Remote Sensing Modeling of Ecosystem Productivity and Evapotranspiration: New Insights from VENµS

Jiquan Chen ¹ Pietro Sciusco ¹ Dawei Xu ² Michael Abraha ¹ Changliang Shao ² Cheyenne Lei ¹ Gabriela Shirkey ¹ Arnon Karnieli ⁵ Ranjeet John ⁴ Gang Dong ³ David Reed ¹ Fei Li ¹ Geoffrey Henebry ¹ Xu Wang ² Kyla Dahlin ¹ Xiaoping Xin ²

¹CGCEO/Geography, Michigan State University, East Lansing, MICHIGAN, USA
²National Hulunber Grassland Ecosystem Observation and Research Station, Chinese Academy of Agricultural Sciences, Beijing, Beijing, China
³School of Life Science, Shanxi University, Taiyuan, Shanxi, China
⁴Department of Biology, University of South Dakota, Vermillion, SD, USA
⁵Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev Sede-Boker Campus, Negev Sede-Boker, Negev Sede-Boker , Israel

VENµS provides surface reflectance images with high spatial resolution, rapid repeat cycle, two red-edge and one water vapor bands. Comparing VENµS spectral reflectance data from two sites in contrasting environments in southwestern Michigan (USA) and northeastern Inner Mongolia (PRC), we evaluate direct measurements of land surface properties (e.g., LAI, energy flux), ecosystem production, and evapotranspiration from intensive experiments and long-term monitoring stations, and explored their connections with VENµS spectral reflectance and derived vegetation indices to identify new insights into ecosystem functions from VENµS. At the VENµS site in Inner Mongolia (HGERS), five flux towers and a suite of manipulative experiments have been maintained by the CAAS team; whereas in Michigan (KALAM2-2), eight flux towers and large experimental plots of LTER and GLBRC at the Kellogg Biological Station provide rich ground data for validating model predictions. Land cover maps and the affiliated remote sensing metrics (e.g., NDVI, EVI) derived from VENµS (10 m), Landsat (30 m), Sentinel-2 (10 m) and MODIS imagery for 2019 enable downscaling of ecosystem functions to the corresponding resolution of the images. An expected advantage of VENµS is its capability in delineating smaller patches (e.g., 10 m), including many experimental plots. The corresponding predictions, including landscape patch mosaics, will likely enhance model predictions when scaling up to the landscape. Daily net ecosystem production and evapotranspiration derived from the eddy-covariance flux towers and sampling chambers will be used to seek new insights from VENµS data through comparisons with those from Landsat, Sentinel, and MODIS data.