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Big data-driven spectral index construction for fine-scale salt pond mapping

文献类型：期刊论文

作者Zhang, Jin1,3; Huang, Chong3; Li, He3; Wang, Shuxuan1,3; Liu, Qingsheng3; Tang, Xiaoya1,3; Zhang, Chenchen2; Su, Fenzhen1,3
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2026-02-01
卷号146页码:105139
关键词Salt ponds mapping Spectral indices Big data-driven Random Forest Sentinel-2 Bohai Rim region
ISSN号1569-8432
DOI10.1016/j.jag.2026.105139
产权排序1
文献子类Article
英文摘要Accurate mapping of salt pond systems is crucial for coastal resource management and ecological conservation. However, existing spectral indices for salt pond detection are mostly derived empirically, limiting their ability to differentiate pond subtypes or generalize across diverse coastal environments. This study proposes a big data-driven framework for constructing novel spectral indices that enhance the fine-scale classification of salt ponds in the Bohai Rim region of China. By systematically generating and screening over 390,000 candidate indices derived from Sentinel-2 bands, four new indices were identified to effectively discriminate salt pond subtypes: the Non-Water Body Detection Index (NWBDI), Crystallization Pond Index (CPI), Red-Green Concentration Pond Index (RGCPI), and Evaporation Pond Discrimination Index (EPDI). To rigorously evaluate their performance, three Random Forest configurations were compared, including a baseline model with original spectral bands, an expert-index-enhanced model, and a model incorporating the proposed novel indices. Across the Bohai Rim region, the model with proposed new indices achieved an overall accuracy (OA) of 81.14%, demonstrating clear advantages over both original spectral bands (OA of 72.93%) and expert-index approaches (OA of 76.64%). The resulting fine-scale map revealed a total salt pond area of 2,214.33 km2 in 2020 across the Bohai Rim, consistent with national statistics. Beyond improving classification accuracy, the data-driven index discovery revealed physically meaningful spectral relationships linked to salinity gradients and hydrobiogeochemical properties, marking a methodological shift from expert-driven hypothesis testing to automated, data-driven feature discovery. This study demonstrates that the data-driven approach can provide a transferable solution for constructing task-specific spectral indices and advancing large-scale environmental monitoring.
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WOS关键词DIVERSITY ; NDWI ; SEA
WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:001683173500001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/221013]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Huang, Chong
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;
2.Univ Oklahoma, Ctr Earth Observat & Modeling, Sch Biol Sci, Norman, OK 73019 USA
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Zhang, Jin,Huang, Chong,Li, He,et al. Big data-driven spectral index construction for fine-scale salt pond mapping[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2026,146:105139.
APA Zhang, Jin.,Huang, Chong.,Li, He.,Wang, Shuxuan.,Liu, Qingsheng.,...&Su, Fenzhen.(2026).Big data-driven spectral index construction for fine-scale salt pond mapping.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,146,105139.
MLA Zhang, Jin,et al."Big data-driven spectral index construction for fine-scale salt pond mapping".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 146(2026):105139.

入库方式： OAI收割

来源：地理科学与资源研究所

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