NPP Simulation of Agricultural and Pastoral Areas Based on Landsat and MODIS Data Fusion
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Northern Tianshan of China is an important development base of agricultural and animal husbandry. The space-time information of net primary productivity (NPP) was accurately obtained based on remote sensing data, the grassland resources of agricultural and pastoral areas can be rationally allocated. It has important and actual significance to the development of northern Tianshan. Due to the influence of weather and the limitation of time and space resolution of satellite sensors, it is difficult to obtain remote sensing data with medium space resolution and high time resolution series. Based on the middle spatial resolution Landsat 8 OLI data and the high time resolution series MODIS data, the middle spatial resolution and high time resolution series remote sensing data were obtained by using STARFM algorithm for space-time fusion of remote sensing data. Then, the vegetation NPP in the middle section of the northern Tianshan was simulated through the CASA model. The results showed that the relationship coefficient r of the fused NDVI data and Landsat 8 OLI NDVI data was not more than 0.759, the Bias was between 0.006 2 and 0.009 4 and RMSE was between 0.074 and 0.135 in eight periods in 2016. There were good space detail information of the NPP simulated of the fusion data and CASA model. The R2 of NPP simulation value and field measurement value was 0.860 1. The research results can provide method for the collaborative simulation of NPP by multi-source remote sensing image fusion technology and light utilization model.
Agricultural and pastoral areas, CASA model, Date fusion, Landsat 8 OLI, MODIS, NPP
Xiaojun Yin, Honghui Zhu, Jerry Gao, Jun Gao, Lijie Guo, and Zhenzhen Gou. "NPP Simulation of Agricultural and Pastoral Areas Based on Landsat and MODIS Data Fusion" Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery (2020): 163-170. https://doi.org/10.6041/j.issn.1000-1298.2020.08.018