Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model

Grassland aboveground biomass is crucial for evaluating grassland desertification, degradation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, especially at the regional scale, this study investigates a new method combining remote sensing data assimilation technology and a grassland process-based model to estimate regional grassland biomass, focusing on improving the simulation accuracy by modeling and revealing the mechanism interpretability of grassland growth processes. Xilinhot City of Inner Mongolia was used as the study area. The ModVege model was selected as the grass dynamic simulation model. A likelihood function was constructed composed of the LAI, grassland aboveground biomass, and daily measurements wherein the accumulated temperature reached ST2 (the temperature sum defining the end of reproductive growth). Then, the Markov chain Monte Carlo (MCMC) methodology was adapted to calibrate the ModVege model by maximizing the likelihood function. The time-series LAI from MOD15A3H was assimilated into the ModVege model, and the model parameters ST2 and BMGV0 (initial biomass and green vegetative tissues, respectively) were optimized at a 500 m pixel scale based on the four-dimensional variational method (4DVar) method. Compared with August 15th, the RMSE and MAPE of aboveground biomass were 242 kg/ha and 10%, respectively, after calibration. Data assimilation improved this accuracy, with the RMSE decreasing to 214 kg/ha. Overall, the aboveground grassland biomass of Xilinhot City shows spatial distribution patterns of high value in the northeast and low value in the central and southeast areas. Generally, the method implemented in this study provides an important reference for the aboveground biomass estimation of regional grassland.

[1]  Jianxi Huang,et al.  A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation , 2022, Scientific data.

[2]  Jianxi Huang,et al.  Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China , 2021, Remote. Sens..

[3]  T. Tu,et al.  Grass modelling in data-limited areas by incorporating MODIS data products , 2021 .

[4]  Yang Yong,et al.  Dynamic monitoring of net primary productivity and its response to climate factors in native grassland in Inner Mongolia using a light-use efficiency model , 2020 .

[5]  Jianxi Huang,et al.  Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches , 2020, Remote. Sens..

[6]  M. Zeeman,et al.  Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis , 2020 .

[7]  Philip Lewis,et al.  Assimilation of remote sensing into crop growth models: Current status and perspectives , 2019, Agricultural and Forest Meteorology.

[8]  E. Ruelle,et al.  Weather forecasts to enhance an Irish grass growth model , 2019, European Journal of Agronomy.

[9]  Luc Delaby,et al.  Development of the Moorepark St Gilles grass growth model (MoSt GG model): A predictive model for grass growth for pasture based systems , 2018, European Journal of Agronomy.

[10]  P. Virkajärvi,et al.  Modelling grass yields in northern climates – a comparison of three growth models for timothy , 2018, Field Crops Research.

[11]  Xueting Zhang,et al.  Assimilation of 30m resolution LAI into crop growth model for improving LAI estimation in plateau grassland , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[12]  P. Carrère,et al.  Testing the ability of a simple grassland model to simulate the seasonal effects of drought on herbage growth , 2016 .

[13]  Jianxi Huang,et al.  Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation , 2016 .

[14]  J. Schellberg,et al.  Grass strategies and grassland community responses to environmental drivers: a review , 2015, Agronomy for Sustainable Development.

[15]  Dehai Zhu,et al.  Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model , 2015 .

[16]  Philipp Kraft,et al.  SPOTting Model Parameters Using a Ready-Made Python Package , 2015, PloS one.

[17]  Shi Qiu,et al.  Estimating the Aboveground Dry Biomass of Grass by Assimilation of Retrieved LAI Into a Crop Growth Model , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Bin Xu,et al.  Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China , 2014, Remote. Sens..

[19]  Bin Xu,et al.  Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China , 2014, Remote. Sens..

[20]  Tomoko Nakano,et al.  Monitoring aboveground biomass in semiarid grasslands using MODIS images , 2013 .

[21]  R. Schulte,et al.  Evaluation of three grass growth models to predict grass growth in Ireland , 2012, The Journal of Agricultural Science.

[22]  Michael Sommer,et al.  Application of satellite remote sensing for mapping wind erosion risk and dust emission‐deposition in Inner Mongolia grassland, China , 2012 .

[23]  B. Mary,et al.  Soil moisture effects on gross nitrification differ between adjacent grassland and forested soils in central Alberta, Canada , 2012, Plant and Soil.

[24]  C. Bernhofer,et al.  Spatiotemporal variability of grassland vegetation cover in a catchment in Inner Mongolia, China, derived from MODIS data products , 2011, Plant and Soil.

[25]  Hao Lu,et al.  Damage and Control of Poisonous Weeds in Western Grassland of China , 2010 .

[26]  Michele Meroni,et al.  Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model , 2009, Sensors.

[27]  Philip Lewis,et al.  Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter , 2008 .

[28]  P. Carrère,et al.  Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description , 2006 .

[29]  M. Oesterheld,et al.  Effect of grazing on community structure and productivity of a Uruguayan grassland , 2005, Plant Ecology.

[30]  W. Rawls,et al.  Estimating generalized soil-water characteristics from texture , 1986 .

[31]  I. R. Johnson,et al.  Vegetative crop growth model incorporating leaf area expansion and senescence, and applied to grass , 1983 .

[32]  Jasper A. Vrugt,et al.  Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation , 2016, Environ. Model. Softw..

[33]  Zhou Xi.  Aboveground Biomass Inversion of Grassland in Ili Area Using MODIS Data , 2015 .

[34]  L. Rui.  Using Data of HJ-1A/B Satellite for Hulunbeier Grassland Aboveground Biomass Estimation , 2010 .

[35]  Zhao Jun.  Principles and methods of grassland yield estimation by using remote sensing technology , 2009 .

[36]  Xie Gao,et al.  Study on valuation of rangeland ecosystem services of China , 2001 .

[37]  A. Brereton,et al.  Agrometeorology of grass and grasslands for middle latitudes , 1996 .