基于Sentinel-2A影像估算黄土高原光合/非光合植被盖度  

Sentinel-2A data-derived estimation of photosynthetic and non-photosynthetic vegetation cover over the loess plateau

作　　者：吕渡 刘宝元 何亮 张晓萍[1,3] 程卓 贺洁 LÜ Du;LIU Bao-yuan;HE Liang;ZHANG Xiao-ping;CHENG Zhuo;HE Jie(Institute of Soil and Water Conservation,Chinese Academy of Sciences and Ministry of Water Resources,Yangling 712100;University of Chinese Academy of Sciences,Beijing 100049;Institute of Soil and Water Conservation,Northwest A&F University,Yangling 712100;State Key Laboratory of Earth Surface Processes and Resource Ecology,School of Geography,Beijing Normal University,Beijing 100875)

机构地区：[1]中国科学院水利部水土保持研究所,陕西杨凌712100 [2]中国科学院大学,北京100049 [3]西北农林科技大学水土保持研究所,陕西杨凌712100 [4]北京师范大学地理科学学部,地表过程与资源生态国家重点实验室,北京100875

出　　处：《中国环境科学》2022年第9期4323-4332,共10页China Environmental Science

基　　金：国家自然科学基金资助项目(41877083)。

摘　　要：以黄土高原为例,基于Sentinel-2A影像和地表实测地物光谱与盖度数据,分别在模拟混合场景和野外实测混合场景中,评估4种NPV植被指数(NPVI):SWIR32(短波红外比值指数)、DFI(干枯燃料指数)、STI(土壤耕作指数)和NDTI(归一化差异耕作指数)估算非光合植被盖度(fNPV)的有效性,并利用优化法确定线性光谱混合模型的关键参数端元值,估算研究区光合植被盖度(f_(PV))和f_(NPV).结果表明,在模拟混合场景下,4种NPVI与模拟f_(NPV)线性关系的R2是0.365~0.750;在野外场景中,其相关性均有一定程度的降低,R2是0.147~0.211.研究构建NDVI-SWIR32像元三分模型,并确定了最优端元值:NDVIPV=0.80,SWIR32PV=0.60,NDVINPV=0.17,SWIR32NPV=0.77,NDVIBS=0.23,SWIR32_(BS)=0.99.模型对f_(PV)和f_(NPV)估算精度R2分别是0.817和0.463,NSE分别是0.806和0.458.利用该模型估算全区2019年4、8和12月的平均fPV和f_(NPV),分别为20.3%和59.2%,48.6%和33.1%,10.7%和59.0%.随时间推移,f_(PV)从东南向西北不断增加而后减小,f_(NPV)与之相反.NDVI-SWIR32模型可以用于Sentinel-2A影像数据来监测黄土高原地区fPV和fNPV的时空动态变化.In this study,we evaluated four non-photosynthetic vegetation indices(NPVI),including Shortwave Infrared Ratio(SWIR32),Dead Fuel Index(DFI),Soil Tillage Index(STI)and Normalized Difference Tillage Index(NDTI)for Non-photosynthetic Vegetation(fNPV)estimation in the simulated and field mixed scenarios,respectively,and applied them to estimate fNPV using Sentinel-2A data(10m)over the Loess Plateau.We applied a linear unmixing model to estimate Photosynthetic Vegetation(fPV)and fNPV based on the triangular relationship between Normalized Vegetation Difference Index(NDVI)and NPVI(e.g.,SWIR32).The NDVI-NPVI endmember values were determined.The results showed that the correlation coefficient(R2)between each NPVI and simulated fNPV was between 0.365 to 0.750,and 0.147 to 0.211 between each NPVI and fNPV under the field mixed scenario.Using this approach,we estimated the Loess Plateau’s average fPV and fNPV for April,August and December in 2019,being 20.3%and 59.2%,48.6%and 33.1%,and 10.7%and 59.0%,respectively.The R^(2) of the model for fPV and fNPV estimation reached 0.817 and 0.463,respectively,while the NSE was 0.806 and 0.458,respectively.The results also revealed the seasonal variation fPV from southeast to northwest over time,and the opposite trend for fNPV.Our study suggests that the NDVI-SWIR32 model can be used with Sentinel-2A data to adequately monitor the spatiotemporal dynamics of fPV and fNPV in the Loess Plateau.

关 键 词：Sentinel-2A 光合植被盖度 非光合植被盖度 线性光谱混合模型 黄土高原 

分 类 号：X171[环境科学与工程—环境科学] TP79[自动化与计算机技术—检测技术与自动化装置]