Land Surface Modeling and Remote Sensing Laboratory
University of Wisconsin-Madison
We are interested in understanding how climate change and human activities affect terrestrial ecosystems as well as their feedbacks to the human and Earth system. We develop and use a variety tools including land surface models, remote sensing model and data, integrated assessment model and model-data fusion techniques.
Lab news
June 28, 2021
Our proposal "Facilitating Wildfire Insurance Business with Big Data and Machine Learning" (PI: Min Chen; Co-PI: Volker Radeloff) has been selected for funding by the American Family-UW-madison data science initiative program.
Recent wildfires across the western US have caused enormous environmental hazards and economic losses. This project will prototype a machine learning framework, modeled on fires in California, that will improve prediction of wildfire probability and severity at daily, weekly, and monthly scales.
Learn more at https://datascience.wisc.edu/institute/research-amfam/
Feb 25, 2021
Our paper "Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis", led by Dalei Hao, is published at Global Change Biology. This study reveals that leaf physiology, canopy structure, and sun‐sensor geometries all affect the SIF‐GPP relationship. Also it seems a good use of viewing angle (e.g., magical hotspot angle) can help improve SIF-GPP linear relationship.
Check it out at https://doi.org/10.1111/gcb.15554
People
Research Projects
Publications
Products, data & tools
Teaching
