Grassland ecosystem is one of the most important and widely distributed ecosystem types in terrestrial ecosystems. Grassland biomass is an important indicator to characterize and evaluate grassland ecosystems. The purpose of this paper is to study a typical grassland biomass model based on vegetation index. Grassland A is used as the research area, the research object is grassland aboveground biomass, and the aboveground biomass data of field measured sampling points are used as modeling data. Based on the NDVI vegetation index, the correlation analysis between RVI and the measured value of above-ground biomass in grasslands, respectively, established a linear model, an exponential model and a quadratic polynomial model. Through SPSS statistical analysis, the accuracy of the model was tested, and the aboveground biomass of remote sensing grassland was inverted using the optimal model. The research results show that the aboveground grassland biomass simulated by the power function model with RVI-Y=102.45x1.35 is basically close to the measured aboveground biomass. The average error of the estimated error/measured value is 6.79%, and the fitting accuracy is 94.6%. Therefore, it is feasible to estimate grassland biomass using an exponential function model based on RVI-above-ground biomass.

Leinninger Gina. Biomass Model of Typical Grassland Area Based on Vegetation Index. Academic Journal of Environmental Biology (2020), Vol. 1, Issue 2: 26-34. https://doi.org/10.38007/AJEB.2020.010204.

[1] Okolie J A, Nanda S, Dalai A K, et al. Optimization and modeling of process parameters during hydrothermal gasification of biomass model compounds to generate hydrogen-rich gas products. International Journal of Hydrogen Energy, 2020, 45(36):18275-18288. https://doi.org/10.1016/j.ijhydene.2019.05.132

[2] Henner D N, Hastings A, Pogson M, et al. PopFor: A new model for estimating poplar yields. Biomass & bioenergy, 2020, 134(Mar.):1-15.

[3] Nguyen T, Mai V T, Huynh L K . Detailed kinetic modeling of thermal decomposition of guaiacol - A model compound for biomass lignin. Biomass & Bioenergy, 2018, 112(MAY):45-60.

[4] Okolie J A, Nanda S, Dalai A K, et al. Optimization and modeling of process parameters during hydrothermal gasification of biomass model compounds to generate hydrogen-rich gas products. International Journal of Hydrogen Energy, 2020, 45(36):18275-18288. https://doi.org/10.1016/j.ijhydene.2019.05.132

[5] Shirke V, Ranade S V, Bansal R . Equilibrium Model for Biomass Gasification: Study of effect of Biomass properties and Operating parameters. Materials Today: Proceedings, 2018, 5(11):22983-22992. https://doi.org/10.1016/j.matpr.2018.11.026

[6] Benson M L, Pierce L, Bergen K, et al. Model-Based Estimation of Forest Canopy Height and Biomass in the Canadian Boreal Forest Using Radar, LiDAR, and Optical Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 2020, PP(99):1-19.

[7] Obeid R, Lewis D M, Smith N, et al. Reaction Kinetics and Characterization of Species in Renewable Crude from Hydrothermal Liquefaction of Mixtures of Polymer Compounds To Represent Organic Fractions of Biomass Feedstocks. Energy And Fuels, 2020, 34(1):419-429.

[8] Collalti A, Tjoelker M G, Hoch G, et al. Plant respiration: Controlled by photosynthesis or biomass?. Global Change Biology, 2020, 26(3):1739-1753. https://doi.org/10.1111/gcb.14857

[9] Naif S S, Mahmood D A, Al-Jiboori M H . Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad. Open Agriculture, 2020, 5(1):631-637. https://doi.org/10.1515/opag-2020-0065

[10] Mandal D, Ratha D, Bhattacharya A, et al. A Radar Vegetation Index for Crop Monitoring Using Compact Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(9):6321-6335.

[11] Rohith G, Kumar L S . Effectiveness of Super-resolution technique on vegetation indices. IEEE Access, 2020, PP(99):1-1.

[12] Lees K J, Artz R, Khomik M, et al. Using Spectral Indices to Estimate Water Content and GPP in Sphagnum Moss and Other Peatland Vegetation. IEEE Transactions on Geoscience and Remote Sensing, 2020, PP(99):1-11.

[13] Chaudhary S K, Gupta D K, Srivastava P K, et al. Evaluation of Radar/Optical based vegetation descriptors in Water Cloud Model for Soil Moisture Retrieval. IEEE Sensors Journal, 2020, PP(99):1-1.

[14] Khan W, Minallah N, Khan I U, et al. On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop. IEEE Access, 2020, PP(99):1-1.

[15] Mam A, Plo B . Using Areal Composition of Riparian Vegetation Communities to Identify Thresholds in Prairie Streams - ScienceDirect. Rangeland Ecology & Management, 2020, 73( 1):162-170. https://doi.org/10.1016/j.rama.2019.09.004

[16] Kcf A, Yz A, Phg B, et al. Burning and Climate Interactions Determine Impacts of Grazing on Tallgrass Prairie Systems. Rangeland Ecology & Management, 2020, 73( 1):104-118. https://doi.org/10.1016/j.rama.2019.10.002

[17] Naif S S, Mahmood D A, Al-Jiboori M H . Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad. Open Agriculture, 2020, 5(1):631-637. https://doi.org/10.1515/opag-2020-0065

[18] Pordel F, Ebrahimi A, Azizi Z . Canopy cover or remotely sensed vegetation index,explanatory variables of above-ground biomass in an arid rangeland,Iran. Journal of Arid Land, 2018, v.10(05):107-120. https://doi.org/10.1007/s40333-018-0017-y