Assimilating multi-source remotely sensed data into a light use efficiency model for net primary productivity estimation
Assimilating multi-source remotely sensed data into a light use efficiency model for net primary productivity estimation
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将多源遥感数据同化为光利用效率模型以估算净初级生产力
DOI：
10.1016/j.jag.2018.05.013
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发表时间：
2018-10
影响因子：
7.5
通讯作者：
Wen Youyue
中科院分区：
文献类型：
--
作者：
Yan Yuchao;Liu Xiaoping;Ou Jinpei;Li Xia;Wen Youyue
为了将归一化植被指数（NDVI）等植被指数纳入卡耐基-艾梅斯-斯坦福方法（CASA）模型，用于估算净初级生产力（NPP），特别是在生长季节，需要高时空分辨率的卫星数据。然而，目前的遥感数据无法在高时空分辨率下准确监测植被变化。为了同时考虑时间和空间信息，开发了时空融合模型，以获得来自高时间分辨率数据（如MODIS）的时间信息以及来自高空间分辨率数据（如Landsat）的空间信息。本文首次利用时空融合模型生成了具有Landsat数据空间分辨率和MODIS数据时间分辨率的NDVI合成图像。其次，从合成的NDVI时间序列数据中提取物候特征，提高土地覆盖分类精度。最后，对将综合NDVI和土地覆被分类图融合到CASA模型中进行综合NPP估算的方法进行了评价。结果表明，综合NPP的估算精度优于非融合NDVI数据估算的NPP，提高土地覆被分类精度可以提高综合NPP的估算精度。月合成NPP与当月和前月的温度、降雨量和太阳辐射呈显著的指数关系。
High spatiotemporal resolution satellite data are necessary for the retrieval of vegetation indexes, such as Normalized Difference Vegetation Index (NDVI), to be assimilated into the Carnegie-Ames-Stanford Approach (CASA) model for net primary productivity (NPP) estimation, especially in the growing season. However, current remotely sensed data cannot accurately monitor vegetation changes at high spatiotemporal resolution. To consider both temporal and spatial information, spatiotemporal fusion models have been developed to obtain the temporal information from high temporal resolution data (e.g., MODIS) together with the spatial information from high spatial resolution data (e.g., Landsat). In this paper, synthetic NDVI images with the spatial resolution of Landsat data and the temporal resolution of MODIS data were first produced using spatiotemporal fusion models. Next, phenological features were extracted from synthetic NDVI time series data to improve land cover classification accuracy. Finally, we evaluated the approach of assimilating the synthetic NDVI and land cover classification map into the CASA model for synthetic NPP estimation. The results revealed that the accuracy of the synthetic NPP was better than NPP estimation from non-fusion NDVI data, and improving the land cover classification accuracy could improve the accuracy of the synthetic NPP estimation. Furthermore, the monthly synthetic NPP showed a significant exponential relationship with the temperature, rainfall, and solar radiation of the current and previous month.
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