数据融合技术在提高NPP估算精度中的应用

摘要

The current remote sensing data can not simultaneously satisfy the precise monitoring of vegetation productivity changes in both high temporal and spatial resolutions. In this study, application of an image fusion method to an ecosystem model for improving the accuracy of NPP evaluations is proposed. Firstly, the Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)is applied to get higher temporal and spatial resolution NDVI data(30 m)from the MODIS-NDVI and TM-NDVI images and then multi-scale Net Primary Productivity(NPP)of Xilinhot grasslands are estimated based on the CASA model using different scales of MODIS-NDVI data and the 30 m fusion data. The results indicate that the corre-lation between the model-estimated NPP and the measured aboveground biomass is gradually increased with the improve-ment of the resolution of the input NDVI data. The max correlation coefficient(r)reached 0.915. Additionally, the coeffi-cient between the NPP estimations derived from fusion NDVI data and the observed biomass is higher than the coefficient of non-fusion image. The results also indicate that the accuracy of NPP estimations from fusion NDVI data is better than non-fusion NDVI data and the fusion NDVI image as the model input data can improve the accuracy of NPP estimations.%针对现有遥感数据不能同时满足在时间和空间上精确监测植被动态变化的问题，提出利用时空适应性反射率融合模型（STARFM）的方法对MODIS-NDVI和TM-NDVI影像数据进行融合处理获得30 m较高时空分辨率的融合NDVI影像，进而将多种尺度的MODIS-NDVI和融合NDVI数据分别输入到CASA模型，对锡林浩特地区进行植被净初级生产力（NPP）的多尺度估算。将不同尺度的NPP估算结果与地上生物量地面实测值进行验证比较，结果表明：随着输入NDVI空间分辨率的提高，NPP估算值与实测地上生物量之间的相关性也逐渐增大，r最大值达到了0.915。此外以融合NDVI影像作为输入数据之一的NPP估算值与实测地上生物量的相关性均比未融合NDVI的相关性高，说明融合NDVI估算NPP的效果较未融合NDVI好，并且以融合NDVI影像作为模型输入数据可提高NPP估算精度。