{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T13:20:12Z","timestamp":1773580812015,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T00:00:00Z","timestamp":1599350400000},"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>Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.<\/jats:p>","DOI":"10.3390\/rs12182888","type":"journal-article","created":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T23:12:49Z","timestamp":1599433969000},"page":"2888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1381-6661","authenticated-orcid":false,"given":"Nishanta","family":"Khanal","sequence":"first","affiliation":[{"name":"International Centre for Integrated Mountain Development, Kathmandu GPO Box 3226, Nepal"},{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1596-6855","authenticated-orcid":false,"given":"Mir Abdul","family":"Matin","sequence":"additional","affiliation":[{"name":"International Centre for Integrated Mountain Development, Kathmandu GPO Box 3226, Nepal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2711-5791","authenticated-orcid":false,"given":"Kabir","family":"Uddin","sequence":"additional","affiliation":[{"name":"International Centre for Integrated Mountain Development, Kathmandu GPO Box 3226, Nepal"}]},{"given":"Ate","family":"Poortinga","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Tower, 979\/69 Paholyothin Road, Samsen Nai, Phayathai, Bangkok 10400, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6392-6084","authenticated-orcid":false,"given":"Farrukh","family":"Chishtie","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Tower, 979\/69 Paholyothin Road, Samsen Nai, Phayathai, Bangkok 10400, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5842-0663","authenticated-orcid":false,"given":"Karis","family":"Tenneson","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Tower, 979\/69 Paholyothin Road, Samsen Nai, Phayathai, Bangkok 10400, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-1219","authenticated-orcid":false,"given":"David","family":"Saah","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Tower, 979\/69 Paholyothin Road, Samsen Nai, Phayathai, Bangkok 10400, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1126\/science.1159607","article-title":"Ecosystem disturbance, carbon, and climate","volume":"321","author":"Running","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7959","DOI":"10.3390\/rs70607959","article-title":"Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps","volume":"7","author":"Waldner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s13021-016-0065-6","article-title":"Improving carbon monitoring and reporting in forests using spatially-explicit information","volume":"11","author":"Boisvenue","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/rse2.15","article-title":"Framing the concept of satellite remote sensing essential biodiversity variables: Challenges and future directions","volume":"2","author":"Pettorelli","year":"2016","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Turner","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","first-page":"2961","article-title":"Lumped surface and sub-surface runoff for erosion modeling within a small hilly watershed in northern Vietnam","volume":"28","author":"Bui","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_7","first-page":"S27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MGRS.2016.2548504","article-title":"Domain adaptation for the classification of remote sensing data: An overview of recent advances","volume":"4","author":"Tuia","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Landgrebe, D.A., and Malaret, E. (1986). Noise in remote-sensing systems: The effect on classification error. IEEE Trans. Geosci. Remote Sens., 294\u2013300.","DOI":"10.1109\/TGRS.1986.289648"},{"key":"ref_11","unstructured":"Markham, B., and Townshend, J. (1981). Land Cover Classification Accuracy as a Function of Sensor Spatial Resolution, NASA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4981","DOI":"10.1080\/01431160500213912","article-title":"A competitive pixel-object approach for land cover classification","volume":"26","author":"Song","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/014311600210641","article-title":"Beware of per-pixel characterization of land cover","volume":"21","author":"Townshend","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., and Dedieu, G. (2017). Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sens., 9.","DOI":"10.3390\/rs9020173"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rahmati, O., Ghorbanzadeh, O., Teimurian, T., Mohammadi, F., Tiefenbacher, J.P., Falah, F., Pirasteh, S., Ngo, P.T.T., and Bui, D.T. (2019). Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions. Remote Sens., 11.","DOI":"10.3390\/rs11242995"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/JSTARS.2010.2062173","article-title":"Sub-pixel mapping of tree canopy, impervious surfaces, and cropland in the Laurentian Great Lakes Basin using MODIS time-series data","volume":"4","author":"Shao","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/07038992.2015.1089401","article-title":"Large area mapping of annual land cover dynamics using multitemporal change detection and classification of Landsat time series data","volume":"41","author":"Franklin","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"81","article-title":"Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data","volume":"12","author":"Lunetta","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wand, M.P., and Jones, M.C. (1994). Kernel Smoothing, Chapman and Hall\/CRC.","DOI":"10.1201\/b14876"},{"key":"ref_22","unstructured":"Simonoff, J.S. (2012). Smoothing Methods in Statistics, Springer Science & Business Media."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","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_26","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":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1080\/01431160500300297","article-title":"Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity","volume":"26","author":"Geerken","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3689","DOI":"10.1080\/01431161003762405","article-title":"Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements","volume":"32","author":"Atzberger","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.06.014","article-title":"A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine","volume":"155","author":"Kong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khanal, N., Uddin, K., Matin, M.A., and Tenneson, K. (2019). Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sens., 11.","DOI":"10.3390\/rs11192296"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.12.023","article-title":"An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data","volume":"174","author":"Shao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.5194\/bg-10-4055-2013","article-title":"A comparison of methods for smoothing and gap filling time series of remote sensing observations-application to MODIS LAI products","volume":"10","author":"Kandasamy","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2005.11.002","article-title":"Consideration of smoothing techniques for hyperspectral remote sensing","volume":"60","author":"Vaiphasa","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4534","DOI":"10.1109\/TGRS.2012.2192741","article-title":"An overview and comparison of smooth labeling methods for land-cover classification","volume":"50","author":"Schindler","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","first-page":"101979","article-title":"Primitives as building blocks for constructing land cover maps","volume":"85","author":"Saah","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"111278","DOI":"10.1016\/j.rse.2019.111278","article-title":"Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000-2017 Landsat time-series","volume":"232","author":"Potapov","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Tenneson, K., Shapiro, A., Nquyen, Q., San Aung, K., Chishtie, F., and Saah, D. (2019). Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification. Remote Sens., 11.","DOI":"10.3390\/rs11070831"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"186","DOI":"10.3389\/fenvs.2019.00186","article-title":"Linking earth observations for assessing the food security situation in Vietnam: A landscape approach","volume":"7","author":"Poortinga","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Aekakkararungroj, A., Kityuttachai, K., Nguyen, Q., Bhandari, B., Thwal, N.S., Priestley, H., Kim, J., Tenneson, K., and Chishtie, F. (2020). Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region. Remote Sens., 12.","DOI":"10.3390\/rs12091472"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jenvman.2014.07.047","article-title":"Development of 2010 national land cover database for the Nepal","volume":"148","author":"Uddin","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bey, A., S\u00e1nchez-Paus D\u00edaz, A., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.F., Moore, R., Federici, S., and Rezende, M. (2016). Collect earth: Land use and land cover assessment through augmented visual interpretation. Remote Sens., 8.","DOI":"10.3390\/rs8100807"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.envsoft.2019.05.004","article-title":"Collect Earth: An online tool for systematic reference data collection in land cover and use applications","volume":"118","author":"Saah","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/17538947.2010.505664","article-title":"A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America","volume":"4","author":"Atzberger","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_47","first-page":"1205","article-title":"The whittaker smoother and the moore-penrose inverse in signal reconstruction","volume":"6","author":"Chountasis","year":"2012","journal-title":"Appl. Math. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1109\/TGRS.2007.903044","article-title":"Extracting phenological signals from multiyear AVHRR NDVI time series: Framework for applying high-order annual splines with roughness damping","volume":"45","author":"Hermance","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1080\/01431160600967128","article-title":"Stabilizing high-order, non-classical harmonic analysis of NDVI data for average annual models by damping model roughness","volume":"28","author":"Hermance","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/34.291451","article-title":"Automated smoothing of image and other regularly spaced data","volume":"16","author":"Berman","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1080\/0143116031000115085","article-title":"Smoothing vegetation spectra with wavelets","volume":"25","author":"Schmidt","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/PROC.1967.5957","article-title":"What is the fast Fourier transform?","volume":"55","author":"Cochran","year":"1967","journal-title":"Proc. IEEE"},{"key":"ref_53","unstructured":"Gui\u00f1\u00f3n, J.L., Ortega, E., Garc\u00eda-Ant\u00f3n, J., and P\u00e9rez-Herranz, V. (2007). Moving average and Savitzki-Golay smoothing filters using Mathcad. Pap. ICEE, 2007."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.5751\/ES-01230-100124","article-title":"Evaluating forest management in Nepal: Views across space and time","volume":"10","author":"Nagendra","year":"2005","journal-title":"Ecol. Soc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"476","article-title":"Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data","volume":"10","author":"Mingwei","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2888\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:24Z","timestamp":1760177244000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,6]]},"references-count":57,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182888"],"URL":"https:\/\/doi.org\/10.3390\/rs12182888","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,6]]}}}