{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T01:27:50Z","timestamp":1773106070854,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Engineer Research and Development Center (ERDC) Basic Research Portfolio","award":["0601102A\/T14\/ST1409"],"award-info":[{"award-number":["0601102A\/T14\/ST1409"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multispectral imagery provides unprecedented information on Earth system processes: however, data gaps due to clouds and shadows are a major limitation. Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Water Index (NDWI) are two spectral indexes employed for monitoring vegetation phenology, land-cover change and more. Synthetic Aperture Radar (SAR) with its cloud-penetrating abilities can fill data gaps using coincident imagery. In this study, we evaluated C-band Sentinel-1, L-band Uninhabited Aerial Vehicle SAR (UAVSAR) and texture for gap filling using efficient machine learning regression algorithms across three seasons. Multiple models were evaluated including Support Vector Machine, Random Forest, Gradient Boosted Trees and an ensemble of models. The Gap filling ability of SAR was evaluated with Sentinel-2 imagery from the same date, 3 days and 8 days later than both SAR sensors in September. Sentinel-1 and Sentinel-2 imagery from winter and spring seasons were also evaluated. Because SAR imagery contains noise, we compared two robust de-noising methods and evaluated performance against a refined lee speckle filter. Mean Absolute Error (MAE) rates of the cloud gap-filling model were assessed across different dataset combinations and land covers. The results indicated de-noised Sentinel-1 SAR and UAVSAR with GLCM texture provided the highest predictive abilities with random forest R2 = 0.91 (\u00b10.014), MAE = 0.078 (\u00b10.003) (NDWI) and R2 = 0.868 (\u00b10.015), MAE = 0.094 (\u00b10.003) (NDVI) during September. The highest errors were observed across bare ground and forest, while the lowest errors were on herbaceous and woody wetland. Results on January and June imagery without UAVSAR were less strong at R2 = 0.60 (\u00b10.036), MAE = 0.211 (\u00b10.005) (NDVI), R2 = 0.61 (\u00b10.043), MAE = 0.209 (\u00b10.005) (NDWI) for January and R2 = 0.72 (\u00b10.018), MAE = 0.142 (\u00b10.004) (NDVI), R2 = 0.77 (\u00b10.022), MAE = 0.125 (\u00b10.004) (NDWI) for June. Ultimately, the results suggest de-noised C-band SAR with texture metrics can accurately predict NDVI and NDWI for gap-filling clouds during most seasons. These shallow machine learning models are rapidly trained and applied faster than intensive deep learning or time series methods.<\/jats:p>","DOI":"10.3390\/rs14174221","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Gap Filling Cloudy Sentinel-2 NDVI and NDWI Pixels with Multi-Frequency Denoised C-Band and L-Band Synthetic Aperture Radar (SAR), Texture, and Shallow Learning Techniques"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8980-8943","authenticated-orcid":false,"given":"Kristofer","family":"Lasko","sequence":"first","affiliation":[{"name":"Geospatial Research Laboratory, Engineer Research and Development Center, Alexandria, VA 22315, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.rse.2015.08.020","article-title":"Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series","volume":"169","author":"DeVries","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2014.08.017","article-title":"Global, Landsat-based forest-cover change from 1990 to 2000","volume":"155","author":"Kim","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yao, W., Tang, Q., Liu, L., Xiao, P., Kong, X., Zhang, P., Shi, F., and Wang, Y. (2018). Continuous change detection of forest\/grassland and cropland in the Loess Plateau of China using all available Landsat data. Remote Sens., 10.","DOI":"10.3390\/rs10111775"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Masek, J., Ju, J., Roger, J.C., Skakun, S., Claverie, M., and Dungan, J. (2018, January 22\u201327). Harmonized Landsat\/Sentinel-2 products for land monitoring. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517760"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Rover, J., Brown, J., Worstell, B., Howard, D., Wu, Z., 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_11","doi-asserted-by":"crossref","unstructured":"Wilson, A.M., and Jetz, W. (2016). Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol., 14.","DOI":"10.1371\/journal.pbio.1002415"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1080\/10106049.2019.1608592","article-title":"Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping","volume":"36","author":"Lasko","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2014.10.009","article-title":"Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations","volume":"156","author":"Whitcraft","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Belda, S., Pipia, L., Morcillo-Pallar\u00e9s, P., and Verrelst, J. (2020). Optimizing gaussian process regression for image time series gap-filling and crop monitoring. Agronomy, 10.","DOI":"10.3390\/agronomy10050618"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A pixel-based Landsat compositing algorithm for large area land cover mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2014.11.005","article-title":"An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites","volume":"158","author":"Hermosilla","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1080\/01431161.2017.1399477","article-title":"Improvement of land-cover classification over frequently cloud-covered areas using Landsat 8 time-series composites and an ensemble of supervised classifiers","volume":"39","author":"Man","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","first-page":"202","article-title":"Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data","volume":"57","author":"Vuolo","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6749","DOI":"10.1038\/s41598-020-63560-0","article-title":"Could vegetation index be derive from synthetic aperture radar?\u2013the linear relationship between interferometric coherence and NDVI","volume":"10","author":"Bai","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-1379-2020","article-title":"Investigating the Performance of Random Forest and Support Vector Regression for Estimation of Cloud-Free Ndvi Using SENTINEL-1 SAR Data","volume":"43","author":"Mohite","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pelta, R., Beeri, O., Tarshish, R., and Shilo, T. (2022). Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach. Remote Sens., 14.","DOI":"10.3390\/rs14112600"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Haupt, S., Engelbrecht, J., and Kemp, J. (2017, January 23\u201328). Predicting modis EVI from SAR parameters using random forests algorithms. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127972"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112419","DOI":"10.1016\/j.rse.2021.112419","article-title":"Recurrent-based regression of Sentinel time series for continuous vegetation monitoring","volume":"263","author":"Garioud","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jing, R., Duan, F., Lu, F., Zhang, M., and Zhao, W. (2022). An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions. Remote Sens., 14.","DOI":"10.3390\/rs14071632"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1080\/10106049.2019.1624984","article-title":"Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data","volume":"36","author":"Yadav","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Grohnfeldt, C., Schmitt, M., and Zhu, X. (2018, January 22\u201327). A conditional generative adversarial network to fuse SAR and multispectral optical data for cloud removal from Sentinel-2 images. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519215"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Singh, P., and Komodakis, N. (2018, January 22\u201327). Cloud-gan: Cloud removal for sentinel-2 imagery using a cyclic consistent generative adversarial networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519033"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/LGRS.2019.2894734","article-title":"Synthesis of multispectral optical images from SAR\/optical multitemporal data using conditional generative adversarial networks","volume":"16","author":"Bermudez","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fuentes Reyes, M., Auer, S., Merkle, N., Henry, C., and Schmitt, M. (2019). Sar-to-optical image translation based on conditional generative adversarial networks\u2014Optimization, opportunities and limits. Remote Sens., 11.","DOI":"10.3390\/rs11172067"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gao, J., Yuan, Q., Li, J., Zhang, H., and Su, X. (2020). Cloud removal with fusion of high resolution optical and SAR images using generative adversarial networks. Remote Sens., 12.","DOI":"10.3390\/rs12010191"},{"key":"ref_31","first-page":"4105309","article-title":"Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation","volume":"60","author":"Darbaghshahi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/01431161.2021.2012295","article-title":"Removing cloud cover interference from Sentinel-2 imagery in Google Earth Engine by fusing Sentinel-1 SAR data with a CNN model","volume":"43","author":"Zhang","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.rse.2014.02.020","article-title":"C-and L-band SAR interoperability: Filling the gaps in continuous forest cover mapping in Tasmania","volume":"155","author":"Mitchell","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, X., Ziniti, B., Torbick, N., and Ducey, M.J. (2018). Assessment of forest above ground biomass estimation using multi-temporal C-band sentinel-1 and polarimetric L-band PALSAR-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10091424"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112111","DOI":"10.1016\/j.rse.2020.112111","article-title":"On the impact of C-band in place of L-band radar for SMAP downscaling","volume":"251","author":"Ghafari","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hamze, M., Baghdadi, N., El Hajj, M.M., Zribi, M., Bazzi, H., Cheviron, B., and Faour, G. (2021). Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13112102"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kraatz, S., Torbick, N., Jiao, X., Huang, X., Robertson, L.D., Davidson, A., McNairn, H., Cosh, M.H., and Siqueira, P. (2021). Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site. Agronomy, 11.","DOI":"10.3390\/agronomy11020273"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Bazzi, H., and Zribi, M. (2018). Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens., 11.","DOI":"10.3390\/rs11010031"},{"key":"ref_39","first-page":"1042704","article-title":"Sen2Cor for sentinel-2","volume":"Volume 10427","author":"Pflug","year":"2017","journal-title":"Image and Signal Processing for Remote Sensing XXIII"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hensley, S., Wheeler, K., Sadowy, G., Jones, C., Shaffer, S., Zebker, H., Miller, T., Heavey, B., Chuang, E., and Chao, R. (2008, January 26\u201330). The UAVSAR instrument: Description and first results. Proceedings of the 2008 IEEE Radar Conference, Rome, Italy.","DOI":"10.1109\/RADAR.2008.4720722"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3390\/rs5010042","article-title":"Using InSAR coherence to map stand age in a boreal forest","volume":"5","author":"Pinto","year":"2012","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"975","DOI":"10.3390\/rs4040975","article-title":"An empirical assessment of temporal decorrelation using the uninhabited aerial vehicle synthetic aperture radar over forested landscapes","volume":"4","author":"Simard","year":"2012","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fatoyinbo, L., Pinto, N., Hofton, M., Simard, M., Blair, B., Saatchi, S., Lou, Y., Dubayah, R., Hensley, S., and Armston, J. (2017, January 23\u201328). The 2016 NASA AfriSAR campaign: Airborne SAR and Lidar measurements of tropical forest structure and biomass in support of future satellite missions. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127949"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Geudtner, D., Torres, R., Snoeij, P., Davidson, M., and Rommen, B. (2014, January 13\u201318). Sentinel-1 system capabilities and applications. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946711"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lasko, K.D., and Sava, E. (2021). Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. ERDC Libr.","DOI":"10.21079\/11681\/42402"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yommy, A.S., Liu, R., and Wu, S. (2015, January 26\u201327). SAR image despeckling using refined Lee filter. Proceedings of the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2015.236"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/36.842003","article-title":"Multitemporal ERS SAR analysis applied to forest mapping","volume":"38","author":"Quegan","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","first-page":"4009105","article-title":"Thermal noise removal from polarimetric Sentinel-1 data","volume":"19","author":"Mascolo","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","unstructured":"Zuhlke, M., Fomferra, N., Brockmann, C., Peters, M., Veci, L., Malik, J., and Regner, P. (2015, January 2\u20135). SNAP (sentinel application platform) and the ESA sentinel 3 toolbox. Proceedings of the Sentinel-3 for Science Workshop, Venice, Italy."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1007\/978-0-387-39940-9_565","article-title":"Cross-validation","volume":"5","author":"Refaeilzadeh","year":"2009","journal-title":"Encycl. Database Syst."},{"key":"ref_51","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., and Fanise, P. (2018). Soil moisture and irrigation mapping in A semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10121953"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/JSTARS.2017.2784784","article-title":"Mapping double and single crop paddy rice with Sentinel-1A at varying spatial scales and polarizations in Hanoi, Vietnam","volume":"11","author":"Lasko","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mazza, A., Gargiulo, M., Scarpa, G., and Gaetano, R. (2018, January 22\u201327). Estimating the ndvi from sar by convolutional neural networks. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519459"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111452","DOI":"10.1016\/j.rse.2019.111452","article-title":"Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes","volume":"235","author":"Pipia","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Alvarez-Mozos, J., Villanueva, J., Arias, M., and Gonzalez-Audicana, M. (2021, January 11\u201316). Correlation Between NDVI and Sentinel-1 Derived Features for Maize. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554099"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sarafanov, M., Kazakov, E., Nikitin, N.O., and Kalyuzhnaya, A.V. (2020). A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and NDVI. Remote Sens., 12.","DOI":"10.3390\/rs12233865"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.isprsjprs.2021.08.015","article-title":"A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky\u2013Golay filter","volume":"180","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:16:17Z","timestamp":1760141777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,27]]},"references-count":60,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174221"],"URL":"https:\/\/doi.org\/10.3390\/rs14174221","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,27]]}}}