Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution

High-precision and rapidly changing Net Primary Production (NPP) monitoring in regions depends on remote sensing data with high spatial and temporal resolution. However, high-quality remote sensing data with high spatial and temporal resolution are difficult to acquire with a single remote sensor. To solve the problem of missing data, we used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm in blending MODIS and Landsat data. We obtained high-frequency temporal information from the MODIS data and high-resolution spatial information from the Landsat8 OLI data to predict NPP with high spatial and temporal resolution. Using the STARFM model, we obtained the time series data with 30 m spatial resolution and 16 day temporal resolution. Then, the fused time series NDVI data combined with meteorological data were used as inputs to the Carnegie-Ames-Stanford approach model to estimate the NPP in the Chang-Zhu-Tan urban agglomerations. An area with 500×500 pixels was randomly selected from true Landsat images and fused Landsat images. The correlation coefficient and RMSE between the true Landsat8 NDVI and fused Landsat NDVI image were above 0.7 and 0.08, respectively. This result indicates that the true data and fused data maintained good consistency. The NPP estimated with the fused Landsat NDVI data showed better detailed spatial information than that obtained with the MODIS data. The fused NPP data showed distinct boundaries between water, road, and building, whereas the NPP simulated from the MODIS did not. The mean values of the NPP data obtained with the fused Landsat NDVI and MODIS NDVI data were 323.01 and 260.88 gC·m2·a-1, respectively. The mean value of the NPP from the fused Landsat NDVI data was higher than that of the NPP from the MODIS data due to the spectral difference between the MODIS and Landsat images. To further validate the fused NPP data, we employed the measured value of NPP. The correlation coefficients, RMSEs, and relative errors of the fused NPP and measured NPP were 0.89, 4.83 gC·m2·a-1, and 9.82%, respectively, which indicated that the fused NPP had a good consistency with the measured data. The fused NPP was consistent with the NPP obtained with the MODIS data in terms of pixels; the vegetation coverage accounted for more than 80% of the mixed pixels. Meanwhile, the fused NPP was significantly higher than the NPP estimated from the MODIS data in terms of pixels; the vegetation coverage accounted for less than 20% of the mixed pixels. At the same time, the fused NPP data retained the time features of the time series MODIS data. With the rapid development of regions, remote sensing data from a single date cannot meet the requirements of the monitoring of dynamic vegetation growth in urban areas. A spatial-temporal fusion technique is an effective way to blend images from different sensors for applications that require high resolutions in both time and space. This technique should be able to support time series remote data with high spatial and temporal resolution for NPP monitoring in regional areas. However, due to the rapid expansion of cities and unreasonable planning of regions, vegetation patches become increasingly fragmented with high spatial heterogeneity. Therefore, on the one hand, we should improve the spatial resolution of remote sensing data. On the other hand, we have to solve spatial heterogeneity, which is the focus of our follow-up research. © 2018, Science Press. All right reserved.