{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T20:18:24Z","timestamp":1785269904278,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T00:00:00Z","timestamp":1563408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["No. SQ2017YFC150110"],"award-info":[{"award-number":["No. SQ2017YFC150110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases\/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.<\/jats:p>","DOI":"10.3390\/rs11141699","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T03:14:41Z","timestamp":1563506081000},"page":"1699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method"],"prefix":"10.3390","volume":"11","author":[{"given":"Qi","family":"Yin","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3319-6284","authenticated-orcid":false,"given":"Maolin","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghai","family":"Ke","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Water Security, Base of the State Key Laboratory of Urban Environment Process &amp; Digital Modeling, Capital Normal University, Beijing 100089, China"},{"name":"Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the People\u2019s Republic of China, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuwan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China"},{"name":"Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the People\u2019s Republic of China, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11442-014-1082-6","article-title":"Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s","volume":"24","author":"Liu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1007\/s00521-010-0393-5","article-title":"Exchange rate prediction with non-numerical information","volume":"20","author":"Wang","year":"2011","journal-title":"Neural Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2015.04.008","article-title":"Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery","volume":"105","author":"Qin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.agrformet.2007.09.012","article-title":"Global warming, rice production, and water use in China: Developing a probabilistic assessment","volume":"148","author":"Tao","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4769","DOI":"10.1073\/pnas.0710581105","article-title":"Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia","volume":"105","author":"Gilbert","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.scitotenv.2016.10.223","article-title":"Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015","volume":"579","author":"Zhang","year":"2017","journal-title":"Sci. Total. Environ."},{"key":"ref_9","first-page":"1","article-title":"Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images","volume":"46","author":"Zhou","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1890\/04-0470","article-title":"Modeling gross primary production of an evergreen needleleaf forest using MODIS and climate data","volume":"15","author":"Xiao","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2015.01.004","article-title":"Tracking the dynamics of paddy rice planting area in 1986\u20132010 through time series Landsat images and phenology-based algorithms","volume":"160","author":"Dong","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.landurbplan.2009.02.002","article-title":"Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery","volume":"92","author":"Sakamoto","year":"2009","journal-title":"Landsc. Urban Plan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, G., Xiao, X., Dong, J., Kou, W., Jin, C., Qin, Y., Zhou, Y., Wang, J., Angelo Menarguez, M., and Biradar, C. (2015). Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS J. Photogramm. Remote Sens., 106.","DOI":"10.1016\/j.isprsjprs.2015.05.011"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.isprsjprs.2016.05.010","article-title":"Evolution of regional to global paddy rice mapping methods: A review","volume":"119","author":"Dong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"5346","DOI":"10.3390\/rs5105346","article-title":"An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_21","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_22","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_23","first-page":"1","article-title":"Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data","volume":"58","author":"Zhu","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2016.12.002","article-title":"Energy crop mapping with enhanced TM\/MODIS time series in the BCAP agricultural lands","volume":"124","author":"Wang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.asr.2018.09.018","article-title":"Object-based rice mapping using time-series and phenological data","volume":"63","author":"Zhang","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2016.12.008","article-title":"Multi-source remotely sensed data fusion for improving land cover classification","volume":"124","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.04.004","article-title":"Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data","volume":"93","author":"Jia","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16091","DOI":"10.3390\/rs71215820","article-title":"Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/JSTARS.2018.2834383","article-title":"Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2\/PALSAR-2","volume":"11","author":"Guan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, M., and Wu, B. (2016, January 18\u201320). Using high spatial and temporal resolution data blended from SPOT-5 and MODIS to map biomass of summer maize. Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577683"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.rse.2014.10.018","article-title":"Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery","volume":"156","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, S., Na, X., Kong, B., Wang, Z., Jiang, H., Yu, H., Zhao, Z., Li, X., Liu, C., and Dale, P. (2009). Identifying Wetland Change in China\u2019s Sanjiang Plain Using Remote Sensing. Wetlands, 29.","DOI":"10.1672\/08-04.1"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1016\/S2095-3119(17)61705-2","article-title":"Rice cultivation changes and its relationships with geographical factors in Heilongjiang Province, China","volume":"16","author":"Lu","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, H., Wan, W., Fang, Y., Zhu, S., Chen, X., Liu, B., and Yang, H. (2018). A Google Earth Engine-enabled Software for Efficiently Generating High-quality User-ready Landsat Mosaic Images. Environ. Model. Softw., 112.","DOI":"10.1016\/j.envsoft.2018.11.004"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Data Set for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global forest\/non-forest maps from ALOS PALSAR data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhang, X., and Luo, M. (2018). Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071047"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, G., Weng, Q., Pu, R., Gao, F., Sun, C., Li, H., and Zhao, C. (2016). Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE. Remote Sens., 8.","DOI":"10.3390\/rs8010075"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(02)00129-3","article-title":"Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in Northern Asia","volume":"84","author":"Xiao","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/S1002-0160(06)60064-7","article-title":"Multisensor Comparisons for Validation of MODIS Vegetation Indices1 1Project supported by the National Natural Science Foundation of China (No. 40171065) and the National High Technology Research and Development Program (863 Program) of China (No. 2002AA243011)","volume":"16","author":"Cheng","year":"2006","journal-title":"Pedosphere"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, L., Chang, Q., Li, F., Yan, L., Huang, Y., Wang, Q., and Luo, L. (2019). Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models. Remote Sens., 11.","DOI":"10.3390\/rs11030361"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2017.02.006","article-title":"Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration","volume":"126","author":"Ke","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2014.09.012","article-title":"A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion","volume":"156","author":"Gevaert","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1699\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:07:04Z","timestamp":1760188024000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1699"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,18]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141699"],"URL":"https:\/\/doi.org\/10.3390\/rs11141699","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,18]]}}}