{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T10:14:42Z","timestamp":1792059282840,"version":"4.4.0"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T00:00:00Z","timestamp":1467676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study improved simulation of forest carbon fluxes in the Changbai Mountains with a process-based model (Biome-BGC) using incorporation and data assimilation. Firstly, the original remote sensing-based MODIS MOD_17 GPP (MOD_17) model was optimized using refined input data and biome-specific parameters. The key ecophysiological parameters of the Biome-BGC model were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis. Then the optimized MOD_17 model was used to calibrate the Biome-BGC model by adjusting the sensitive ecophysiological parameters. Once the best match was found for the 10 selected forest plots for the 8-day GPP estimates from the optimized MOD_17 and from the Biome-BGC, the values of sensitive ecophysiological parameters were determined. The calibrated Biome-BGC model agreed better with the eddy covariance (EC) measurements (R2 = 0.87, RMSE = 1.583 gC\u00b7m\u22122\u00b7d\u22121) than the original model did (R2 = 0.72, RMSE = 2.419 gC\u00b7m\u22122\u00b7d\u22121). To provide a best estimate of the true state of the model, the Ensemble Kalman Filter (EnKF) was used to assimilate five years (of eight-day periods between 2003 and 2007) of Global LAnd Surface Satellite (GLASS) LAI products into the calibrated Biome-BGC model. The results indicated that LAI simulated through the assimilated Biome-BGC agreed well with GLASS LAI. GPP performances obtained from the assimilated Biome-BGC were further improved and verified by EC measurements at the Changbai Mountains forest flux site (R2 = 0.92, RMSE = 1.261 gC\u00b7m\u22122\u00b7d\u22121).<\/jats:p>","DOI":"10.3390\/rs8070567","type":"journal-article","created":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T10:06:19Z","timestamp":1467713179000},"page":"567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation"],"prefix":"10.3390","volume":"8","author":[{"given":"Min","family":"Yan","sequence":"first","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Tian","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erxue","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3591-4091","authenticated-orcid":false,"given":"Xufeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongtao","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry Education, Fuzhou University, Fuzhou 350002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qu\u00e9r\u00e9, C.L., Moriarty, R., Andrew, R.M., Canadell, J.G., Sitch, S., Korsbakken, J.I., Friedlingstein, P., Peters, G.P., Andres, R.J., and Boden, T.A. 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