="1">
-
-[Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes] <br>
-**Gu, Z.**, Chen, J., Chen, Y., Qiu, Y., Zhu, X., & Chen. X <br>
-
-Accepted by *Remote Sensing of Environment* on October 19, 2023
-
-- Synthesizing remote sensing images with high spatial and temporal resolution, especially for agricultural regions
-- **Background**: Images of key phenological periods play a vital role in agricultural applications as they capture the unique spectral characteristics of crops. Unfortunately, acquiring high-spatial-resolution images of the key phenological period from a single satellite platform remains challenging due to its short duration and synchronization with the rainy season. Spatiotemporal fusion (STF) is an effective tool for the prediction of missing high spatial resolution images on the required date. However, most STF methods assume a uniform relationship between the reflectance on the base date and the predicted date for the same land-cover type, which fails to hold in agricultural scenarios because of the diverse phenological changes among different crop types or different growth processes, even within a single crop type.
-- **Methodology**: We developed an innovative algorithm, Agri-Fuse, that emphasizes change information. Using category-based regression with the constraint of the unmixing model, the phenological change relationship of each type of change was solved.
-</div>
-</div
+- 🚀**Active and fast learner**: Constantly stepping out of my comfort zone with rich experience in phenology, machine learning, and deep learning. test