{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T17:25:44Z","timestamp":1764350744622,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571105"],"award-info":[{"award-number":["41571105"]}],"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>Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE\u00af = 11.1d) is better than daily fusion datasets (RMSE\u00af = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE\u00af of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.<\/jats:p>","DOI":"10.3390\/rs14112542","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:25:12Z","timestamp":1653956712000},"page":"2542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets"],"prefix":"10.3390","volume":"14","author":[{"given":"Yungang","family":"Cao","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Puying","family":"Du","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Xueqin","family":"Bai","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Ruodan","family":"Lei","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Xiuchun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"ref_1","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_2","first-page":"929","article-title":"Responses of plant phenology to climatic change","volume":"26","author":"Lu","year":"2006","journal-title":"Acta Ecol. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13740","DOI":"10.1073\/pnas.0600815103","article-title":"Diverse responses of phenology to global changes in a grassland ecosystem","volume":"103","author":"Cleland","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10265-008-0173-9","article-title":"Effects of growth temperature and winter duration on leaf phenology of a spring ephemeral (Gagea lutea) and a summergreen forb (Maianthemum dilatatum)","volume":"121","author":"Yoshie","year":"2008","journal-title":"J. Plant Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s00704-018-2527-0","article-title":"Characteristics of vegetation activity and its responses to climate change in desert\/grassland biome transition zones in the last 30years based on GIMMS3g","volume":"136","author":"Hou","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.05.125","article-title":"Inner Mongolian grassland plant phenological changes and their climatic drivers","volume":"683","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1080\/01431160110106087","article-title":"Evaluating vegetation phenological patterns in Inner Mongolia using NDVI time-series analysis","volume":"23","author":"Lee","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1111\/gcb.13168","article-title":"Variability and evolution of global land surface phenology over the past three decades (1982\u20132012)","volume":"22","author":"Garonna","year":"2016","journal-title":"Glob. Change Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.agrformet.2004.12.002","article-title":"Energy exchange between the atmosphere and a meadow ecosystem on the Qinghai-Tibetan Plateau","volume":"129","author":"Gu","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1111\/j.1365-2486.2005.01097.x","article-title":"Onset of spring starting earlier across the Northern Hemisphere","volume":"12","author":"Schwartz","year":"2006","journal-title":"Glob. Change Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1111\/j.1466-822X.2006.00247.x","article-title":"Altered geographic and temporal variability in phenology in response to climate change","volume":"15","author":"Menzel","year":"2006","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_13","unstructured":"Reed, B.C., and Brown, J.F. (2005, January 16\u201318). Trend analysis of time-series phenology derived from satellite data. Proceedings of the 3rd International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Biloxi, MS, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1029\/97GB00330","article-title":"A continental phenology model for monitoring vegetation responses to interannual climatic variability","volume":"11","author":"White","year":"1997","journal-title":"Glob. Biogeochem. Cycle"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.1080\/01431160310001618149","article-title":"European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset","volume":"25","author":"Stockli","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.scitotenv.2016.07.206","article-title":"Alpine vegetation phenology dynamic over 16years and its covariation with climate in a semi-arid region of China","volume":"572","author":"Zhou","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.agrformet.2017.10.026","article-title":"Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982\u20132013)","volume":"248","author":"Zhang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.scitotenv.2019.02.265","article-title":"Spatiotemporal variation in vegetation spring phenology and its response to climate change in freshwater marshes of Northeast China","volume":"666","author":"Shen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1111\/j.1365-2486.2011.02397.x","article-title":"Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982\u20132008","volume":"17","author":"Jeong","year":"2011","journal-title":"Glob. Change Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2013.01.010","article-title":"Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements","volume":"132","author":"Hmimina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Rover, J., Brown, J., Worstell, B., Howard, D., Wu, Z.T., Gallant, A.L., Rundquist, B., and Burke, M. (2019). Monitoring Landscape Dynamics in Central US Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11030328"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16226","DOI":"10.3390\/rs71215825","article-title":"Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition","volume":"7","author":"Brown","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1080\/01431161.2011.593581","article-title":"The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring\u2014A comparison with flux tower and MODIS NDVI","volume":"3","author":"Kovalskyy","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Snyder, K.A., Huntington, J.L., Wehan, B.L., Morton, C.G., and Stringham, T.K. (2019). Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin. Sensors, 19.","DOI":"10.3390\/s19051139"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yan, L., and Roy, D.P. (2018). Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS). Remote Sens., 10.","DOI":"10.3390\/rs10040609"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, X.L., Cai, F.Y., Tian, J.Q., and Williams, T.K.A. (2018). Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions. Remote Sens., 10.","DOI":"10.3390\/rs10040527"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/36.763276","article-title":"Unmixing-based multisensor multiresolution image fusion","volume":"37","author":"Zhukov","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2012.2186638","article-title":"Spatiotemporal Reflectance Fusion via Sparse Representation","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"063507","DOI":"10.1117\/1.JRS.6.063507","article-title":"Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model","volume":"6","author":"Wu","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1109\/TGRS.2012.2213095","article-title":"Spatiotemporal Satellite Image Fusion Through One-Pair Image Learning","volume":"51","author":"Song","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xie, D.F., Zhang, J.S., Zhu, X.F., Pan, Y.Z., Liu, H.L., Yuan, Z.M.Q., and Yun, Y. (2016). An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions. Sensors, 16.","DOI":"10.3390\/s16020207"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/JSTARS.2018.2797894","article-title":"Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, W.J., Zeng, Y.N., Li, S.N., Pi, X.Y., and Huang, W. (2019). An Improved Spatiotemporal Fusion Approach Based on Multiple Endmember Spectral Mixture Analysis. Sensors, 19.","DOI":"10.3390\/s19112443"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2014.01.007","article-title":"Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data","volume":"144","author":"Walker","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1080\/15481603.2018.1423725","article-title":"Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series","volume":"55","author":"Onojeghuo","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106310","DOI":"10.1016\/j.ecolind.2020.106310","article-title":"A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing","volume":"114","author":"Lyu","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9213","DOI":"10.3390\/rs6109213","article-title":"Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of \u201cIndex-then-Blend\u201d and \u201cBlend-then-Index\u201d Approaches","volume":"6","author":"Jarihani","year":"2014","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1080\/01431160410001719849","article-title":"Remote sensing capabilities to estimate pasture production in France","volume":"25","author":"Faivre","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal AVHRR data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1080\/01431161.2018.1506185","article-title":"Remote sensing monitoring of green-up dates in the Xilingol grasslands of northern China and their correlations with meteorological factors","volume":"40","author":"Guo","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Jonsson","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2015.02.003","article-title":"Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics","volume":"161","author":"Xin","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:19:03Z","timestamp":1760138343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,26]]},"references-count":48,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112542"],"URL":"https:\/\/doi.org\/10.3390\/rs14112542","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,5,26]]}}}