{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:29:29Z","timestamp":1775327369878,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFB3900503"],"award-info":[{"award-number":["2021YFB3900503"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Bare land, as a significant land cover type on the Earth\u2019s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth\u2019s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.<\/jats:p>","DOI":"10.3390\/rs16091534","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T08:18:27Z","timestamp":1714119507000},"page":"1534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8014-434X","authenticated-orcid":false,"given":"Chaokang","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, Yanqi Lake Campus, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6084-1889","authenticated-orcid":false,"given":"Qinjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, Yanqi Lake Campus, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Kashi Aerospace Information Research Institute, Kashi 844199, China"},{"name":"Key Laboratory of the Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"}]},{"given":"Jingyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, Yanqi Lake Campus, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Wentao","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, Yanqi Lake Campus, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Boqi","family":"Yuan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, Yanqi Lake Campus, University of Chinese Academy of Sciences, Beijing 101408, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, M., Huang, H., Li, Z., Hackman, K.O., Liu, C., Andriamiarisoa, R.L., Ny Aina Nomenjanahary Raherivelo, T., Li, Y., and Gong, P. (2020). Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12213663"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yalew, S., Mul, M., Van Griensven, A., Teferi, E., Priess, J., Schweitzer, C., and Van Der Zaag, P. (2016). Land-Use Change Modelling in the Upper Blue Nile Basin. Environments, 3.","DOI":"10.3390\/environments3030021"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mahmoud, S.H., and Alazba, A.A. (2015). Hydrological Response to Land Cover Changes and Human Activities in Arid Regions Using a Geographic Information System and Remote Sensing. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125805"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"127321","DOI":"10.1016\/j.jclepro.2021.127321","article-title":"Evaluating the Potential Impacts of Land Use Changes on Ecosystem Service Value under Multiple Scenarios in Support of SDG Reporting: A Case Study of the Wuhan Urban Agglomeration","volume":"307","author":"Peng","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, Y., Tsou, J., and Li, Y. (2017). Surface Urban Heat Island Analysis of Shanghai (China) Based on the Change of Land Use and Land Cover. Sustainability, 9.","DOI":"10.3390\/su9091538"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2014.09.023","article-title":"Detecting Change in Urban Areas at Continental Scales with MODIS Data","volume":"158","author":"Mertes","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global Consequences of Land Use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kuenzer, C., Heimhuber, V., Huth, J., and Dech, S. (2019). Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions\u2014A Review. Remote Sens., 11.","DOI":"10.3390\/rs11171985"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e13746","DOI":"10.1016\/j.heliyon.2023.e13746","article-title":"Impact of Land Use Land Cover Change Using Remote Sensing with Integration of Socio-Economic Data on Rural Livelihoods in the Nashe Watershed, Ethiopia","volume":"9","author":"Fikadu","year":"2023","journal-title":"Heliyon"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nguyen, C.T., Chidthaisong, A., Kieu Diem, P., and Huo, L.-Z. (2021). A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. Land, 10.","DOI":"10.3390\/land10030231"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Kh\u00e9lifi, N., Mezghani, A., and Heggy, E. (2019). Patterns and Mechanisms of Climate, Paleoclimate and Paleoenvironmental Changes from Low-Latitude Regions, Springer International Publishing. Advances in Science, Technology & Innovation.","DOI":"10.1007\/978-3-030-01599-2"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s40899-024-01033-2","article-title":"Contribution to the Land Suitability Analysis for Potential Surface Irrigation Development Using Remote Sensing and GIS-MCE of the Soroka Watershed, Northwestern Ethiopia","volume":"10","author":"Tesfaye","year":"2024","journal-title":"Sustain. Water Resour. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"168854","DOI":"10.1016\/j.scitotenv.2023.168854","article-title":"Study on the Relationship between Regional Soil Desertification and Salinization and Groundwater Based on Remote Sensing Inversion: A Case Study of the Windy Beach Area in Northern Shaanxi","volume":"912","author":"Liu","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sorenson, P.T., Kiss, J., Bedard-Haughn, A.K., and Shirtliffe, S. (2022). Multi-Horizon Predictive Soil Mapping of Historical Soil Properties Using Remote Sensing Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14225803"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lu, C., Mao, J., Pang, J., Liu, Z., and Hou, M. (2021). Comprehensive Evaluation of the Importance of Ecological Land in Arid Hilly Cities in Northwest China: A Case Study of the Core Urban Area of Lanzhou. Land, 10.","DOI":"10.3390\/land10090942"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106265","DOI":"10.1016\/j.catena.2022.106265","article-title":"NDBSI: A Normalized Difference Bare Soil Index for Remote Sensing to Improve Bare Soil Mapping Accuracy in Urban and Rural Areas","volume":"214","author":"Liu","year":"2022","journal-title":"Catena"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Enoguanbhor, E., Gollnow, F., Nielsen, J., Lakes, T., and Walker, B. (2019). Land Cover Change in the Abuja City-Region, Nigeria: Integrating GIS and Remotely Sensed Data to Support Land Use Planning. Sustainability, 11.","DOI":"10.3390\/su11051313"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1325516","DOI":"10.3389\/fenvs.2024.1325516","article-title":"Ecological Environment Quality Assessment and Spatial Autocorrelation of Northern Shaanxi Mining Area in China Based-on Improved Remote Sensing Ecological Index","volume":"12","author":"Zhu","year":"2024","journal-title":"Front. Environ. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zou, Q., Yu, W., and Bao, Z. (2023). A Blockchain Solution for Remote Sensing Data Management Model. Appl. Sci., 13.","DOI":"10.20944\/preprints202308.0178.v1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, H., Jiang, H., Gu, X., Peng, J., Li, W., Hong, L., and Tao, C. (2020). CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification. Sensors, 20.","DOI":"10.3390\/s20041226"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4252","DOI":"10.1109\/JSTARS.2019.2908515","article-title":"Urban Observation: Integration of Remote Sensing and Social Media Data","volume":"12","author":"Qi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chi, J., Lee, H., Hong, S.G., and Kim, H.-C. (2021). Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula. Remote Sens., 13.","DOI":"10.3390\/rs13132470"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7919","DOI":"10.1109\/JSTARS.2023.3308051","article-title":"Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data","volume":"16","author":"Chen","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","first-page":"3125414","article-title":"Land Use Classification Using Improved U-Net in Remote Sensing Images of Urban and Rural Planning Monitoring","volume":"2022","author":"Xie","year":"2022","journal-title":"Sci. Program."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/2150704X.2017.1420265","article-title":"Application of a Parallel Spectral\u2013Spatial Convolution Neural Network in Object-Oriented Remote Sensing Land Use Classification","volume":"9","author":"Cui","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ali, U., Esau, T.J., Farooque, A.A., Zaman, Q.U., Abbas, F., and Bilodeau, M.F. (2022). Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11060333"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105066","DOI":"10.1016\/j.jaridenv.2023.105066","article-title":"A Regional, Remote Sensing-Based Approach to Mapping Land Degradation in the Little Karoo, South Africa","volume":"219","author":"Kirsten","year":"2023","journal-title":"J. Arid Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8428","DOI":"10.1080\/01431161.2020.1779378","article-title":"Mapping of Rice Growth Phases and Bare Land Using Landsat-8 OLI with Machine Learning","volume":"41","author":"Ramadhani","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shi, Y., Qi, Z., Liu, X., Niu, N., and Zhang, H. (2019). Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data. Remote Sens., 11.","DOI":"10.3390\/rs11222719"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, C., Liu, Y., Wang, D., Liu, S., Yu, L., and Ren, Y. (2023). Automatic Extraction of Bare Soil Land from High-Resolution Remote Sensing Images Based on Semantic Segmentation with Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15061646"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, L., Tang, X., Gan, Y., You, S., Luo, Z., Du, L., and He, Y. (2022). Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection. Remote Sens., 15.","DOI":"10.3390\/rs15010217"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8169","DOI":"10.1007\/s00521-020-04931-6","article-title":"Improved Convolutional Neural Network in Remote Sensing Image Classification","volume":"33","author":"Xu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_33","first-page":"1","article-title":"Multiattribute Sample Learning for Hyperspectral Image Classification Using Hierarchical Peak Attribute Propagation","volume":"71","author":"Tu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.ecolind.2015.03.037","article-title":"Classification and Change Detection of Built-up Lands from Landsat-7 ETM+ and Landsat-8 OLI\/TIRS Imageries: A Comparative Assessment of Various Spectral Indices","volume":"56","author":"Estoque","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2012.09.009","article-title":"BCI: A Biophysical Composition Index for Remote Sensing of Urban Environments","volume":"127","author":"Deng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/0034-4257(88)90031-4","article-title":"Relative Sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for Vegetation and Desertification Monitoring","volume":"24","author":"Becker","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","first-page":"39","article-title":"Tropical Forest Cover Density Mapping","volume":"43","author":"Rikimaru","year":"2002","journal-title":"Trop. Ecol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"322","DOI":"10.5589\/m02-013","article-title":"Determination of the Overall Soil Erosion Potential in the Nsikazi District (Mpumalanga Province, South Africa) Using Remote Sensing and GIS","volume":"28","author":"Wentzel","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., Schaepman, M.E., and De Jong, R. (2017). Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_42","first-page":"40","article-title":"RNDSI: A Ratio Normalized Difference Soil Index for Remote Sensing of Urban\/Suburban Environments","volume":"39","author":"Deng","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.3390\/rs4102957","article-title":"Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area","volume":"4","author":"Adnyana","year":"2012","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rasul, A., Balzter, H., Ibrahim, G., Hameed, H., Wheeler, J., Adamu, B., Ibrahim, S., and Najmaddin, P. (2018). Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates. Land, 7.","DOI":"10.3390\/land7030081"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Qi, L., Shi, P., Dvorakova, K., Van Oost, K., Sun, Q., Yu, H., and Van Wesemael, B. (2023). Detection of Soil Erosion Hotspots in the Croplands of a Typical Black Soil Region in Northeast China: Insights from Sentinel-2 Multispectral Remote Sensing. Remote Sens., 15.","DOI":"10.5194\/egusphere-egu23-4089"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rukhovich, D.I., Koroleva, P.V., Rukhovich, A.D., and Komissarov, M.A. (2022). Informativeness of the Long-Term Average Spectral Characteristics of the Bare Soil Surface for the Detection of Soil Cover Degradation with the Neural Network Filtering of Remote Sensing Data. Remote Sens., 15.","DOI":"10.3390\/rs15010124"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, C., Zhong, C., Su, A., Xiong, C., Wang, J., and Liu, J. (2017). Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sens., 9.","DOI":"10.3390\/rs9030249"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/BF02370415","article-title":"Specific Root Area: A Soil Characteristic","volume":"119","author":"Oja","year":"1989","journal-title":"Plant Soil"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"619","DOI":"10.14358\/PERS.69.6.619","article-title":"Remote- and Ground-Based Sensor Techniques to Map Soil Properties","volume":"69","author":"Barnes","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"016018","DOI":"10.1117\/1.JRS.10.016018","article-title":"Integrating Seasonal Optical and Thermal Infrared Spectra to Characterize Urban Impervious Surfaces with Extreme Spectral Complexity: A Shanghai Case Study","volume":"10","author":"Wang","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0065-2113(02)75005-0","article-title":"Quantitative Remote Sensing of Soil Properties","volume":"Volume 75","year":"2002","journal-title":"Advances in Agronomy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating Impervious Surface Distribution by Spectral Mixture Analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.07.021","article-title":"Synergies between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey Mission","volume":"117","author":"Roberts","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108230","DOI":"10.1016\/j.ecolind.2021.108230","article-title":"Synthesized Remote Sensing-Based Desertification Index Reveals Ecological Restoration and Its Driving Forces in the Northern Sand-Prevention Belt of China","volume":"131","author":"Chen","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/JSTARS.2023.3326958","article-title":"Change Detection Enhanced by Spatial-Temporal Association for Bare Soil Land Using Remote Sensing Images","volume":"17","author":"Wu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","first-page":"5789","article-title":"Monitoring Grassland Desertification in Zoige County Using Landsat and UAV Image","volume":"30","author":"Tu","year":"2021","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, B., Teng, S., Liu, B., Zhang, Z., and Li, Y. (2023). Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape. Remote Sens., 15.","DOI":"10.3390\/rs15204948"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111968","DOI":"10.1016\/j.rse.2020.111968","article-title":"Landsat 9: Empowering Open Science and Applications through Continuity","volume":"248","author":"Masek","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"117638","DOI":"10.1016\/j.envres.2023.117638","article-title":"Monitoring of Wetland Turbidity Using Multi-Temporal Landsat-8 and Landsat-9 Satellite Imagery in the Bisalpur Wetland, Rajasthan, India","volume":"241","author":"Singh","year":"2024","journal-title":"Environ. Res."},{"key":"ref_60","first-page":"300","article-title":"Thresholding and Fuzzy Rule-Based Classification Approaches in Handling Mangrove Forest Mixed Pixel Problems Associated with in QuickBird Remote Sensing Image Analysis","volume":"2","author":"Mohd","year":"2012","journal-title":"Int. J. Agric. For."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mzid, N., Pignatti, S., Huang, W., and Casa, R. (2021). An Analysis of Bare Soil Occurrence in Arable Croplands for Remote Sensing Topsoil Applications. Remote Sens., 13.","DOI":"10.3390\/rs13030474"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.patrec.2008.10.003","article-title":"Optimal Multi-Level Thresholding Using a Two-Stage Otsu Optimization Approach","volume":"30","author":"Huang","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1016\/j.patrec.2004.10.003","article-title":"Optimal Multi-Thresholding Using a Hybrid Optimization Approach","volume":"26","author":"Zahara","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.asoc.2017.01.019","article-title":"Multi-Objective and Multi-Level Image Thresholding Based on Dominance and Diversity Criteria","volume":"54","author":"Yin","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/36.297984","article-title":"Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies","volume":"32","author":"Kaufman","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/36.739156","article-title":"A Comparative Evaluation of NOAA\/AVHRR Vegetation Indexes for Burned Surface Detection and Mapping","volume":"37","author":"Pereira","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The Use of Multiple Measurements in Taxonomic Problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_69","unstructured":"Powers, D. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"41224","DOI":"10.1109\/ACCESS.2018.2857405","article-title":"Combinational Biophysical Composition Index (CBCI) for Effective Mapping Biophysical Composition in Urban Areas","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:33:52Z","timestamp":1760106832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":70,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091534"],"URL":"https:\/\/doi.org\/10.3390\/rs16091534","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}