关键词: NPP, 线性拟合, 时序拟合, 时空融合, 遥感数据融合

Abstract:

Multi-source remote sensing data fusion models can produce remotely sensed data products with both fine spatial resolution and frequent coverage from multi-source satellite data. It is able to provide strong support for the dynamic monitoring of vegetation with high precision. With the development of different fusion models, evaluating the characteristics and applicability of each model is of great importance for choosing the fittest fusion model in the practice of cropland productivity monitoring. In this paper, we chose Agriculture Comprehensive Exploitation Zone in Lingwu, Ningxia as a focal area. By using linear fitting, time series fusion, and spatial-temporal fusion model to blend the remote sensing data of the Landsat with spatial resolution of 30 m and MODIS with spatial resolution of 500 m, and the time step of 8 days, respectively. Finally, we made a comparison of ability to make a fine description of farmland productivity in spatial pattern, ability of conducting the dynamic monitoring of farmland productivity, and computing speed based on different multi-source remote sensing data fusion models. Results show that: (1) all of the three fusion models can clearly show the differences of NPP between threadiness bare objects such as roads, ridges and cropland. However, time series fusion and spatial-temporal fusion models are clearer than linear fitting model. The spatial-temporal fusion model shows more differences in evenness than time series fusion model for a relatively homogeneous cropland field. (2) Linear fitting model is suitable for estimating the annual variation of farmland productivity only, time series fusion model and spatial-temporal fusion model is suitable for the dynamic monitoring of farmland productivity. What's more, time series fusion model is suitable for monitoring farmland productivity at large or small-scale. (3) There are obvious vatiation in computing speed among the three fusion models. Computing speed of linear fitting model is the fastest, while spatial-temporal fusion model is the slowest. Among them, the computing speed of linear fitting model is 1.5 times faster than time series fusion model and 20 times faster than the spatial-temporal fusion model, respectively.

Key words: NPP, linear fitting, time series fusion, spatial-temporal fusion, remote sensing data fusion