{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:59:45Z","timestamp":1772798385687,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xinjiang Uyghur Autonomous Region Key R&amp;D Special Project \u2018Construction of a Forest and Fruit Resource Data System Based on \u201cSpace, Sky, and Earth\u201d Multisource Remote Sensing Monitoring Technology\u2019","award":["20222101536"],"award-info":[{"award-number":["20222101536"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With China\u2019s fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments.<\/jats:p>","DOI":"10.3390\/rs15174140","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"4140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Optimized Extraction Method of Fruit Planting Distribution Based on Spectral and Radar Data Fusion of Key Time Phase"],"prefix":"10.3390","volume":"15","author":[{"given":"Guobing","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"},{"name":"Institute of Modern Forestry, Xinjiang Academy of Forestry Sciences, Urumqi 830063, China"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Modern Forestry, Xinjiang Academy of Forestry Sciences, Urumqi 830063, China"}]},{"given":"Jianghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Nigela","family":"Tuerxun","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Wanqiang","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Liang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109749","DOI":"10.1016\/j.scienta.2020.109749","article-title":"A Review of Plant Breeders\u2019 Rights Application and Granting for Fruit Trees in China from 2000 to 2019","volume":"276","author":"Meng","year":"2021","journal-title":"Sci. Hortic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104276","DOI":"10.1016\/j.landusepol.2019.104276","article-title":"Quantifying the Net Benefit of Land Use of Fruit Trees in China","volume":"90","author":"Xia","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11549","DOI":"10.1038\/s41598-022-15414-0","article-title":"Research on Remote Sensing Classification of Fruit Trees Based on Sentinel-2 Multi-Temporal Imageries","volume":"12","author":"Zhou","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gao, W., Qiu, Q., Yuan, C., Shen, X., Cao, F., Wang, G., and Wang, G. (2022). Forestry Big Data: A Review and Bibliometric Analysis. Forests, 13.","DOI":"10.3390\/f13101549"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for Geo-Big Data Applications: A Meta-Analysis and Systematic Review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1016\/S2095-3119(17)61859-8","article-title":"Agricultural Remote Sensing Big Data: Management and Applications","volume":"17","author":"Huang","year":"2018","journal-title":"J. Integr. Agric."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of Studies on Tree Species Classification from Remotely Sensed Data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dainelli, R., Toscano, P., Di Gennaro, S.F., and Matese, A. (2021). Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing\u2014A Systematic Review. Part II: Research Applications. Forests, 12.","DOI":"10.3390\/f12040397"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone Remote Sensing for Forestry Research and Practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. For. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, Q., Zhang, J., Guo, S., Ye, Z., Deng, H., Hou, X., and Zhang, H. (2022). Urban Tree Classification Based on Object-Oriented Approach and Random Forest Algorithm Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14163885"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, C., Wang, Z., Li, T., Li, J., and Zhao, G. (2022). Identification of Cultivated Land Quality Grade Using Fused Multi-Source Data and Multi-Temporal Crop Remote Sensing Information. Remote Sens., 14.","DOI":"10.3390\/rs14092109"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pan, L., Xia, H., Zhao, X., Guo, Y., and Qin, Y. (2021). Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7\/8 Images, and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132510"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107275","DOI":"10.1016\/j.compag.2022.107275","article-title":"Predicting Individual Apple Tree Yield Using UAV Multi-Source Remote Sensing Data and Ensemble Learning","volume":"201","author":"Chen","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"153559","DOI":"10.1016\/j.scitotenv.2022.153559","article-title":"Machine Learning in Modelling Land-Use and Land Cover-Change (LULCC): Current Status, Challenges and Prospects","volume":"822","author":"Wang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2020.2968550","article-title":"Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification","volume":"18","author":"Cao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108529","DOI":"10.1016\/j.ecolind.2021.108529","article-title":"Classification of Zambian Grasslands Using Random Forest Feature Importance Selection during the Optimal Phenological Period","volume":"135","author":"Zhao","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"},{"key":"ref_23","first-page":"1","article-title":"Land-Use\/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112308","DOI":"10.1016\/j.rse.2021.112308","article-title":"Change Detection Using Deep Learning Approach with Object-Based Image Analysis","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series over Large Areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bao, J., Li, J., Wang, G., Tang, Z., and Zhi, J. (2022). Branch Growth, Leaf Canopies and Photosynthetic Responses of Zizyphus Jujube Cv. \u201cHuizao\u201d to Nutrient Addition in the Arid Areas of Northwest China. Diversity, 14.","DOI":"10.3390\/d14110914"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103957","DOI":"10.1016\/j.earscirev.2022.103957","article-title":"Recent Climate and Hydrological Changes in a Mountain\u2013Basin System in Xinjiang, China","volume":"226","author":"Yao","year":"2022","journal-title":"Earth-Sci. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, H., Yang, J., Xia, G., and Lin, T. (2022). Spatio-Temporal Differentiation of Coupling Coordination between Ecological Footprint and Ecosystem Service Functions in the Aksu Region, Xinjiang, China. Sustainability, 14.","DOI":"10.3390\/su14063483"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1093\/ee\/nvy063","article-title":"Intercropping With Fruit Trees Increases Population Abundance and Alters Species Composition of Spider Mites on Cotton","volume":"47","author":"Li","year":"2018","journal-title":"Environ. Entomol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J. (2021). Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13101954"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Duan, Q., Tan, M., Guo, Y., Wang, X., and Xin, L. (2019). Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine. Forests, 10.","DOI":"10.3390\/f10090729"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, Z., Xu, R., Du, W., Wang, L., and Lu, D. (2019). High-Resolution Urban Land Mapping in China from Sentinel 1A\/2 Imagery Based on Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11070752"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111814","DOI":"10.1016\/j.rse.2020.111814","article-title":"Sentinel-1 Time Series Data for Monitoring the Phenology of Winter Wheat","volume":"246","author":"Schlund","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"112381","DOI":"10.1016\/j.rse.2021.112381","article-title":"A Review of Geostatistical Simulation Models Applied to Satellite Remote Sensing: Methods and Applications","volume":"259","author":"Zakeri","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.geomorph.2014.03.008","article-title":"High-Resolution Topography for Understanding Earth Surface Processes: Opportunities and Challenges","volume":"216","author":"Tarolli","year":"2014","journal-title":"Geomorphology"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.isprsjprs.2013.11.009","article-title":"A Practical Method for SRTM DEM Correction over Vegetated Mountain Areas","volume":"87","author":"Su","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01431160902897858","article-title":"A Comparison of MODIS 250-m EVI and NDVI Data for Crop Mapping: A Case Study for Southwest Kansas","volume":"31","author":"Wardlow","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rado\u010daj, D., \u0160iljeg, A., Marinovi\u0107, R., and Juri\u0161i\u0107, M. (2023). State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review. Agriculture, 13.","DOI":"10.3390\/agriculture13030707"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual Polarimetric Radar Vegetation Index for Crop Growth Monitoring Using Sentinel-1 SAR Data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cheng, X., Liu, W., Zhou, J., Wang, Z., Zhang, S., and Liao, S. (2022). Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization. Agronomy, 12.","DOI":"10.3390\/agronomy12081948"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101853","DOI":"10.1016\/j.ecoinf.2022.101853","article-title":"An Algorithm for Early Rice Area Mapping from Satellite Remote Sensing Data in Southwestern Guangdong in China Based on Feature Optimization and Random Forest","volume":"72","author":"Liu","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/JSTARS.2023.3283011","article-title":"The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction","volume":"16","author":"Liang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/LGRS.2017.2745049","article-title":"A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values","volume":"14","author":"Behnamian","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7053","DOI":"10.1007\/s00500-016-2247-2","article-title":"SVM or Deep Learning? A Comparative Study on Remote Sensing Image Classification","volume":"21","author":"Liu","year":"2017","journal-title":"Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Razaque, A., Ben Haj Frej, M., Almi\u2019ani, M., Alotaibi, M., and Alotaibi, B. (2021). Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification. Sensors, 21.","DOI":"10.3390\/s21134431"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1007\/s11769-006-0282-0","article-title":"Review of Remotely Sensed Imagery Classification Patterns Based on Object-Oriented Image Analysis","volume":"16","author":"Liu","year":"2006","journal-title":"Chin. Geogr. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Luo, C., Qi, B., Liu, H., Guo, D., Lu, L., Fu, Q., and Shao, Y. (2021). Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13040561"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, L., Abubakar, G.A., and Huang, J. (2021). High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13061148"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tu, Y., Chen, B., Zhang, T., and Xu, B. (2020). Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sens., 12.","DOI":"10.3390\/rs12071058"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A Comparison of Random Forest and Its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cheng, K., and Wang, J. (2019). Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm\u2014A Case Study in the Qinling Mountains. Forests, 10.","DOI":"10.3390\/f10070559"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aca.2014.03.039","article-title":"A Comparative Investigation of Modern Feature Selection and Classification Approaches for the Analysis of Mass Spectrometry Data","volume":"829","author":"Gromski","year":"2014","journal-title":"Anal. Chim. Acta"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Cai, L., Shi, W., Miao, Z., and Hao, M. (2018). Accuracy Assessment Measures for Object Extraction from Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10020303"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the Unsuitability of the Kappa Coefficient in the Assessment and Comparison of the Accuracy of Thematic Maps Obtained by Image Classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the Accuracy of Species Distribution Models: Prevalence, Kappa and the True Skill Statistic (TSS): Assessing the Accuracy of Distribution Models","volume":"43","author":"Allouche","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chabalala, Y., Adam, E., and Ali, K.A. (2022). Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes. Remote Sens., 14.","DOI":"10.3390\/rs14112621"},{"key":"ref_62","first-page":"100776","article-title":"Tree-Fruits Crop Type Mapping from Sentinel-1 and Sentinel-2 Data Integration in Egypt\u2019s New Delta Project","volume":"27","author":"Nabil","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Tian, H., Fang, X., Lan, Y., Ma, C., Huang, H., Lu, X., Zhao, D., Liu, H., and Zhang, Y. (2022). Extraction of Citrus Trees from UAV Remote Sensing Imagery Using YOLOv5s and Coordinate Transformation. Remote Sens., 14.","DOI":"10.3390\/rs14174208"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Adugna, T., Xu, W., and Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sens., 14.","DOI":"10.3390\/rs14030574"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zafari, A., Zurita-Milla, R., and Izquierdo-Verdiguier, E. (2019). Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. Remote Sens., 11.","DOI":"10.3390\/rs11050575"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"297","DOI":"10.5589\/m09-015","article-title":"Characterizing Urban Surface Cover and Structure with Airborne Lidar Technology","volume":"35","author":"Goodwin","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2014.11.027","article-title":"Eastern Europe\u2019s Forest Cover Dynamics from 1985 to 2012 Quantified from the Full Landsat Archive","volume":"159","author":"Potapov","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1111\/gcb.12838","article-title":"Mapping Global Cropland and Field Size","volume":"21","author":"Fritz","year":"2015","journal-title":"Glob. Chang. Biol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:41:07Z","timestamp":1760128867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,23]]},"references-count":68,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174140"],"URL":"https:\/\/doi.org\/10.3390\/rs15174140","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,23]]}}}