AUTHOR=Rahman Azbina , Zhang Xinxuan , Houser Paul , Sauer Timothy , Maggioni Viviana TITLE=Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model JOURNAL=Frontiers in Water VOLUME=3 YEAR=2022 URL=https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2021.789352 DOI=10.3389/frwa.2021.789352 ISSN=2624-9375 ABSTRACT=

As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.