基于CO_(2)通量和遥感融合数据的亚高山草甸关键物候参数提取研究  

Research on the Extraction of Key Phenological Metrics of Subalpine Meadow based on CO_(2) Flux and Remote Sensing Fusion Data

作　　者：周昊强 包刚[1,2] 徐自为 Sainbuyan Bayarsaikhan 包玉海[1,2] ZHOU Haoqiang;BAO Gang;XÜZiwei;Bayarsaikhan Sainbuyan;BAO Yühai(College of Geographical Science,Inner Mongolia Normal University,Hohhot 010022,China;Inner Mongolia Key Laboratory of Remote Sensing&Geography Information System,Inner Mongolia Normal University,Hohhot 010022,China;State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing 100875,China;Institute of Geography and Geoecology,Mongolian Academy of Sciences,Ulaanbaatar 15170,Mongolia)

机构地区：[1]内蒙古师范大学地理科学学院,内蒙古呼和浩特010022 [2]内蒙古师范大学内蒙古自治区遥感与地理信息系统重点实验室,内蒙古呼和浩特010022 [3]北京师范大学地表过程与资源生态国家重点实验室,北京100875 [4]蒙古国科学院地理与地质生态研究所,蒙古国乌兰巴托15170

出　　处：《遥感技术与应用》2023年第3期624-639,共16页Remote Sensing Technology and Application

基　　金：内蒙古自治区自然科学基金项目(2021MS04014);国家自然科学基金项目(42171461)。

摘　　要：作为祁连山地区最广泛分布的植被类型,亚高山草甸在维持当地碳水通量和响应气候变化方面扮演者重要的角色。因此,准确探测其物候动态对于深入了解山地生态系统功能及其对气候系统的反馈至关重要。在祁连山东北部15 km×15 km的融合试验区内,结合地面涡度通量数据和多源卫星遥感影像,开展多源影像融合和陆表物候提取试验。采用增强型时空自适应反射率融合模型(ESTARFM)融合ETM+、OLI和VIIRS传感器的多源影像,重建2013~2020年双波段增强型植被指数(EVI_(2))、归一化植被指数(NDVI)和植被近红外反射率指数(NIRv)的高时间(最短1 d)和高空间(30 m)分辨率时序影像数据集。在此基础上,利用双重双曲正切函数(DHT)和全局模型函数(GMF)拟合通量塔GPP和各遥感植被指数影像的生长曲线,并应用动态阈值法提取生长季始期(SOS)、峰期(POS)和末期(EOS),以评估不同融合植被指数提取亚高山草甸关键物候参数的适用性。结果表明:ESTARFM融合影像能够准确反映真实影像的亮度和纹理特征,但输入影像的云污染像元也会对融合精度产生影响。在站点尺度(无云污染),NIRv和EVI_(2)表现出相似的融合精度;而在像元尺度(存在云污染,云量<20%),NIRv的融合精度明显高于EVI_(2),表明NIRv在算法上提高了植被—裸土混合像元中植被部分反射率的敏感性,在云污染条件下仍能保持较高的融合精度。对于生长曲线拟合算法,DHT+GMF能准确模拟通量塔GPP和各遥感植被指数的季节动态,决定系数高于0.960和均方根误差低于0.062。3种植被指数融合影像的物候提取精度比较表明,NIRv提取SOS和EOS的精度最高,而NDVI提取POS的精度最高,在站点(像元)尺度的偏差分别为4 d(3 d)、5 d(5 d)和4 d(6 d)。As the most widely distributed vegetation type in the Qilian Mountains region,subalpine meadows play an important role in maintaining local carbon and water fluxes and responding to climate change.There⁃fore,accurately detecting their phenological dynamics is crucial for a deeper understanding of mountain ecosys⁃tem functioning and its feedback to the climate system.In this study,we conducted a multisource image fusion and land surface phenology extraction experiment in a 15 km×15 km test area in the northeastern Qilian Moun⁃tains,combining ground-level eddy flux data and multisource satellite remote sensing images.We used an En⁃hanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM)to fuse multisource images from the ETM+,OLI,and VIIRS sensors,reconstructing a high temporal(minimum 1 day)and high spatial(30 m)resolution time-series image dataset of the 2-band Enhanced Vegetation Index(EVI_(2)),Normalized Dif⁃ference Vegetation Index(NDVI)and Near-Infrared Reflectance of Vegetation(NIRv)from 2013 to 2020.Based on this,we fitted the growth curves of the GPP flux tower and remote sensing vegetation index images using a Double Hyperbolic Tangent function(DHT)and Global Model Function(GMF),respectively,and applied a dynamic threshold method to extract the start(SOS),peak(POS)and end(EOS)of the growing season to evaluate the applicability of different fusion vegetation indices in extracting key phenological parame⁃ters of subalpine meadows.The results showed that ESTARFM fused images could accurately reflect the brightness and texture features of the real images,but cloud-contaminated pixels in the input images could also affect the fusion accuracy.At the site scale(without cloud pollution),NIRv and EVI_(2) exhibited similar fusion ac⁃curacy,while at the pixel scale(with cloud pollution,cloud cover<20%),the fusion accuracy of NIRv was significantly higher than that of EVI_(2),indicating that NIRv improved the sensitivity of vegetation partial reflec⁃tance in vegetation-bare soil mixed p

关 键 词：亚高山草甸 关键物候参数 涡度协方差 时空融合 

分 类 号：Q948[生物学—植物学] TP79[自动化与计算机技术—检测技术与自动化装置]