本数据集围绕中国西北典型干旱区土壤盐渍化问题，基于野外实地采样与实验室分析，系统整理了盐渍化土壤含盐量与地表高光谱观测数据。研究区域包括甘肃省高台县、甘肃省景泰县和青海省格尔木市，分别代表河西走廊、黄河灌溉区和柴达木盆地等不同自然地理与盐渍化成因背景。数据集包含地表 0–10 cm 土壤样点的空间位置（经度、纬度、海拔）、土壤电导率、八大可溶性盐离子(K+, Na+, Ca2+, Mg2+, Cl-, SO42-, CO32-, HCO3-浓度，以及 350–1075 nm波段范围内的地表实测高光谱反射率数据。数据以样点尺度组织，时间覆盖 2020—2022 年，变量命名规范、单位统一。与同类数据相比，本数据集兼具多区域代表性、高光谱实测精度和完整的土壤盐分信息，可为干旱区土壤盐渍化遥感反演、区域差异分析和生态环境研究提供基础数据支撑。", "ds_source": "
本数据集为野外观测与实验室分析获得的原始科研数据。高光谱数据通过野外实地观测获取，使用 ASD FieldSpec HandHeld 2 和 PSR-3500 便携式地物高光谱仪，在样点现场测量土壤地表反射率。土壤样品同步采集并带回实验室，采用规范的土壤理化分析流程测定电导率和可溶性盐离子含量。数据采集过程覆盖样点布设、野外光谱测量、土壤采样与实验室分析等环节，数据来源清晰、过程可追溯。", "ds_process_way": "
数据加工以整理和规范化处理为主，不涉及模型反演或复杂数学运算。野外高光谱数据经白板校正后导出为地表反射率，并与样点编号一一对应。土壤属性数据由实验室测定结果录入电子表格，经人工核查后统一单位和字段名称。高光谱波段采用“R_波长（nm）”方式命名，反射率为无量纲量；土壤离子浓度单位为mg/L，电导率单位为 dS/m。数据未进行插值、平滑或统计推算，保留原始观测特征。", "ds_quality": "
数据质量主要由观测仪器性能、实验室分析规范性和数据整理过程共同保障。野外高光谱观测在稳定天气条件下进行，统一观测时间，并通过白板校正和重复测量降低随机误差。土壤样品采集和实验室分析采用统一流程和方法，确保不同样点和不同区域数据的可比性。在数据汇集与整理阶段，对数据完整性、异常值和单位一致性进行人工检查，确保数据结构统一、内容完整。数据集不包含模型推算结果，其质量代表原始观测与测定水平，适合作为基础数据用于后续模型构建和科学分析。", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "甘肃省高台县、甘肃省景泰县和青海省格尔木市", "ds_acq_lon_east": 104.36166666666666, "ds_acq_lat_south": 36.33027777777778, "ds_acq_lon_west": 93.80805555555555, "ds_acq_lat_north": 39.79416666666666, "ds_acq_alt_low": 1291.0, "ds_acq_alt_high": 2919.0, "ds_share_type": "apply-access", "ds_total_size": 2101571, "ds_files_count": 4, "ds_format": "excel", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "2a73010a-3e1a-43f5-a496-279c4afcee27.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c3f3820d-f5f9-43aa-ada3-00ebfbe290e6", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2026-01-23 10:16:54", "last_updated": "2026-01-23 10:16:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.MINQIN-SALIN-STN.DB7063.2026", "license": null, "i18n": { "en": { "title": "Surface Hyperspectral Observations and Soil Salinity Dataset in Typical Arid Regions of Northwest China", "ds_format": "excel", "ds_source": "
This dataset consists of original scientific data obtained from field observations and laboratory analyses. Hyperspectral data were acquired through in situ field measurements using portable field spectrometers, including the ASD FieldSpec HandHeld 2 and PSR-3500, to measure soil surface reflectance at sampling sites. Soil samples were collected simultaneously and transported to the laboratory, where soil electrical conductivity and soluble salt ion concentrations were determined following standardized soil physicochemical analysis procedures. The data acquisition process encompasses site layout, field hyperspectral measurements, soil sampling, and laboratory analyses, ensuring clear data provenance and full traceability.", "ds_quality": "
Data quality is primarily ensured by the performance of the observation instruments, the standardization of laboratory analyses, and the data organization process. Field hyperspectral measurements were conducted under stable weather conditions within a consistent observation time window, and random errors were reduced through white reference calibration and repeated measurements. Soil sampling and laboratory analyses followed uniform procedures and methods, ensuring the comparability of data across different sampling sites and regions. During data compilation and organization, manual checks were performed to verify data completeness, identify outliers, and ensure consistency of units, thereby maintaining a unified data structure and complete content. The dataset does not include model-derived results; its quality reflects the level of original observations and measurements, making it suitable as baseline data for subsequent model development and scientific analyses.", "ds_ref_way": "", "ds_abstract": "
This dataset focuses on soil salinization in typical arid regions of Northwest China and was developed based on field sampling and laboratory analysis. It systematically compiles soil salinity measurements and surface hyperspectral observations. The study areas include Gaotai County and Jingtai County in Gansu Province, and Golmud City in Qinghai Province, representing different natural geographical units and salinization mechanisms such as the Hexi Corridor, the Yellow River irrigation area, and the Qaidam Basin. The dataset includes spatial information (longitude, latitude, and elevation) of surface soil samples (0–10 cm), soil electrical conductivity, concentrations of eight major soluble ions (K⁺, Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, CO₃²⁻, and HCO₃⁻), and field-measured hyperspectral surface reflectance data covering the wavelength range of 350–1075 nm. The data are organized at the sampling-point scale and span the period from 2020 to 2022, with standardized variable naming and consistent units. \nCompared with similar datasets, this dataset features strong regional representativeness, high-precision field hyperspectral measurements, and comprehensive soil salinity information, providing reliable basic data for hyperspectral remote sensing inversion of soil salinity, regional comparative analysis, and ecological and environmental studies in arid regions.", "ds_time_res": "", "ds_acq_place": "Gaotai County in Gansu Province, Jingtai County in Gansu Province, and Golmud City in Qinghai Province", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Data processing mainly involved data organization and standardization and did not include model inversion or complex mathematical calculations. Field-acquired hyperspectral data were converted to surface reflectance through white reference calibration and were matched one-to-one with sampling point identifiers. Soil attribute data obtained from laboratory measurements were entered into electronic spreadsheets, and units and field names were standardized after manual verification. Hyperspectral bands were named using the format “R_wavelength (nm)”, with reflectance expressed as a dimensionless quantity; soil ion concentrations were expressed in mg·L⁻¹, and electrical conductivity was expressed in dS·m⁻¹. No interpolation, smoothing, or statistical estimation was applied, and the original observational characteristics of the data were preserved.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "土壤盐渍化", "土壤含盐量", "高光谱遥感", "地表反射率", "干旱区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国西北", "高台", "景泰", "格尔木" ], "ds_time_tags": [ 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "蒋小芳", "email": "1695090635@qq.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "薛娴", "email": "xianxue@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "尤全刚", "email": "youqg@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "尤全刚", "email": "youqg@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "其他" }