{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:19:30Z","timestamp":1760231970570,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41201408","LY16D010009"],"award-info":[{"award-number":["41201408","LY16D010009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Zhejiang Province Natural Science Foundation of China","award":["41201408","LY16D010009"],"award-info":[{"award-number":["41201408","LY16D010009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The quick and precise assessment of rice distribution by remote sensing technology is important for agricultural development. However, mountain rice is limited by the complex terrain, and its distribution is fragmented. Therefore, it is necessary to fully use the abundant spatial, temporal, and spectral information of remote sensing imagery. This study extracted 22 classification features from Sentinel-2 imagery (spectral features, texture features, terrain features, and a custom spectral-spatial feature). A feature selection method based on the optimal extraction period of features (OPFSM) was constructed, and a multitemporal feature combination (MC) was generated based on the separability of different vegetation types in different periods. Finally, the extraction accuracy of MC for mountain rice was explored using Random Forest (RF), CatBoost, and ExtraTrees (ET) machine learning algorithms. The results show that MC improved the overall accuracy (OA) by 3\u20136% when compared to the feature combinations in each rice growth stage, and by 7\u201314% when compared to the original images. MC based on the ET classifier (MC-ET) performed the best for rice extraction, with the OA of 86%, Kappa coefficient of 0.81, and F1 score of 0.95 for rice. The study demonstrated that OPFSM could be used as a reference for selecting multitemporal features, and the MC-ET classification scheme has high application potential for mountain rice extraction.<\/jats:p>","DOI":"10.3390\/rs14205096","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"5096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Multitemporal Mountain Rice Identification and Extraction Method Based on the Optimal Feature Combination and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Kaili","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bokun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Engineering Geophysical Prospecting and Design Institute Co., Ltd., Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang Engineering Geophysical Prospecting and Design Institute Co., Ltd., Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhai, Y., Wang, N., Zhang, L., Hao, L., and Hao, C. (2020). Automatic Crop Classification in Northeastern China by Improved Nonlinear Dimensionality Reduction for Satellite Image Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12172726"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.rse.2017.03.029","article-title":"PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series","volume":"194","author":"Boschetti","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8763","DOI":"10.1080\/01431161.2010.550647","article-title":"Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems","volume":"32","author":"Conrad","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"026038","DOI":"10.1117\/1.JRS.10.026038","article-title":"Root mass ratio: Index derived by assimilation of synthetic aperture radar and the improved World Food Study model for heavy metal stress monitoring in rice","volume":"10","author":"Liu","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhao, W., Chen, S., and Ye, T. (2021). Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series. Remote Sens., 13.","DOI":"10.3390\/rs13204160"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mrinal, S., Wu, B., and Zhang, M. (2016). An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India. Remote Sens., 8.","DOI":"10.3390\/rs8060479"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.3390\/rs2071844","article-title":"Estimating Global Cropland Extent with Multi-year MODIS Data","volume":"2","author":"Pittman","year":"2010","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2012.09.022","article-title":"Landcover classification of the Lower Nhecol\u00e2ndia subregion of the Brazilian Pantanal Wetlands using ALOS\/PALSAR, RADARSAT-2 and ENVISAT\/ASAR imagery","volume":"128","author":"Evans","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2017.12.002","article-title":"The global forest\/non-forest map from TanDEM-X interferometric SAR data","volume":"205","author":"Martone","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1016\/j.rse.2009.04.005","article-title":"Potential of SAR sensors TerraSAR-X, ASAR\/ENVISAT and PALSAR\/ALOS for monitoring sugarcane crops on Reunion Island","volume":"113","author":"Baghdadi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Emile, N., Dinh, H., Nicolas, B., Dominique, C., and Laure, H. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.3390\/rs10081217"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Choi, H., and Jeong, J. (2019). Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform. Remote Sens., 11.","DOI":"10.3390\/rs11101184"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s11769-013-0613-x","article-title":"Review of shadow detection and de-shadowing methods in remote sensing","volume":"23","author":"Shahtahmassebi","year":"2013","journal-title":"Chin. Geogr. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.rse.2018.08.011","article-title":"Extending RAPID model to simulate forest microwave backscattering","volume":"217","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1007\/s11769-009-0186-x","article-title":"Remote sensing monitoring of tobacco field based on phenological characteristics and time series image\u2014A case study of Chengjiang County, Yunnan Province, China","volume":"19","author":"Peng","year":"2009","journal-title":"Chin. Geogr. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, G., Liu, X., and Liu, M. (2019). Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation. Remote Sens., 11.","DOI":"10.3390\/rs11030268"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111660","DOI":"10.1016\/j.rse.2020.111660","article-title":"Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series","volume":"239","author":"Taymans","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/01431160903464179","article-title":"Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005","volume":"32","author":"Biradar","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4841","DOI":"10.1080\/01431161.2011.635715","article-title":"The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa","volume":"33","author":"Sibanda","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2019.01.019","article-title":"Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region","volume":"223","author":"Astola","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Franch, B., Bautista, A.S., Fita, D., Rubio, C., Tarraz\u00f3-Serrano, D., S\u00e1nchez, A., Skakun, S., Vermote, E., Becker-Reshef, I., and Uris, A. (2021). Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13204095"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, J., Jin, X., Ren, J., Liu, J., Liang, X., and Zhou, Y. (2021). Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13071245"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6157","DOI":"10.1080\/01431161.2020.1736730","article-title":"Spectral-spatial hyperspectral image classification based on superpixel and multi-classifier fusion","volume":"41","author":"Cui","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3185","DOI":"10.1109\/TCYB.2020.3004263","article-title":"Spectral-Spatial Weighted Kernel Manifold Embedded Distribution Alignment for Remote Sensing Image Classification","volume":"51","author":"Dong","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","first-page":"372","article-title":"Enhanced land use\/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity","volume":"23","author":"Mas","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1109\/TGRS.2008.2002577","article-title":"An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery","volume":"46","author":"Huang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/TGRS.2006.885412","article-title":"Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data","volume":"45","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1080\/01431161.2015.1079665","article-title":"Feature extraction using median\u2013mean and feature line embedding","volume":"36","author":"Imani","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, J., Pan, Z., Wang, H., Huang, P., Sun, J., Qin, F., and Liu, Z. (2019). An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery. Sensors, 19.","DOI":"10.3390\/s19092087"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109057","DOI":"10.1016\/j.agrformet.2022.109057","article-title":"Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China","volume":"323","author":"Cheng","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Guo, H., Zhang, L., Liang, D., Liu, X., Wan, X., and Liu, J. (2021). Tropical Forests Classification Based on Weighted Separation Index from Multi-Temporal Sentinel-2 Images in Hainan Island. Sustainability, 13.","DOI":"10.3390\/su132313348"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"053547","DOI":"10.1117\/1.3619838","article-title":"Mapping rice areas of South Asia using MODIS multitemporal data","volume":"5","author":"Gumma","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s12524-019-01062-x","article-title":"Modeling Biophysical Variables and Land Surface Temperature Using the GWR Model: Case Study\u2014Tehran and Its Satellite Cities","volume":"48","author":"Alibakhshi","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kupidura, P. (2019). The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101233"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, C., Huang, W., Tang, J., Tian, Y., and Zhang, Q. (2020). Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest. Remote Sens., 12.","DOI":"10.3390\/rs12213504"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, S., Gu, L., Li, X., Jiang, T., and Ren, R. (2020). Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12193119"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2018.09.016","article-title":"Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery","volume":"219","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2004.03.001","article-title":"Optimal classification methods for mapping agricultural tillage practices","volume":"91","author":"South","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2011.11.008","article-title":"Identifying plant species using mid-wave infrared (2.5\u20136\u03bcm) and thermal infrared (8\u201314\u03bcm) emissivity spectra","volume":"118","author":"Ullah","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_43","unstructured":"Hall, M.A. (1998, January 4\u20136). Practical feature subset selection for machine learning. Proceedings of the 21st Australasian Computer Science Conference ACSC\u201998, Perth, Australia."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.compchemeng.2019.06.001","article-title":"Formation lithology classification using scalable gradient boosted decision trees","volume":"128","author":"Dev","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.3390\/rs12121973","article-title":"Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles","volume":"12","author":"Samat","year":"2020","journal-title":"Remote Sens."},{"key":"ref_46","first-page":"183","article-title":"Comparing measures of sample skewness and kurtosis","volume":"47","author":"Joanes","year":"1998","journal-title":"J. R. Stat. Soc. (Ser. D)"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tu, B., Li, N., Fang, L., He, D., and Ghamisi, P. (2019). Hyperspectral Image Classification with Multi-Scale Feature Extraction. Remote Sens., 11.","DOI":"10.3390\/rs11050534"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Liu, Q., Hang, R., and Liu, G. (2017). Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images. Remote Sens., 9.","DOI":"10.3390\/rs9050506"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TCYB.2016.2605044","article-title":"Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images","volume":"48","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1080\/2150704X.2018.1523581","article-title":"A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder","volume":"10","author":"Teffahi","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"015010","DOI":"10.1117\/1.JRS.11.015010","article-title":"Hyperspectral image classification based on joint sparsity model with low-dimensional spectral\u2013spatial features","volume":"11","author":"Wang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1080\/01431161.2011.614287","article-title":"A multiscale urban complexity index based on 3D wavelet transform for spectral-spatial feature extraction and classification: An evaluation on the 8-channel WorldView-2 imagery","volume":"33","author":"Huang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1080\/2150704X.2021.1895448","article-title":"Slow feature extraction for hyperspectral image classification","volume":"12","author":"Liu","year":"2021","journal-title":"Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TGRS.2012.2209657","article-title":"Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features","volume":"51","author":"Qian","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1109\/TGRS.2011.2157166","article-title":"Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification","volume":"49","author":"Linlin","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.bja.2019.10.017","article-title":"Deep learning for risk assessment: All about automatic feature extraction","volume":"124","author":"Cosgriff","year":"2020","journal-title":"Br. J. Anaesth."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","article-title":"A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images","volume":"10","author":"Li","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"16","DOI":"10.2112\/JCR-SI111-003.1","article-title":"A Deep Learning Approach to Detecting Ships from High-Resolution Aerial Remote Sensing Images","volume":"111","author":"Huang","year":"2020","journal-title":"J. Coast. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jprocont.2019.08.006","article-title":"DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology","volume":"84","author":"Maggipinto","year":"2019","journal-title":"J. Process Control"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1080\/01431161.2018.1475779","article-title":"Object-based feature selection for crop classification using multi-temporal high-resolution imagery","volume":"40","author":"Song","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112389","DOI":"10.1016\/j.engstruct.2021.112389","article-title":"Failure mode identification of column base plate connection using data-driven machine learning techniques","volume":"240","author":"Kabir","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.energy.2018.08.207","article-title":"Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression","volume":"164","author":"Ahmad","year":"2018","journal-title":"Energy"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"126593","DOI":"10.1016\/j.chemosphere.2020.126593","article-title":"Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples","volume":"252","author":"Kibbey","year":"2020","journal-title":"Chemosphere"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ. Interdiscip. J."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.asr.2018.09.018","article-title":"Object-based rice mapping using time-series and phenological data","volume":"63","author":"Meng","year":"2019","journal-title":"Adv. Space Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5096\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:52:48Z","timestamp":1760143968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,12]]},"references-count":65,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205096"],"URL":"https:\/\/doi.org\/10.3390\/rs14205096","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,12]]}}}