Field Crops Research 271 (2021) 108250 Available online 28 July 2021 0378-4290/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Grass modelling in data-limited areas by incorporating MODIS data products Xiao Huang a, b , Gang Zhao c , Conrad Zorn d , Fulu Tao e , Shaoqiang Ni f , Wenyuan Zhang g , Tongbi Tu h, *, Mats H¨ oglind b a State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China b Norwegian Institute of Bioeconomy Research, Klepp Station, Norway c Department of Global Ecology, Carnegie Institution for Science, Stanford, USA d Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand e Natural Resources Institute Finland (Luke), Helsinki, Finland f Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China g Department of Zoology, University of Oxford, Oxford, UK h Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China A R T I C L E INFO Keywords: Process-based grass models Data-limited areas MODIS data products Bayesian calibration Ensemble Kalman flter BASGRA ABSTRACT Process-based grass models (PBGMs) are widely used for predicting grass growth under potential climate change and different management practices. However, accurate predictions using PBGMs heavily rely on feld obser- vations for data assimilation. In data-limited areas, performing robust and reliable estimates of grass growth remains a challenge. In this paper, we incorporated satellite-based MODIS data products, including leaf area index, gross primary production and evapotranspiration, as an additional supplement to feld observations. Popular data assimilation methods, including Bayesian calibration and the updating method ensemble Kalman flter, were applied to assimilate satellite derived information into the BASic GRAssland model (BASGRA). A range of different combinations of data assimilating methods and data availability were tested across four grassland sites in Norway, Finland and Canada to assess the corresponding accuracy and make recommendations regarding suitable approaches to incorporate MODIS data. The results demonstrated that optimizing the model parameters that are specifc for grass species and cultivar should be targeted prior to updating model state variables. The MODIS derived data products were capable of constraining model’s simulations on phenological development and biomass accumulation by parameter optimization with its performance exceeding model outputs driven by default parameters. By integrating even a small number of feld measurements into the parameter calibration, the model’s predictive accuracy was further improved - especially at sites with obvious biases in the input MODIS data. Overall, this comparative study has provided fexible solutions with the potential to strengthen the capacity of PBGMs for grass growth estimation in practical applications. 1. Introduction Grassland is one of the largest ecosystems in the world (Suttie et al., 2005), occupying up to 40 % of the total terrestrial surface (Blair et al., 2014). In the high-latitude areas, grass-based forages are important sources for dairy and meat production, providing necessary nutrients (e. g. protein, fbre) to livestock (Dengler et al., 2020). However, the biomass productivity of grasslands within this region is instable under climate change (Wir´ ehn, 2018). The low temperatures in winter (Cohen et al., 2012), as well as drought hazards during the growing season (Bakke et al., 2020), can signifcantly affect the survival and growth of perennial grasses (H¨ oglind et al., 2010; Østrem et al., 2015). Ultimately, this can lead to substantial inter-annual variation in grass yields (Rende, 2019) and it is therefore of great importance to accurately estimate the grass growth dynamics to improve food security and adaptation to future climates (H¨ oglind et al., 2013). * Corresponding author. E-mail addresses: damon19910125@gmail.com (X. Huang), gzhao@carnegiescience.edu (G. Zhao), conrad.zorn@auckland.ac.nz (C. Zorn), fulu.tao@luke.f (F. Tao), nsq15@mails.tsinghua.edu.cn (S. Ni), wenyuan.zhang@zoo.ox.ac.uk (W. Zhang), tutb@mail.sysu.edu.cn (T. Tu), mats.hoglind@nibio.no (M. H¨ oglind). Contents lists available at ScienceDirect Field Crops Research journal homepage: www.elsevier.com/locate/fcr https://doi.org/10.1016/j.fcr.2021.108250 Received 31 March 2021; Received in revised form 20 June 2021; Accepted 22 July 2021