{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:05:37Z","timestamp":1776276337788,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"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":["41771104"],"award-info":[{"award-number":["41771104"]}],"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>Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object\u2019s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.<\/jats:p>","DOI":"10.3390\/rs12030450","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:18:48Z","timestamp":1580872728000},"page":"450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5109-3812","authenticated-orcid":false,"given":"Quan","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7025-2137","authenticated-orcid":false,"given":"Diyou","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-8914","authenticated-orcid":false,"given":"Sijing","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenbo","family":"Du","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-5858","authenticated-orcid":false,"given":"Wei","family":"Su","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dehai","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8068-9415","authenticated-orcid":false,"given":"Xiaochuang","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-4973","authenticated-orcid":false,"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,1]]},"reference":[{"key":"ref_1","unstructured":"Ye, S., Zhao, C., Wang, Y., Liu, D., Du, Z., and Zhu, D. (2017). Design and implementation of automatic orthorectification system based on GF-1 big data. Trans. Chin. Soc. Agric. Eng., 33."},{"key":"ref_2","unstructured":"Zeng, C. (2017). The Quality Assessment and Feature Analysis of Domestic High Resolution Satellite Images. [Master\u2019s Thesis, Chengdu University of Technology]."},{"key":"ref_3","unstructured":"Bai, Z. (2013). Technical characteristics of GF-1 remote sensing satellite. Aerosp. China, 5\u20139."},{"key":"ref_4","first-page":"1","article-title":"Image Fusion and Quality Assessment of GF-1","volume":"41","author":"Dong","year":"2016","journal-title":"For. Inventory Plan."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2016.02.019","article-title":"Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data","volume":"177","author":"Jia","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2015.05.009","article-title":"Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations","volume":"106","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"097097","DOI":"10.1117\/1.JRS.9.097097","article-title":"Quantitative evaluation of observation capability of GF-1 wide field of view sensors for soil moisture inversion","volume":"9","author":"Chen","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_8","unstructured":"Wang, M. (2013). Study on the Distributions and Physical Properties of Cirrus clouds. [Master\u2019s Thesis, Nanjing University of Information Science & Technology]."},{"key":"ref_9","unstructured":"Chen, X. (2015). Research on Recognition Technology of Typtical Ground-based Cloud. [Ph.D. Thesis, Southeast University]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cai, W., Liu, Y., Li, M., Cheng, L., and Zhang, C. (2011, January 24\u201326). A self-adaptive homomorphic filter method for removing thin cloud. Proceedings of the 2011 19th International Conference on Geoinformatics, Shanghai, China.","DOI":"10.1109\/GeoInformatics.2011.5980963"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, M., and Tang, H. (2010, January 10\u201312). A modified homomorphism filtering algorithm for cloud removal. Proceedings of the 2010 International Conference on Computational Intelligence and Software Engineering, Wuhan, China.","DOI":"10.1109\/CISE.2010.5677195"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"453","article-title":"Automated detection and removal of clouds and their shadows from Landsat TM images","volume":"82","author":"Wang","year":"1999","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01431168808954841","article-title":"An improved method for detecting clear sky and cloudy radiances from AVHRR data","volume":"9","author":"Saunders","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32141","DOI":"10.1029\/1998JD200032","article-title":"Discriminating clear sky from clouds with MODIS","volume":"103","author":"Ackerman","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_17","first-page":"435","article-title":"Cloud Detection in MODIS Data Based on Spectrum Analysis","volume":"30","author":"Li","year":"2005","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_18","first-page":"1783","article-title":"Cloud Detection over the Southest China Basing on Statistical Analysis","volume":"15","author":"Liu","year":"2005","journal-title":"J. Image Graph."},{"key":"ref_19","first-page":"10","article-title":"Automatic detection and removal of thin haze based on own features of Landsat image","volume":"40","author":"Li","year":"2006","journal-title":"J. Zhejiang Univ. Sci."},{"key":"ref_20","first-page":"35","article-title":"Cloud and Cloud Shadow Detection in GF-1 Imagery Using Single-date Method","volume":"32","author":"Yun","year":"2017","journal-title":"Remote Sens. Inf."},{"key":"ref_21","unstructured":"(2004). Automatic Cloud Detection from MODIS Images."},{"key":"ref_22","unstructured":"Kawano, K., and Kudoh, J.I. (2001, January 9\u201313). Cloud detection method for NOAA AVHRR images by using local area parameters. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217), Sydney, NSW, Australia."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1175\/JAM-2188.1","article-title":"NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling. Part I: Algorithm description","volume":"44","author":"Dybbroe","year":"2005","journal-title":"J. Appl. Meteorol."},{"key":"ref_24","first-page":"33","article-title":"Cloud Detection of MODIS Satellite Images Based on Dynamical Cluster","volume":"4","author":"Liu","year":"2007","journal-title":"Remote. Sens. Inf."},{"key":"ref_25","first-page":"16","article-title":"A multi-spectral remote sensing imagery cloud detection algorithm based on spectral angle principle","volume":"36","author":"Sun","year":"2017","journal-title":"Microcomput. Its Appl."},{"key":"ref_26","first-page":"41","article-title":"A Method for Cloud Interpretation in ZY-3 Satellite Imagery and Its Application","volume":"32","author":"Liu","year":"2017","journal-title":"Remote Sens. Inf."},{"key":"ref_27","first-page":"45","article-title":"Cloud Detection Algorithm for Domestic High-Resolution Multispectral Image Data","volume":"41","author":"Wu","year":"2015","journal-title":"Comput. Netw."},{"key":"ref_28","first-page":"74","article-title":"Cloud Detection and Cloud Phase Retrieval Based on BP Neural Network","volume":"14","author":"Jin","year":"2016","journal-title":"Opt. Optoelectron. Technol."},{"key":"ref_29","first-page":"2184","article-title":"Automatic cloud detection for remote sensing image","volume":"27","author":"Yu","year":"2006","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_30","first-page":"1061","article-title":"A Cloud Detection Algorithm for MODIS Images Combining Kmeans Clustering and Multi-Spectral Thershold Method","volume":"31","author":"Wang","year":"2010","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fu, H., Shen, Y., Liu, J., He, G., Chen, J., Liu, P., Qian, J., and Li, J. (2019). Cloud detection for FY meteorology satellite based on ensemble thresholds and random forests approach. Remote Sens., 11.","DOI":"10.3390\/rs11010044"},{"key":"ref_32","unstructured":"Ye, S. (2016). Research on Application of Remote Sensing Tupu\u2014Take Monitoring of Meteorological Disaster for Example. [Ph.D. Thesis, China Agricultural University]."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ye, S., Liu, D., Yao, X., Tang, H., Xiong, Q., Zhuo, W., Du, Z., Huang, J., Su, W., and Shen, S. (2018). RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness. Remote Sens., 10.","DOI":"10.3390\/rs10091376"},{"key":"ref_34","first-page":"247","article-title":"Principles of the Interaction Between NDVI Profile and the Growing Situation of Crops","volume":"22","author":"Jiang","year":"2002","journal-title":"Acta Ecol. Sin."},{"key":"ref_35","first-page":"155","article-title":"Crop classification based on GF-1\/WFV NDVI time series","volume":"31","author":"Yang","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4105","DOI":"10.1109\/TGRS.2007.905312","article-title":"Cloud-screening algorithm for ENVISAT\/MERIS multispectral images","volume":"45","author":"Guanter","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0034-4257(02)00034-2","article-title":"An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images","volume":"82","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, N., Liu, D., Feng, Q., Xiong, Q., Zhang, L., Ren, T., Zhao, Y., Zhu, D., and Huang, J. (2019). Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sens., 11.","DOI":"10.3390\/rs11121500"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, Z., Liu, D., Xiong, Q., Yang, N., Ren, T., Zhang, C., Zhang, X., and Li, S. (2019). Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability, 11.","DOI":"10.3390\/su11185052"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/450\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:53:39Z","timestamp":1760172819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,1]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030450"],"URL":"https:\/\/doi.org\/10.3390\/rs12030450","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,1]]}}}