{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:47:05Z","timestamp":1775209625820,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Projects of Natural Science Fund of China","award":["41671368"],"award-info":[{"award-number":["41671368"]}]},{"name":"the Projects of Natural Science Fund of China","award":["41371348"],"award-info":[{"award-number":["41371348"]}]},{"name":"Strategic Priority Research Program A of the Chinese Academy of Sciences","award":["XDA20010301"],"award-info":[{"award-number":["XDA20010301"]}]},{"name":"the National Key Research and Development Program of China","award":["2017YFB0203101"],"award-info":[{"award-number":["2017YFB0203101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.<\/jats:p>","DOI":"10.3390\/ijgi8110502","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T11:17:25Z","timestamp":1573125445000},"page":"502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6353-2643","authenticated-orcid":false,"given":"Rui","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohui","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbo","family":"Su","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunrong","family":"Mi","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Resources and Environment, Shanxi Institute of Energy, Jinzhong 030600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"ref_1","first-page":"2879","article-title":"Recent Progresses in Monitoring Crop Spatial Patterns by Using Remote Sensing Technologies","volume":"43","author":"Tang","year":"2010","journal-title":"Sci. Agric. Sin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.rse.2010.01.006","article-title":"The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis","volume":"114","author":"Ozdogan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"166","article-title":"Review of research advances in remote sensing monitoring of grain crop area","volume":"21","author":"Chen","year":"2005","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_4","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_5","first-page":"531","article-title":"Crop area estimation with remote sensing","volume":"42","author":"David","year":"2010","journal-title":"Res. J. Agric. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s12524-014-0377-5","article-title":"Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China\u2019s Environment Satellite (HJ-1) Data","volume":"42","author":"Yao","year":"2014","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_7","first-page":"18","article-title":"Anhui Winter Wheat Growing Remote Sensing Monitoring and Evaluation Methods Research","volume":"27","author":"Liu","year":"2011","journal-title":"Chin. Agric. Sci. Bull."},{"key":"ref_8","first-page":"46","article-title":"Application and Analysis of MODIS Satellite NDVI Time Series Change in Winter Wheat Area Estimate","volume":"34","author":"Li","year":"2011","journal-title":"Meteorol. Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.scitotenv.2016.11.042","article-title":"Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery","volume":"579","author":"Pelta","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.scitotenv.2017.02.161","article-title":"A process-based land use\/land cover change assessment on a mountainous area of Greece during 1945\u20132009: Signs of socio-economic drivers","volume":"587","author":"Xystrakis","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.scitotenv.2017.04.124","article-title":"Land cover change during a period of extensive landscape restoration in Ningxia Hui Autonomous Region, China","volume":"598","author":"Restrepo","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.scitotenv.2017.05.194","article-title":"Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amaz\u00f4nicos Savanna Vegetation Enclave (Brazilian Amazon)","volume":"601","author":"Alves","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.scitotenv.2014.09.099","article-title":"Integration of remote sensing datasets for local scale assessment and prediction of drought","volume":"505","author":"Nichol","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.08.023","article-title":"Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery","volume":"140","author":"Zhong","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.3390\/rs5031335","article-title":"Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","first-page":"361","article-title":"NDVI time-series reconstruction based on MODIS and HJ-1 CCD data spatial\u2013temporal fusion","volume":"20","author":"Sun","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2013.10.005","article-title":"Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows","volume":"87","author":"Witharana","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","first-page":"1122","article-title":"Recent Progresses in Research of Integrating Multi-Source Remote Sensing Data for Crop Mapping","volume":"48","author":"Song","year":"2015","journal-title":"Sci. Agric. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.rse.2014.05.015","article-title":"Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil","volume":"152","author":"Dupuy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qiao, H.-B., Cheng, D.-F., and Soc, I.C. (2009, January 4\u20135). Application of EOS\/MODIS-NDVI at Different Time Sequences on Monitoring Winter Wheat Acreage in Henan Province. Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology, Wuhan, China.","DOI":"10.1109\/ESIAT.2009.159"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1139\/x90-063","article-title":"Advances in remote sensing technologies for forest surveys and management","volume":"20","author":"Leckie","year":"1990","journal-title":"Can. J. For. Res."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1080\/01431168508948281","article-title":"Analysis of the Phenology of Global Vegetation Using Meteorological Satellite Data","volume":"6","author":"Justice","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9735","DOI":"10.1080\/01431161.2011.576710","article-title":"Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: A comparison study","volume":"32","author":"Turker","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1080\/01431160701395203","article-title":"Crop classification by support vector machine with intelligently selected training data for an operational application","volume":"29","author":"Mathur","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.compag.2010.12.012","article-title":"Evaluating high resolution SPOT 5 satellite imagery for crop identification","volume":"75","author":"Yang","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","first-page":"3306","article-title":"HJ-1 Remotely Sensed Data and Sampling Method for Wheat Area Estimation","volume":"43","author":"Zhang","year":"2010","journal-title":"Sci. Agric. Sin."},{"key":"ref_28","first-page":"294","article-title":"Decision Tree Algorithm of Automatically Extracting Paddy Rice Information 5from SPOT-5 Images Based on Characteristic Bands","volume":"23","author":"Zheng","year":"2008","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_29","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_30","first-page":"32","article-title":"Forest cover classification using Landsat ETM plus data and time series MODIS NDVI data","volume":"33","author":"Jia","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"11518","DOI":"10.3390\/rs61111518","article-title":"Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data","volume":"6","author":"Jia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2014.04.008","article-title":"Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","volume":"149","author":"Pervez","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"103","article-title":"A support vector machine to identify irrigated crop types using time-series Landsat NDVI data","volume":"34","author":"Zheng","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jin, N., Tao, B., Ren, W., Feng, M., Sun, R., He, L., Zhuang, W., and Yu, Q. (2016). Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data. Remote Sens., 8.","DOI":"10.3390\/rs8030207"},{"key":"ref_37","first-page":"30","article-title":"Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland","volume":"30","author":"Olexa","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"704","DOI":"10.3390\/rs70100704","article-title":"A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity","volume":"7","author":"Bisquert","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3493","DOI":"10.1080\/01431169408954342","article-title":"A global 1\u00b0 by 1\u00b0 NDVI data set for climate studies derived from the GIMMS continental NDVI data","volume":"15","author":"Los","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","first-page":"118","article-title":"Fidelity Performance of Three Filters for High Quality NDVI Time-series Analysis","volume":"25","author":"Cao","year":"2010","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_41","first-page":"732","article-title":"Comparison on Three Algorithms of Reconstructing Time-series MODIS EVI","volume":"17","author":"Wang","year":"2015","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_42","first-page":"58","article-title":"NDVI Time-series Reconstruction Methods of China\u2019s HJ Satellite Imagery","volume":"30","author":"Li","year":"2015","journal-title":"Remote Sens. Inf."},{"key":"ref_43","first-page":"133","article-title":"Analysis on Three NDVI Time-series Reconstruction Methods and Their Applications in North Tibet","volume":"13","author":"Song","year":"2011","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2012.11.009","article-title":"Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data","volume":"130","author":"Brown","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0034-4257(02)00051-2","article-title":"Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data","volume":"82","author":"Xiao","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2007.05.006","article-title":"A hyperspectral band selector for plant species discrimination","volume":"62","author":"Vaiphasa","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_48","first-page":"1098","article-title":"A novel optimization parameters of support vector machines model for the land use\/cover classification","volume":"10","author":"Liu","year":"2012","journal-title":"J. Food Agric. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.rse.2009.01.010","article-title":"Land cover mapping of large areas using chain classification of neighboring Landsat satellite images","volume":"113","author":"Knorn","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TGRS.2009.2032239","article-title":"Simulated Multispectral Imagery for Tree Species Classification Using Support Vector Machines","volume":"48","author":"Heikkinen","year":"2010","journal-title":"Ieee Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4143","DOI":"10.1109\/TGRS.2009.2023908","article-title":"Support Vector Machine for Multifrequency SAR Polarimetric Data Classification","volume":"47","author":"Lardeux","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of biomass in wheat using random forest regression algorithm and remote sensing data","volume":"4","author":"Li","year":"2016","journal-title":"Crop J."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ghasemian, N., and Akhoondzadeh, M. (2018). Introducing two Random Forest based methods for cloud detection in remote sensing images. Adv. Space Res., S0273117718303624.","DOI":"10.1016\/j.asr.2018.04.030"},{"key":"ref_55","first-page":"93","article-title":"Classification of tree species based on longwave hyperspectral data from leaves, a case study for a tropical dry forest","volume":"66","author":"Harrison","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box\u2014A Toolbox and Application Programming Interface for EnMAP Data Processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.rse.2011.10.014","article-title":"Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology","volume":"117","author":"Walker","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2009.03.014","article-title":"Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority","volume":"113","author":"Foody","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS)","volume":"43","author":"Allouche","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_62","first-page":"173","article-title":"Spatial-temporal pattern change of winter wheat area in northwest Shandong Province during 2000\u20132014","volume":"29","author":"Zhao","year":"2017","journal-title":"Remote Sens. Land Resour."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"526","DOI":"10.3390\/rs2020526","article-title":"Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data","volume":"2","author":"Gu","year":"2010","journal-title":"Remote Sens."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/502\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:36Z","timestamp":1760189556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":63,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["ijgi8110502"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8110502","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,7]]}}}