4种遥感数据时空融合模型生成高分辨率归一化植被指数的对比分析

  目的  针对时空融合方法在遥感植被状况调查及动态变化监测中的应用，比对时空自适应反射率融合模型(STARFM)、增强型时空自适应反射率融合模型(ESTARFM)、回归拟合空间滤波和残差补偿模型(Fit-FC)和规则集回归树融合模型(RPRTM)等4种时空融合模型对归一化植被指数(NDVI)的融合效果。  方法  以三江源地区2块具有差异性地表特征的区域为研究样地，采用上述4种时空融合方法，融合空间分辨率30 m的Landsat 8影像和250 m时间步长16 d的MODIS NDVI数据，生成步长为16 d的30 m空间分辨率的NDVI数据。基于Landsat NDVI影像通过定性的目视判别和定量的统计分析来评价不同融合模型结果的空间特征模拟效果，并以真实的MODIS NDVI时间动态为参考，分析了不同融合方法对地表植被动态特征的拟合效果。  结果  ①关于空间特征的捕捉，在地表覆盖状况较复杂的区域，RPRTM融合效果最佳(R2=0.82)；而对于输入影像差异较大的区域，ESTARFM融合效果最佳(R2=0.95)。②关于时间动态的捕捉，RPRTM针对不同的植被型均取得了最佳效果(R2为0.97~0.99)。③相对于模型输入数据的时空可比性，地表异质性对STARFM和ESTARFM融合效果的影响更大。  结论  4种时空融合模型能有效用于生成高时空分辨率的NDVI数据，不同模型其融合效果各有不同，RPRTM在复杂地表区域与模拟植被生长动态变化中均有较好表现。图4表1参38

Comparison of four fusion models for generating high spatio-temporal resolution NDVI

  Objective  In order to choose adapted fusion methods in vegetation survey and dynamic monitoring, we applied four different spatio-temporal fusion models including spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), regression model fitting, spatial filtering and residual compensation (Fit-FC) and the rule-based piecewise regression tree model (RPRTM).   Method  Based on the four spatio-temporal fusion models (STARFM, ESTARFM, Fit-FC and RPRTM), two sampling regions (region Ⅰ and Ⅱ), with different surfaces characteristics in the Three-River Headwaters Regions were taken to generate the high spatial information of the Landsat NDVI (30 m, 16 d). Based on Landsat NDVI image, the spatial characteristics of the fusion data of different fusion models were evaluated by qualitative visual discrimination and quantitative statistical analysis. Meanwhile, based on the MODIS NDVI time series, the fitting effect of different fusion methods on the dynamic characteristics of surface vegetation was analyzed.   Result  (1) RPRTM had the optimal spatial fusion performance in region Ⅰ (R2=0.82); and ESTARFM performed the best in region Ⅱ (R2=0.95). (2) RPRTM has achieved the best fusion for capturing temporal dynamics (R2=0.97?0.99), where the NDVI dynamics were highly consistent with the temporal variations of MODIS. (3) Compared with the spatio-temporal comparability of model input data, landscape heterogeneity had a greater impact on the fusion effect of STARFM and ESTARFM.   Conclusion  Spatio-temporal fusion models can be used effectively to generate NDVI data at high spatial and temporal resolution, with different models having different fusion effects. RPRTM performing well in both complex surface areas and simulated vegetation growth dynamics. Ch, 4 fig. 1 tab. 38 ref.]