{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:46:36Z","timestamp":1777592796688,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T00:00:00Z","timestamp":1531958400000},"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":["41571341"],"award-info":[{"award-number":["41571341"]}],"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":["41331171"],"award-info":[{"award-number":["41331171"]}],"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>Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used.<\/jats:p>","DOI":"10.3390\/rs10071142","type":"journal-article","created":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T02:10:11Z","timestamp":1532052611000},"page":"1142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3923-6056","authenticated-orcid":false,"given":"Donghui","family":"Xie","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"US Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-2846","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"US Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"US Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0748-5525","authenticated-orcid":false,"given":"Martha","family":"Anderson","sequence":"additional","affiliation":[{"name":"US Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(87)90015-0","article-title":"The factor of scale in remote sensing","volume":"21","author":"Woodcock","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/S0034-4257(97)00003-5","article-title":"The effect of spatial resolution on the ability to monitor the status of agricultural lands","volume":"61","author":"Woodcock","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","first-page":"30","article-title":"Next generation of global land cover characterization, mapping, and monitoring","volume":"25","author":"Giri","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Giambene, G. (2007). Resource Management in Satellite Networks: Optimization and Cross-Layer Design, Springer.","DOI":"10.1007\/978-0-387-53991-1"},{"key":"ref_6","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_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cai, F., Tian, J., and Williams, T. (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_11","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_12","doi-asserted-by":"crossref","first-page":"6346","DOI":"10.3390\/rs5126346","article-title":"An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model","volume":"5","author":"Fu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_13","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."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4367","DOI":"10.1080\/01431161.2013.777488","article-title":"A spatial and temporal reflectance fusion model considering sensor observation differences","volume":"34","author":"Shen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7353","DOI":"10.1109\/TGRS.2014.2311445","article-title":"Operational Data Fusion Framework for Building Frequent Landsat-Like Imagery","volume":"52","author":"Wang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Frantz, D., R\u00f6der, A., Udelhoven, T., and Schmidt, M. (2016). Forest Disturbance Mapping Using Dense Synthetic Landsat\/MODIS Time-Series and Permutation-Based Disturbance Index Detection. Remote Sens., 8.","DOI":"10.3390\/rs8040277"},{"key":"ref_17","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_18","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":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"59","article-title":"Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data","volume":"13","author":"Singh","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2015.10.025","article-title":"Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach","volume":"185","author":"Semmens","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"63","article-title":"Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data","volume":"49","author":"Dong","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2013MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"4480","DOI":"10.1109\/JSTARS.2014.2343592","article-title":"Angular Effects and Correction for Medium Resolution Sensors to Support Crop Monitoring","volume":"7","author":"Gao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","article-title":"A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance","volume":"176","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0034-4257(02)00091-3","article-title":"First operational BRDF, albedo nadir reflectance products from MODIS","volume":"83","author":"Schaaf","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2018.02.001","article-title":"Capturing rapid land surface dynamics with Collection V006 MODIS BRDF\/NBAR\/Albedo (MCD43) products","volume":"207","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2015.12.033","article-title":"Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS","volume":"175","author":"Campagnolo","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","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_31","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_32","unstructured":"(2016, December 23). MODIS Data Products, 2015, Downloaded from http:\/\/reverb.echo.nasa.gov\/."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"43526","DOI":"10.1117\/1.3430002","article-title":"Building a consistent medium resolution satellite data set using moderate resolution imaging spectroradiometer products as reference","volume":"4","author":"Gao","year":"2010","journal-title":"J. Appl. Remote Sens."},{"key":"ref_35","unstructured":"(2016, December 23). Landsat Surface Reflectance Products, 2015, Downloaded from http:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2012.04.019","article-title":"A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images","volume":"124","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","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_39","unstructured":"(2016, December 23). USDA Cropland Data Layer (CDL), 2015. Downloaded from https:\/\/nassgeodata.gmu.edu\/CropScape\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0034-4257(01)00298-X","article-title":"Impact of sensor\u2019s point spread function on land cover characterization: Assessment and deconvolution","volume":"80","author":"Huang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2006.06.008","article-title":"The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions","volume":"105","author":"Tan","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal MODIS\u2013Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1080\/01431161.2015.1047991","article-title":"Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico","volume":"36","author":"Franklin","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","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 \u2018Index-then-Blend\u2019 and \u2018Blend-then-Index\u2019 Approaches","volume":"6","author":"Jarihani","year":"2014","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:13:03Z","timestamp":1760195583000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,19]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071142"],"URL":"https:\/\/doi.org\/10.3390\/rs10071142","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,19]]}}}