{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:38:01Z","timestamp":1777898281713,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:00:00Z","timestamp":1684800000000},"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":["42064003"],"award-info":[{"award-number":["42064003"]}],"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":["2021GXNSFBA220046"],"award-info":[{"award-number":["2021GXNSFBA220046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["42064003"],"award-info":[{"award-number":["42064003"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2021GXNSFBA220046"],"award-info":[{"award-number":["2021GXNSFBA220046"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel\u2013Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky\u2013Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications.<\/jats:p>","DOI":"10.3390\/ijgi12060214","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T10:51:56Z","timestamp":1684839116000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Jieyu","family":"Liang","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2591-6619","authenticated-orcid":false,"given":"Chao","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Zhuang Autonomous Region Mineral Resources Reserve Evaluation Center, Nanning 530022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiting","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenkui","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0937-8080","authenticated-orcid":false,"given":"Xiaohui","family":"Song","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anchao","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqi","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111951","DOI":"10.1016\/j.rse.2020.111951","article-title":"Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lyle, G., Clarke, K., Kilpatrick, A., Summers, D.M., and Ostendorf, B. (2023). A Spatial and Temporal Evaluation of Broad-Scale Yield Predictions Created from Yield Mapping Technology and Landsat Satellite Imagery in the Australian Mediterranean Dryland Cropping Region. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12020050"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ghorbanian, A., Mohammadzadeh, A., and Jamali, S. (2022). Linear and Non-Linear Vegetation Trend Analysis throughout Iran Using Two Decades of MODIS NDVI Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14153683"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mashhadi, N., and Alganci, U. (2022). Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11110573"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113060","DOI":"10.1016\/j.rse.2022.113060","article-title":"Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage","volume":"277","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xia, H., Pan, L., Zhao, X., Li, R., Bian, X., Wang, R., and Yu, C. (2021). Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090587"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1080\/02626667.2021.1934473","article-title":"Critical drought intensity-duration-frequency curves based on total probability theorem-coupled frequency analysis","volume":"66","author":"Aksoy","year":"2021","journal-title":"Hydrol. Sci. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, T., Wu, Z., Xiao, P., Sun, Z., Liu, Y., Wang, J., and Wang, Z. (2023). Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China. Remote Sens., 15.","DOI":"10.3390\/rs15051297"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Feng, S., Li, W., Xu, J., Liang, T., Ma, X., Wang, W., and Yu, H. (2022). Land Use\/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14215361"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., and Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sens., 14.","DOI":"10.3390\/rs14153639"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, R., Xu, Z., Chen, Y., Chen, J., and Shen, M. (2022). Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai\u2013Tibetan Plateau from 2000\u20132020. Remote Sens., 14.","DOI":"10.3390\/rs14153648"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, K., Luo, Y., Li, M., Zhong, S., Liu, Q., and Li, X. (2022). Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14174395"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1080\/01431161.2022.2047240","article-title":"Fusing Landsat-7, Landsat-8 and Sentinel-2 surface reflectance to generate dense time series images with 10m spatial resolution","volume":"43","author":"Xiong","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"102640","article-title":"High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques","volume":"105","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/17538947.2013.833313","article-title":"A cloud detection method based on a time series of MODIS surface reflectance images","volume":"6","author":"Tang","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, W., Ge, L., Luo, J., Huan, R., and Yang, Y. (2018). A Spectral\u2013Temporal Patch-Based Missing Area Reconstruction for Time-Series Images. Remote Sens., 10.","DOI":"10.3390\/rs10101560"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yan, L., and Roy, D.P. (2018). Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS). Remote Sens., 10.","DOI":"10.3390\/rs10040609"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111966","DOI":"10.1016\/j.rse.2020.111966","article-title":"An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach","volume":"248","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1080\/0143116021000021251","article-title":"Evaluation of compositing algorithms over the Brazilian Amazon using SPOT-4 VEGETATION data","volume":"24","author":"Carreiras","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2008.09.003","article-title":"Noise reduction of NDVI time series: An empirical comparison of selected techniques","volume":"113","author":"Hird","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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_22","first-page":"102818","article-title":"Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"5418","DOI":"10.1109\/TIM.2020.2966310","article-title":"Window Selection of the Savitzky\u2013Golay Filters for Signal Recovery from Noisy Measurements","volume":"69","author":"Sadeghi","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6008","DOI":"10.1109\/TGRS.2015.2431315","article-title":"A Moving Weighted Harmonic Analysis Method for Reconstructing High-Quality SPOT VEGETATION NDVI Time-Series Data","volume":"53","author":"Yang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Khanal, N., Matin, M.A., Uddin, K., Poortinga, A., Chishtie, F., Tenneson, K., and Saah, D. (2020). A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL. Remote Sens., 12.","DOI":"10.3390\/rs12182888"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.14358\/PERS.73.10.1129","article-title":"Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products","volume":"73","author":"Lu","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/MGRS.2022.3145854","article-title":"Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Hou, P., Jiang, J., Zhao, J., Chen, Y., and Zhai, J. (2023). High-Spatial-Resolution NDVI Reconstruction with GA-ANN. Sensors, 23.","DOI":"10.3390\/s23042040"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112130","DOI":"10.1016\/j.rse.2020.112130","article-title":"Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction","volume":"252","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, C., Lei, S., Yang, J., and Zhao, Y. (2020). A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110665"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.isprsjprs.2018.02.021","article-title":"Dynamic monitoring of the Poyang Lake wetland by integrating Landsat and MODIS observations","volume":"139","author":"Chen","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111901","DOI":"10.1016\/j.rse.2020.111901","article-title":"Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud","volume":"247","author":"Maneta","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5179","DOI":"10.1109\/TGRS.2020.2973762","article-title":"A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.3390\/ijgi4031423","article-title":"Comparative Analysis on Two Schemes for Synthesizing the High Temporal Landsat-like NDVI Dataset Based on the STARFM Algorithm","volume":"4","author":"Li","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, X., Li, W., He, S., and Zheng, T. (2023). Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China. Water, 15.","DOI":"10.3390\/w15101800"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7865","DOI":"10.3390\/rs70607865","article-title":"An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM\/ETM+ Images","volume":"7","author":"Rao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2019.03.012","article-title":"An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series","volume":"227","author":"Liu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111973","DOI":"10.1016\/j.rse.2020.111973","article-title":"FSDAF 2.0: Improving the performance of retrieving land cover changes and preserving spatial details","volume":"248","author":"Guo","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/LGRS.2016.2622726","article-title":"Fast and Accurate Spatiotemporal Fusion Based Upon Extreme Learning Machine","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"113111","DOI":"10.1016\/j.rse.2022.113111","article-title":"The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation","volume":"279","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., and Gong, P. (2021). Progress and Trends in the Application of Google Earth and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13183778"},{"key":"ref_49","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."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rse.2018.04.042","article-title":"STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-\/gap-free surface reflectance product","volume":"214","author":"Luo","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111718","DOI":"10.1016\/j.rse.2020.111718","article-title":"Spatially and temporally complete Landsat reflectance time series modelling: The fill-and-fit approach","volume":"241","author":"Yan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wang, H., Niu, X., Shao, W., and Yang, Y. (2022). Comparative Analysis and Comprehensive Trade-Off of Four Spatiotemporal Fusion Models for NDVI Generation. Remote Sens., 14.","DOI":"10.3390\/rs14235996"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., and Gong, P. (2022). An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote Sens., 14.","DOI":"10.3390\/rs14081863"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"113002","DOI":"10.1016\/j.rse.2022.113002","article-title":"A novel framework to assess all-round performances of spatiotemporal fusion models","volume":"274","author":"Zhu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0034-4257(02)00076-7","article-title":"The MODIS fire products","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat Data Continuity Mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:40:30Z","timestamp":1760125230000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/6\/214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,23]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["ijgi12060214"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12060214","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,23]]}}}