太赫兹科学与电子信息学报, 2020, 18 (1): 142, 网络出版: 2020-04-13   

基于Sentinel数据的滇池湖滨湿地地上生物量反演

Above-ground biomass estimation in Kunming Dianchi lake wetland using Sentinel imagery
作者单位
西南林业大学 林学院, 云南 昆明 650224
摘要
为了评估基于Sentine 1/2影像数据反演滇池湖滨带湿地森林地上生物量(AGB)的效果和能力, 以Sentinel-1 A/B(SAR)和Sentinel-2 A/B(多光谱)卫星图像为数据源, 获取SAR双极化后向散射系数、多光谱波段、植被指数和林冠生物物理变量等因子, 利用线性回归和机器学习算法, 建立了多个滇池湖滨湿地生物量反演模型。所有模型与滇池湖滨湿地样地地上生物量的相关性为0.619~0.84, 均方根误差(RMSE)范围为40.14~59.7 t/ha, 其中基于SAR的模型反演精确度最低; 在多光谱波段中, 红色和红边(波段4,5和7)与生物量有很好的相关性; 叶面积指数(LAI)模型是生物量反演的最佳变量组合(r=0.84,RMSE=40.14); 基于Sentine 1/2影像数据反演滇池湖滨带湿地地上生物量具有可行性。
Abstract
For evaluating the potential of Sentinel imagery for the inversion of Above-Ground Biomass(AGB) of Kunming Dianchi lake wetland, Sentinel SAR and multispectral imagery are used as data sources respectively, and various biomass prediction models are developed through the conventional linear regression and other machine learning algorithms. SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables are extracted. These models have 0.619-0.84 correlation agreement of observed and predicted values, and root mean square error of 40.14-59.7 t/ha. The SAR-based model has the lowest accuracy. Among the Sentinel-2 multispectral bands, the red and red edge bands(band 4,5 and 7), are the best variable set combination for biomass prediction. The model based on the biophysical variable―Leaf Area Index(LAI) derived from Sentinel-2 is more accurate in predicating the overall AGB. The study demonstrates encouraging results in biomass mapping of Dianchi lake wetland by using the freely accessible and relatively high-resolution Sentinel imagery.

张国飞, 岳彩荣, 章皖秋. 基于Sentinel数据的滇池湖滨湿地地上生物量反演[J]. 太赫兹科学与电子信息学报, 2020, 18(1): 142. ZHANG Guofei, YUE Cairong, ZHANG Wanqiu. Above-ground biomass estimation in Kunming Dianchi lake wetland using Sentinel imagery[J]. Journal of terahertz science and electronic information technology, 2020, 18(1): 142.

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