{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:01:14Z","timestamp":1781244074748,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm\u2013artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space\u2013time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction\u2019s NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.<\/jats:p>","DOI":"10.3390\/s23042040","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T02:14:11Z","timestamp":1676254451000},"page":"2040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["High-Spatial-Resolution NDVI Reconstruction with GA-ANN"],"prefix":"10.3390","volume":"23","author":[{"given":"Yanhong","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Earth Science and Mapping Engineering, China University of Mining and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Hou","sequence":"additional","affiliation":[{"name":"Satellite Environment Application Center, Ministry of Ecology and Environment, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbao","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Earth Science and Mapping Engineering, China University of Mining and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"Satellite Environment Application Center, Ministry of Ecology and Environment, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5272-1158","authenticated-orcid":false,"given":"Jun","family":"Zhai","sequence":"additional","affiliation":[{"name":"Satellite Environment Application Center, Ministry of Ecology and Environment, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jiang, W., Niu, Z., Wang, L., Ya, R., Gui, X., Xiang, F., and Ji, Y. (2022). Impacts of Drought and Climatic Factors on Vegetation Dynamics in the Yellow River Basin and Yangtze River Basin, China. Remote Sens., 14.","DOI":"10.3390\/rs14040930"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1111\/j.1365-2486.2011.02397.x","article-title":"Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982\u20132008","volume":"17","author":"Jeong","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the satellite-derived NDVI to assess ecological responses to environmental change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1109\/JSTARS.2021.3066697","article-title":"Remote Sensing Estimation of Chlorophyll-A in Case-II Waters of Coastal Areas: Three-Band Model Versus Genetic Algorithm-Artificial Neural Networks Model","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","first-page":"5504316","article-title":"Hyperspectral Image Classification Based on Deep Attention Graph Convolutional Network","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Transations Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113073","DOI":"10.1016\/j.rse.2022.113073","article-title":"Near-real-time monitoring of land disturbance with harmonized Landsats 7-8 and Sentinel-2 data","volume":"278","author":"Shang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.isprsjprs.2022.01.021","article-title":"Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone?","volume":"185","author":"Tulbure","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"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":"256","DOI":"10.1016\/j.inffus.2020.07.004","article-title":"Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources","volume":"63","author":"Ghamisi","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, B., Zhang, L., Xie, D., Yin, X., Liu, C., and Liu, G. (2016). Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8010010"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, M., Liu, X., Dong, X., Zhao, B., Zou, X., Wu, L., and Wei, H. (2020). An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM. Remote Sens., 12.","DOI":"10.3390\/rs12213673"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., Yao, Y., Wei, Y., Zhang, Y., Jia, K., Zhang, X., Shang, K., Bei, X., and Guo, X. (2020). A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data. Remote Sens., 12.","DOI":"10.3390\/rs12142312"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"063507","DOI":"10.1117\/1.JRS.6.063507","article-title":"Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model","volume":"6","author":"Wu","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhang, W., Marinoni, A., Gao, L., and Zhang, B. (2018). An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images. Remote Sens., 10.","DOI":"10.3390\/rs10091388"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.32604\/cmc.2022.024309","article-title":"Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data","volume":"71","year":"2022","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hou, S., Sun, W., Guo, B., Li, C., Li, X., Shao, Y., and Zhang, J. (2020). Adaptive-SFSDAF for Spatiotemporal Image Fusion that Selectively Uses Class Abundance Change Information. Remote Sens., 12.","DOI":"10.3390\/rs12233979"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ge, Y., Li, Y., Chen, J., Sun, K., Li, D., and Han, Q. (2020). A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data. Sensors, 2.","DOI":"10.3390\/s20061789"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, D., Li, Y., Yang, W., Ge, Y., Han, Q., Ma, L., Chen, Y., and Li, X. (2018). An Enhanced Single-Pair Learning-Based Reflectance Fusion Algorithm with Spatiotemporally Extended Training Samples. Remote Sens., 10.","DOI":"10.3390\/rs10081207"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6791","DOI":"10.1109\/TGRS.2015.2448100","article-title":"An Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion","volume":"53","author":"Wu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1007\/s00500-018-3108-y","article-title":"Weakly paired multimodal fusion using multilayer extreme learning machine","volume":"22","author":"Wen","year":"2018","journal-title":"Soft Comput."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"7558","DOI":"10.1109\/ACCESS.2019.2962757","article-title":"Improving Time Series Reconstruction by Fixing Invalid Values and Its Fidelity Evaluation","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111884","DOI":"10.1016\/j.rse.2020.111884","article-title":"Cirrus clouds that adversely affect Landsat 8 images: What are they and how to detect them?","volume":"246","author":"Qiu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2015.04.004","article-title":"Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations","volume":"164","author":"Ke","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Rouse, J.W., Haas, R.W., and Schell, J.A. (2022, April 11). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. NASA\/GSFCT Type III Final Report, Available online: https:\/\/ntrs.nasa.gov\/citations\/19750020419."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liao, L.M., Song, J.L., Wang, J.D., Xiao, Z.Q., and Wang, J. (2016). Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI. Remote Sens., 8.","DOI":"10.3390\/rs8060452"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, M., and Liu, Z. (2020). Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data. Sensors, 20.","DOI":"10.3390\/s20010006"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"36602","DOI":"10.1109\/ACCESS.2020.2971060","article-title":"A Hybrid Genetic Algorithm Based on Information Entropy and Game Theory","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","first-page":"107291","article-title":"Application of optimized Artificial and Radial Basis neural networks by using modified Genetic Algorithm on discharge coefficient prediction of modified labyrinth side weir with two and four cycles","volume":"152","author":"Zaji","year":"2020","journal-title":"Measurment"},{"key":"ref_34","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-Golay filter","volume":"180","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Tang, B., and Zhao, Y. (2022). Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions. Remote Sens., 14.","DOI":"10.3390\/rs14112716"},{"key":"ref_36","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_37","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_38","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_39","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_40","doi-asserted-by":"crossref","first-page":"8906","DOI":"10.3390\/rs70708906","article-title":"A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets","volume":"7","author":"Xu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1080\/17538947.2022.2044397","article-title":"A method for reconstructing NDVI time-series based on envelope detection and the Savitzky-Golay filter","volume":"15","author":"Liu","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Malamiri, H., Zare, H., Rousta, I., Olafsson, H., Verdiguier, E., Zhang, H., and Mushore, T. (2020). Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12172747"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5601413","DOI":"10.1109\/TGRS.2021.3050551","article-title":"A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network","volume":"60","author":"Tan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, X., Xiao, X., Fan, L., and Zuo, L. (2022). Changes in the Water-Energy Coupling Relationship in Grain Production: A Case Study of the North China Plain. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19159527"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2022.05.010","article-title":"A linear pushbroom satellite image epipolar resampling method for digital surface model generation","volume":"190","author":"Liao","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"112632","DOI":"10.1016\/j.rse.2021.112632","article-title":"Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion","volume":"264","author":"Chu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_48","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2040\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:31:21Z","timestamp":1760121081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,11]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042040"],"URL":"https:\/\/doi.org\/10.3390\/s23042040","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,11]]}}}