{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:31:03Z","timestamp":1772253063171,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T00:00:00Z","timestamp":1656806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["No.2018YFE0122700"],"award-info":[{"award-number":["No.2018YFE0122700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["No. 41971383"],"award-info":[{"award-number":["No. 41971383"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.2018YFE0122700"],"award-info":[{"award-number":["No.2018YFE0122700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41971383"],"award-info":[{"award-number":["No. 41971383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14133194","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T20:59:18Z","timestamp":1656968358000},"page":"3194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6293-164X","authenticated-orcid":false,"given":"Yuxin","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4099-8675","authenticated-orcid":false,"given":"Hai","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6942-0746","authenticated-orcid":false,"given":"Xuecao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunxiang","family":"Jin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-Informatics of Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9317-4015","authenticated-orcid":false,"given":"Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanlong","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s13593-015-0314-1","article-title":"Grass strategies and grassland community responses to environmental drivers: A review","volume":"35","author":"Maire","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_2","first-page":"47","article-title":"Study on valuation of rangeland ecosystem services of China","volume":"16","author":"Xie","year":"2001","journal-title":"J. Nat. Resour."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11258-004-5800-5","article-title":"Effect of grazing on community structure and productivity of a Uruguayan grassland","volume":"179","author":"Altesor","year":"2005","journal-title":"Plant Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1016\/S1671-2927(09)60242-X","article-title":"Damage and Control of Poisonous Weeds in Western Grassland of China","volume":"9","author":"Zhao","year":"2010","journal-title":"Agric. Sci. China"},{"key":"ref_5","first-page":"1","article-title":"Quantitative assessment of relative roles of climate change and human activities on grassland net primary productivity in the Three-River Source Region, China","volume":"26","author":"Zhang","year":"2017","journal-title":"Acta Prataculturae Sin."},{"key":"ref_6","first-page":"86","article-title":"Temporal and Spatial Pattern of Grassland Degradationand Its Determinants for Recent 30 Years in Xilingol","volume":"39","author":"Ma","year":"2017","journal-title":"Chin. J. Grassl."},{"key":"ref_7","first-page":"682","article-title":"Review of the application of vegetation remote sensing","volume":"27","author":"Shen","year":"2001","journal-title":"J. Zhejiang Univ. (Agric. Life Sci.)"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2480\/agrmet.69.1.1","article-title":"Monitoring aboveground biomass in semiarid grasslands using MODIS images","volume":"69","author":"Nakano","year":"2013","journal-title":"J. Agric. Meteorol."},{"key":"ref_9","first-page":"1754","article-title":"Estimation of the total production of the herbage in the Tianshan Mountain Area using remote sensing technology with NDVI similarity zoning","volume":"35","author":"Liu","year":"2018","journal-title":"Pratacultural Sci."},{"key":"ref_10","first-page":"27","article-title":"Aboveground Biomass Inversion of Grassland in Ili Area Using MODIS Data","volume":"23","author":"Zhou","year":"2015","journal-title":"Acta Agrectir. Sin."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/j.1744-697X.2011.00235.x","article-title":"Application of satellite remote sensing for mapping wind erosion risk and dust emission-deposition in Inner Mongolia grassland, China","volume":"58","author":"Reiche","year":"2012","journal-title":"Grassl. Sci."},{"key":"ref_12","first-page":"1122","article-title":"Using Data of HJ-1A\/B Satellite for Hulunbeier Grassland Aboveground Biomass Estimation","volume":"25","author":"Chen","year":"2010","journal-title":"J. Nat. Resour."},{"key":"ref_13","first-page":"1814","article-title":"Study on estimation method of Mongolia grassland production based on sparse samples","volume":"22","author":"Wang","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_14","first-page":"10","article-title":"Monitoring of grassland herbage accumulation by remote sensing using MOD09GA data in Xinjiang","volume":"27","author":"Xun","year":"2018","journal-title":"Acta Prataculturae Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1111\/1365-3040.ep11588103_6_9","article-title":"Vegetative crop growth model incorporating leaf area expansion and senescence, and applied to grass","volume":"6","author":"Johnson","year":"1983","journal-title":"Plant Cell Environ."},{"key":"ref_16","unstructured":"Brereton, A.J., Danielov, S.A., and Scott, D. (1996). Agrometeorology of Grass and Grasslands for Middle Latitudes, World Meteorological Organisation."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1111\/j.1365-2494.2006.00515.x","article-title":"Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description","volume":"61","author":"Jouven","year":"2006","journal-title":"Grass Forage Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1111\/j.1365-2494.2006.00517.x","article-title":"Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 2. Model evaluation","volume":"61","author":"Jouven","year":"2006","journal-title":"Grass Forage Sci."},{"key":"ref_19","first-page":"91","article-title":"Evaluation of three grass growth models to predict grass growth in Ireland","volume":"151","author":"Hennessy","year":"2012","journal-title":"J. Agric. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.5194\/bg-17-1071-2020","article-title":"Wintertime grassland dynamics may influence belowground biomass under climate change: A model analysis","volume":"17","author":"Katata","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1016\/j.rse.2007.05.020","article-title":"Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter","volume":"112","author":"Quaife","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/s90200922","article-title":"Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model","volume":"9","author":"Migliavacca","year":"2009","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/JSTARS.2014.2360676","article-title":"Estimating the Aboveground Dry Biomass of Grass by Assimilation of Retrieved LAI into a Crop Growth Model","volume":"8","author":"He","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108250","DOI":"10.1016\/j.fcr.2021.108250","article-title":"Grass modelling in data-limited areas by incorporating MODIS data products","volume":"271","author":"Huang","year":"2021","journal-title":"Field Crop Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, X.T., He, B.B., and Quan, X. (2016, January 10\u201315). Assimilation of 30 m resolution LAI into crop growth model for improving LAI estimation in plateau grassland. Proceedings of the International Geoscience and Remote Sensing Symposium (IGRASS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729329"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.agrformet.2015.10.013","article-title":"Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation","volume":"216","author":"Huang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2015.02.001","article-title":"Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model","volume":"204","author":"Huang","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","first-page":"1","article-title":"Dynamic monitoring of net primary productivity and its response to climate factors in native grassland in Inner Mongolia using a light-use efficiency model","volume":"29","author":"Wu","year":"2020","journal-title":"Acta Prataculturae Sin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.3390\/rs6021496","article-title":"Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China","volume":"6","author":"Jin","year":"2014","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.fcr.2015.12.008","article-title":"Testing the ability of a simple grassland model to simulate the seasonal effects of drought on herbage growth","volume":"187","author":"Calanca","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_31","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration-Guidelines for Computing Crop Water Requirement-FAO Irrigation and Drainage Paper 56, FAO. [1st ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.2136\/sssaj1986.03615995005000040039x","article-title":"Estimating generalized soil-water characteristics from texture","volume":"50","author":"Saxton","year":"1986","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_33","unstructured":"Chinese Ecosystem Research Network (2017). Plant phenological observation dataset of the Chinese Ecosystem Research Network (2003\u20132015) [DB\/OL]. Sci. Data Bank, 10."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.envsoft.2015.08.013","article-title":"Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation","volume":"75","author":"Vrugt","year":"2016","journal-title":"Environ. Model. Softw. Environ. Data News"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L. (2015). SPOTting Model Parameters Using a Ready-Made Python Package. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0145180"},{"key":"ref_36","first-page":"34","article-title":"Principles and methods of grassland yield estimation by using remote sensing technology","volume":"3","author":"Li","year":"2009","journal-title":"Pratacultural Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5368","DOI":"10.3390\/rs6065368","article-title":"Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China","volume":"6","author":"Zhao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","first-page":"89","article-title":"Development of the Moorepark St Gilles grass growth model (MoSt GG model): A pre-dictive model for grass growth for pasture based systems","volume":"99","author":"Ruellea","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.fcr.2018.04.014","article-title":"Modelling grass yields in northern climates\u2014A comparison of three growth models for timothy","volume":"224","author":"Korhonen","year":"2018","journal-title":"Field Crops Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.eja.2019.02.013","article-title":"Weather forecasts to enhance an Irish grass growth model","volume":"105","author":"McDonnell","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1038\/s41597-022-01305-6","article-title":"A dataset of winter wheat aboveground biomass in China during 2007\u20132015 based on data assimilation","volume":"9","author":"Huang","year":"2022","journal-title":"Sci. Data"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xie, Y., and Huang, J.X. (2021). Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China. Remote Sens., 13.","DOI":"10.3390\/rs13214372"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, X.L., Huang, J.X., Feng, Q.L., and Yin, D.Q. (2020). Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of China with deep learning approaches. Remote Sens., 12.","DOI":"10.3390\/rs12111744"},{"key":"ref_45","unstructured":"Wang, G.C., and Wen, Y.P. (1996). Carbon Pools in Terrestrial Ecosystems in China. Emissions and Their Relevant Processes of Greenhouse Gases in China, China Environment Science Press. [1st ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s11104-010-0465-4","article-title":"Spatiotemporal variability of grassland vegetation cover in a catchment in Inner Mongolia, China, derived from MODIS data products","volume":"340","author":"Schaffrash","year":"2011","journal-title":"Plant Soil"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s11104-011-0997-2","article-title":"Soil moisture effects on gross nitrification differ between adjacent grassland and forested soils in central Alberta, Canada","volume":"352","author":"Cheng","year":"2012","journal-title":"Plant Soil"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:42:18Z","timestamp":1760139738000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,3]]},"references-count":47,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133194"],"URL":"https:\/\/doi.org\/10.3390\/rs14133194","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1007\/s44397-025-00020-2","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,3]]}}}